Kyndryl is aiming to boost sovereign capabilities through a new deal with Microsoft designed to help customers keep up with evolving data residency requirements.

Governments and highly regulated industries are increasingly facing geopolitical uncertainty, expanding data localization preferences, and ever more complex IT environments, according to Kyndryl.

With this in mind, the company argues that regulatory frameworks such as GDPR, DORA, and NIS2 need to be translated into practical architectures. The aim of this deal is to combine Kyndryl’s advisory, engineering, and operational experience with Microsoft’s sovereign cloud offerings to address these needs.

The collaboration combines Kyndryl Sovereignty Solutioning with Microsoft Sovereign Cloud capabilities, and brings to the table the full spectrum of Microsoft’s public cloud capabilities and private cloud solutions using Azure Local.

This, Kyndryl said, will help organizations address sovereignty across data and operational domains, helping to reduce regulatory framework complexity.

“Kyndryl understands the reality of sovereignty through our first-hand experience with government expectations in Europe, and our strategic alliance with Microsoft brings together complementary strengths to help customers operationalize sovereignty in a practical, scalable way,” said Giovanni Carraro, global strategic alliances leader at Kyndryl.

“By collaborating with Microsoft, we can help customers align their sovereignty goals with real-world architectures, thus balancing control, resilience and performance across hybrid and distributed environments.”

Kyndryl eyes sovereign ‘readiness’

Customers can use Kyndryl’s Sovereignty Readiness Assessment to evaluate their current posture across data, operational, and technical domains, identify any gaps and dependencies and develop a phased roadmap.

The firm said it aims to support implementation using sovereignty-ready architectures that incorporate Microsoft Sovereign Cloud capabilities.

This includes public cloud solutions using Microsoft Azure and Microsoft 365, along with sovereign private cloud solutions using Azure Local in connected and disconnected deployment models.

These are designed to support varying levels of data residency, operational independence, and jurisdictional control as needed.

This unified approach, Kyndryl noted, supports sensitive and regulated workloads, including AI-enabled use cases, with a focus on data governance and model locality.

"Kyndryl’s deep expertise in designing and operating complex, regulated environments complements Microsoft’s comprehensive sovereign cloud capabilities, including controls designed to support data residency requirements, access governance and regulatory compliance,” said Ihab Foudeh, EMEA enterprise partner solutions general manager at Microsoft.

“Together, we are helping organizations adopt cloud services in ways that respect their local requirements while still enabling modernization and innovation.”

Sovereignty momentum

The move by Kyndryl comes amid a concerted focus on digital sovereignty across Europe. Recent research from Gartner, for example, revealed that a third of countries will be locked in to region-specific AI platforms within the next two years.

This shift is driven by a mixture of regulatory pressure, geopolitics, cloud localization, national AI missions, corporate risks, and national security concerns.

In a separate survey of UK IT decision makers last year, almost two-thirds told OVHcloud they were happy to pay between 11% and 30% more for a sovereign technology product that would meet all of their regulatory and sovereignty needs.

More than half acknowledged data sovereignty as a crucial aspect of their data management strategy.

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Two-thirds (66%) of UK businesses could ditch US cloud providers due to rising concerns about digital sovereignty, according to a new study.

Research conducted by Civo found digital sovereignty is now viewed as a “strategic priority” by 73% of firms, marking a 12-point increase compared to last year and shaping decisions on what cloud services to use.

Reliance on a limited pool of foreign cloud providers is also an area of concern, according to Civo, with 64% highlighting this as a recurring talking point.

Notably, with businesses ramping up AI adoption, this adds another layer of complexity to sovereignty-related discussions.

More than half (58%) of respondents told the firm they’re concerned about their AI providers’ legal jurisdiction, while 43% said AI workloads must be hosted within the UK.

“AI has raised the stakes for digital sovereignty,” said Civo chief executive Mark Boost. “The issue is no longer just where data is stored, but also where systems are built, who controls the infrastructure and which legal jurisdiction it falls under.”

“The UK must control the infrastructure on which AI is built to ensure long-term competitiveness in the field. Sovereign cloud is about resilience, choice and control, not digital isolationism,” he added.

UK firms paying the ‘sovereignty tax’

While British companies are keen to reduce their reliance on US-based tech providers, the Civo study noted that many are unable to “break free” and are locked in a cycle of dependency.

Civo refers to this as a “sovereignty tax”, which creates “significant financial, operational, and strategic risks”.

“UK leaders clearly want to break free from Big Tech dependency, but find themselves trapped by an ever-tightening web,” Boost commented.

“This is not a proactive investment or deliberate strategy. It is a symptom of organizations becoming increasingly ensnared in the same hyperscaler ecosystems they acknowledge to be a significant long-term risk.”

Key hurdles to reducing dependence include technical lock-in, complexity of migration, contractual barriers, and various other financial implications of switching providers.

Civo noted that the number of companies that have successfully migrated to a domestic alternative has “stalled” at just 15% while only one-in-four UK companies believe they could ditch a US provider entirely.

Looking ahead, the company predicts that UK firms could end up becoming “more deeply entrenched” in US hyperscaler systems, with 28% having already found themselves in this predicament.

A ‘dangerous loss of autonomy’

Civo warned that remaining locked into foreign-owned infrastructure could result in a “dangerous loss of autonomy” for UK firms, and this isn’t just in terms of data privacy or security.

Other factors also play a role in the aforementioned sovereignty tax, such as unpredictable costs and events such as outages. The firm specifically highlighted the latter of these as a potential danger for UK businesses, with 39% of firms having experienced outages originating from US hyperscalers over the last year.

Enterprises across the country were impacted by a series of major cloud outages last year, most notably with Amazon Web Services (AWS) and Microsoft Azure. Similar outages at Cloudflare, meanwhile, also had a huge impact on operations for firms operating across a range of sectors.

These outages have a huge financial impact on UK enterprises, Civo noted. That tracks with research from Relic last year that warned many UK and Irish firms are “severely underestimating” the costs associated with outages.

Analysis from the firm revealed that “high-impact” outages carry a median cost of around $2 million per hour, with UK and Ireland-based organisations reporting losses of between $1 million and $3 million per hour.

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Wasabi has announced the launch of a new sustainability initiative designed to help partners and MSPs measure and address the carbon emissions associated with cloud storage.

Developed in partnership with sustainable finance marketplace Zero Circle, the Wasabi Impact Circle allows partners to track customers’ storage-related emissions and invest in verified climate projects by purchasing high-integrity carbon credits.

Available now through the Wasabi Account Control Manager, the storage vendor said the initiative represents a simple and transparent way for partners to take climate action amid increasing data volumes and AI-driven storage demands.

The launch builds on Wasabi’s existing partnership with Zero Circle, which previously introduced an invoice-based carbon footprint calculator that allows customers to upload their Wasabi invoice and view their emissions costs in real-time.

In an announcement, Wasabi vice president of product marketing Drew Schlussel said the company is looking to equip partners with additional tools to better support sustainability initiatives.

“The launch of the Wasabi Impact Circle reinforces Wasabi’s commitment to providing sustainable cloud storage solutions,” he commented.

“We are continuously looking for meaningful ways to support our partners and their customers’ sustainability goals, and the Impact Circle empowers organizations to turn their storage emissions into measurable, real-world climate impact.”

Addressing storage-related emissions

With Impact Circle, Wasabi said partners can select carbon credit portfolios that are aligned with their specific sustainability targets, climate priorities, and impact preferences.

Zero Circle manages the sourcing, evaluation, and allocation of the carbon credits used within each portfolio, helping users access curated climate projects without having to assess individual credits themselves.

Portfolios can be prioritized based on a range of sustainability preferences, including streamlined access to vetted carbon credits, projects selected for verified impact and transparency, as well as emerging carbon removal technologies.

“We are excited to expand our partnership with Wasabi, a company that aligns with our values of offering sustainable cloud solutions and addressing the growing demand for credible climate action,” said Zero Circle founder and CEO Hemanth Setty.

“Through the Wasabi Impact Circle, customers can transform emissions into investments in projects that drive measurable environmental progress.”

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Nationwide Building Society has announced an expansion of its partnership with Broadcom as the firm accelerates its digital transformation and hybrid cloud strategy.

As part of the move, the building society plans to adopt VMware Cloud Foundation (VCF) as the company works to build a new private cloud platform.

The VMware service allows enterprises to build custom private cloud setups, combining compute, storage, networking, and security capabilities.

Nationwide said the use of VCF will enable it to deliver more resilient and scalable digital services to customers while also streamlining integration of Virgin Money assets following a £2.9 billion takeover announced in April this year.

Paul Walsh, Director of Infrastructure and Service Delivery at Nationwide, said the partnership extension represents a “significant step forward” in the company’s technology strategy.

As we continue to evolve as a business, including integrating Virgin Money into the group, it is vital that we have a resilient, scalable, and secure technology foundation,” he commented.

“A private cloud built on VMware Cloud Foundation enables us to simplify operations, accelerate innovation, and deliver seamless digital experiences for our members, while maintaining the trust and stability that define the Nationwide brand.”

Nationwide eyes long-term AI gains

While VCF will enable Nationwide to establish a private cloud setup, long-term, Broadcom said it will act as a springboard for cloud native and AI applications.

As ITPro reported last year, Nationwide ranks among a host of UK banks ramping up the use of generative and agentic AI internally, with a particular focus placed on improving workforce efficiency and productivity.

In January, Nationwide announced a partnership with Moneyhub to roll out an AI-powered transaction analytics feature for customers across the UK.

Joe Baguley, Broadcom’s chief technology officer for EMEA, said Nationwide is pursuing a “deliberate and strategic approach” to private cloud that will underpin future innovation by “balancing agility and control”.

“By extending our partnership and adopting VMware Cloud Foundation as a consistent platform across the group, Nationwide will be able to integrate operations more efficiently, accelerate service delivery, and reduce operational complexity, while maintaining the security and governance expected of a leading UK financial services brand,” he said.

Hybrid cloud innovation

As part of its hybrid cloud strategy, Nationwide also maintains close ties with Amazon Web Services (AWS).

In January this year, the bank announced an extension of its partnership with the hyperscaler to deliver “more personalized experiences” for 17 million customers across the country.

Nationwide uses AWS’ cloud-based contact center platform, Amazon Connect, for customer support, giving teams access to a raft of AI-powered tools to streamline operations.

Speaking at the time, Suresh Viswanathan, group chief operating officer at Nationwide, said the expanded partnership aims to further consolidate workloads using AWS and modernize services.

"As we continue our digital transformation, we need cloud technology that can support our ambition to deliver better customer experiences, while keeping safety and security at the forefront,” Viswanathan said.

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Microsoft is facing a class-action lawsuit from shareholders in the US, accusing it of failing to come clean about the state of its Azure business.

The lawsuit, which names several senior Microsoft officials as defendants, including chief executive Satya Nadella and chief financial officer Amy Hood, has been filed by a Michigan pension fund, the City of St Clair Shores Police and Fire Retirement System.

It alleges that Microsoft failed to give investors a full picture of Azure's performance and the financial demands of its AI expansion between May 1, 2025 and January 28 this year – the date on which Microsoft issued its Q2 results.

It reported 39% revenue growth in its Azure and other cloud businesses, down from 40% in the previous quarter, and said it expected growth of between 37% and 38% growth in the first three months of this year. And following this, around $357 billion of the company's market value was wiped out in a single day, representing the company's biggest one-day decline in nearly six years.

Microsoft invested heavily in AI infrastructure, research, and products such as Copilot, the pension fund claimed, while overstating the strength of products such as Copilot and the benefits of its partnership with OpenAI.

According to the lawsuit, the company attributed both the slowing growth of Azure and the higher spending to capacity constraints as it diverted resources to AI-related research and development and Copilot.

It failed to disclose that the Copilot family of products had experienced significant brand positioning, user experience, usage, data siloing, computational capacity, organizational, and interoperability problems, the lawsuit claimed.

The company ranked well below its competitors on a number of benchmark tests, and needed to increase its capital expenditures by billions of dollars. It also needed to divert GPU and CPU capacity away from fulfilling demand for its profitable Azure services, in order to improve the competitive positioning of its Copilot family of products and increase its AI-related research and development, the pension fund said.

"As a result, Microsoft had failed to convert a significant percentage of its commercial Microsoft 365 users to paid Copilot subscriptions and Microsoft's Copilot offerings had lost market share to rival products, a trend that was increasing," said Rosen Law Firm, which is handling the case.

"When the true details entered the market, the lawsuit claims that investors suffered damages."

Microsoft told Reuters that the claims were without merit, adding, "Microsoft stands by the integrity of its public statements and will vigorously defend itself in court".

HPE has kicked off its Partner Growth Summit in Las Vegas by announcing it's moving more of its products to a channel-only route to market on the back of success with HMP Morpheus VM Essentials.

From 1 July, HPE SimpliVity PC1000, HPE Private Cloud PC3000, and HPE Zerto Software will all only be available from partners. This builds not he announcement last year that HPE Morpheus VM Essentials would only be available through channel partners.

Since taking that decision, the company has seen "tremendous impact and momentum across the partner ecosystem", according to Kristian Kerr, hybrid channel lead at HPE.

"We've got 700 more partners that have sold VME software in the last 12 months, and over 1300 partners have taken up the certifications since November when we launched them," Kerr told press and analysts.

Making SimpliVity PC1000, Private Cloud 3000, and Zerto software available exclusively through partners means "more of the private cloud and disaster recoverable disaster recovery portfolio as channel only," Kerr said, "so a huge step forward and further commitment to our partners and extending what we announced last year in the VM essentials."

HPE Morpheus VM Essentials channel update

HPE Morpheus VM Essentials isn't being left as simply the first step in this process. HPE Morpheus VM Essentials for Partner IT Program, or VM Essentials for Partner IT for brevity, allows partners to deploy VM Essentials into their IT environment free of charge for the first three years, with a "nominal" support fee.

According to Kerr, this means they will have the chance to not only "benefit from the significant virtualization savings" but also build their own competencies.

"We're going to be making this available to around 600 partners who will gain the private cloud with virtualization competency by the end of the year, so a huge step forward, just in terms of channel only," he added.

New support for cloud service providers

Another announcement coming out of HPE Partner Growth Summit is a new product aimed at cloud service providers.

HPE CloudOps Software for cloud service providers allows these businesses to build, operate, and monetize different kinds of private cloud service with the support of HPE Partner Ready Vantage, the company claimed.

"This HPE Cloud Permit model further enhances the value with preferential pricing for these service providers, and it's going to be tied to committed spend," Kerr said.

In addition to all of this, HPE has also announced it's wrapping up Juniper's partner program, Partner Advantage, into HPE Partner Ready Vantage. Partner Ready Vantage was announced in November 2025.

Juniper specialisations will be incorporated into HPE competencies, and memberships will be mapped from Partner Advantage to Partner Ready Vantage over the course of the year. For example, Elite Plus Juniper partners will become Platinum members in the networking centre of HPE Partner Ready Vantage.

Following HPE Partner Growth Summit, the main HPE Discover 2026 conference will kick off on Tuesday 16 June. You can follow all the latest announcements and analysis from the event here.

The recent history of cloud strategies has been massively impacted by data sovereignty, a move to collapse tech stacks to become more streamlined and far less complex, which also afforded higher levels of security. The cloud-first approach has become more complex.

As we explored in Parts 1 and 2 of this series, that thinking is rapidly evolving. Rising cloud costs, increasing governance challenges, data sovereignty concerns, and the explosive growth of AI are forcing enterprises to rethink how infrastructure should be designed.

Instead of asking whether workloads belong in the cloud, organizations are now asking a far more strategic question: where should each workload run to deliver the best balance of cost, resilience, control, and performance?

Increasingly, the answer is hybrid infrastructure.

This final article in the three-part series Post-Cloud Strategy: What Comes After Hyperscale? examines how enterprises are designing intentional hybrid and multi-cloud architectures that combine hyperscale platforms, private infrastructure, sovereign cloud environments, and edge computing into a single operational model. Rather than treating hybrid environments as temporary compromises, organizations are now architecting them as long-term foundations for the next generation of enterprise IT.

Hybrid is becoming a deliberate design choice

For many organizations, hybrid infrastructure emerged accidentally rather than strategically. Legacy systems remained on-premises while newer services moved into hyperscale environments, creating fragmented estates that evolved over time without a clear architectural vision.

Now, enterprises are trying to replace that organic complexity. Irin Rahman, CTO at Audiences, says hybrid-by-design strategies require organizations to fundamentally rethink how environments are built and operated.

“‘Hybrid by design’ really means being intentional about how you build and operate across owned and cloud environments, rather than ending up with a mix of systems that evolved organically over time,” Rahman explains. “One of the biggest challenges organizations face now is complexity, particularly as SaaS providers continue to expand their platforms and businesses end up with significant overlap in capabilities across multiple vendors.”

That complexity is becoming one of the defining infrastructure challenges of the post-cloud era. Enterprises are now operating across public cloud platforms, sovereign cloud providers, private infrastructure, and edge environments simultaneously. Without standardization and governance, operational overhead can quickly spiral out of control.

Joe Baguley, CTO, EMEA at Broadcom, tells ITPro that organizations are moving away from simplistic “cloud-first” strategies toward far more selective workload placement decisions.“Balancing workloads across environments starts with a shift from ‘cloud first’ to ‘right workload, right place’,” Baguley says. “Not every application has the same computing requirements, particularly in a world where sovereignty, performance, and cost pressures are increasing simultaneously.”

That philosophy reflects the wider trends discussed throughout the series. Part 1 examined how rising cloud costs and governance failures were forcing enterprises to reassess hyperscale economics, while Part 2 explored how sovereignty and geopolitical concerns are reshaping infrastructure decisions. Together, those pressures are pushing organizations toward more distributed infrastructure models where workloads are positioned according to operational need rather than infrastructure ideology.

Kate Obiidykhata, general manager, cloud-native at Percona, says hybrid-by-design architectures increasingly depend on open technologies that can operate consistently across multiple environments.

“Implementing a hybrid by design approach in practice involves looking at software, components, or services that can run on any platform, rather than being linked to a specific cloud provider or vendor,” she explains. “This normally means a significant role for open source software like databases and components, then using software containers and an orchestration layer like Kubernetes to deploy those elements.”

The objective is not necessarily to move away from hyperscalers altogether, but to avoid becoming overly dependent on any single environment.

Portability is becoming the foundation of modern infrastructure

One of the clearest lessons enterprises have learned from the first wave of cloud adoption is that portability matters far more than many originally anticipated. Early migrations often tightly coupled applications to proprietary cloud services because they offered speed and convenience. However, those decisions also created long-term operational dependency that many organizations are now struggling to unwind.

Baguley says avoiding vendor lock-in requires enterprises to prioritize open standards and operational consistency from the beginning. “If you want to avoid lock-in, you must stratify your architecture,” he says. “Write to open standards like Kubernetes and use declarative configurations like YAML files. That way, you decouple the application from the underlying infrastructure.”

Containers and Kubernetes have therefore become central to modern hybrid architectures. Kubernetes provides a standardized orchestration layer capable of running consistently across hyperscale environments, sovereign cloud platforms, and private infrastructure. In theory, that gives enterprises the flexibility to move workloads without needing to redesign applications for every platform.

However, Baguley emphasised that Kubernetes alone does not eliminate complexity. “Kubernetes is the modern lingua franca of application portability,” he explains. “But Kubernetes alone won’t save you from operational complexity. If every team is spinning up their own flavour of Kubernetes with different networking, security, and storage plugins, you’ve just moved your ‘best-of-breed’ mess up the stack.”

That warning highlights a growing realization across enterprise IT: portability is not simply about technology. It is also about governance and organizational culture. Leo Derikiants, CEO and co-founder of Mind Simulation Lab, says the key to reducing hybrid complexity requires businesses to “separate the infrastructure from the application itself,” he says. “You package everything the same way and use standard tools to run it. From a developer’s point of view, it shouldn’t matter where the code runs. On AWS or on your own servers.”

This platform engineering approach is becoming increasingly common across large enterprises. Instead of developers managing infrastructure directly, organizations are building internal platforms that abstract away operational complexity while maintaining centralized governance and security controls.

That consistency is becoming essential as infrastructure environments become more fragmented and geographically distributed.

Workload placement is now driven by economics and control

The days of automatically placing every workload into hyperscale cloud environments are fading quickly. Enterprises are becoming far more selective about where workloads should run based on cost, performance, resilience, latency, and regulatory requirements.

Rahman says, “One of the biggest hidden costs and risks in hybrid environments is unnecessary data movement. Businesses should work on bringing compute to the data, rather than continuously exporting and importing huge data files into new platforms. Every movement incurs an added cost, potentially exposes your business to additional tax and opens up the risk of data breach or leakage.”

That issue is becoming even more significant as enterprises deploy AI systems that generate enormous amounts of data. Large AI training models and inference workloads can create substantial operational costs if data constantly moves between clouds and centralized infrastructure environments. As a result, organizations are increasingly bringing AI processing closer to where data already resides.

Derikiants says “data gravity” is becoming one of the defining principles of infrastructure design.“Wherever your data lives, that’s where everything else will end up, because moving large amounts of data is extremely expensive,” he says. “So it usually makes sense to keep heavy data in your own storage and move compute around it when needed.”

That thinking is also driving renewed interest in private infrastructure for predictable workloads. Steady-state AI inference systems, databases, and large-scale compute environments can often become significantly cheaper to operate on enterprise-owned infrastructure over time compared with hyperscale cloud pricing models.

At the same time, hyperscalers still offer enormous advantages for burst capacity and rapid experimentation. Baguley says organizations need structured workload placement frameworks that align applications with the environments best suited to their operational requirements. “Some applications may benefit from hyperscale environments, while others require the control of private or sovereign cloud, especially where jurisdictional authority is a concern,” he explains.

That balanced approach increasingly defines the future enterprise stack. Rather than abandoning hyperscalers, organizations are integrating them into broader infrastructure ecosystems where workloads shift dynamically according to business need.

AI and edge computing will accelerate the hybrid future

The rise of AI and edge computing may ultimately become the biggest driver of hybrid infrastructure adoption over the next decade. Traditional centralized cloud models are not designed to process every workload efficiently, particularly when applications require low latency, localized processing, or continuous real-time decision-making.

Baguley says enterprise IT is moving toward a highly distributed operational model. “The future of enterprise IT is highly distributed, highly democratised, and incredibly flat,” he says. “The concept of a single, centralised ‘cloud’ is dying. We are moving toward a world of billions of endpoints on oil rigs, in retail stores, in cars, and in space via low-Earth-orbit satellites.”

Percona’s Obiidykhata says Kubernetes will become even more important as AI and edge workloads expand.“Kubernetes is the preferred implementation platform for AI workloads too,” she says. “According to Linux Foundation research, 66% of organizations use Kubernetes to host generative AI inference workloads.”

At the same time, automation is becoming essential for resilience. Enterprises increasingly want hybrid environments that automatically recover from failures and scale dynamically without constant human intervention.

Mind Simulation Lab’s Derikiants says automation and GitOps principles are becoming critical to achieving that level of operational resilience. “With a GitOps approach, your whole system is described upfront, and the infrastructure just follows it,” he explains. “If something breaks like your data center goes down, the system can bring everything back up in another location and shift traffic automatically.”

Ultimately, the post-cloud era is not about abandoning hyperscale infrastructure. It is about becoming more selective and far more intentional about how enterprise environments are designed.

As explored throughout this three-part series, the future enterprise stack will not belong entirely to hyperscalers, sovereign cloud providers, or private data centers alone. Instead, it will consist of carefully orchestrated hybrid ecosystems designed around flexibility, resilience, portability, and operational control.

All of the major cloud service providers have built vast global infrastructure networks that allow enterprises to centralise workloads and reduce infrastructure overheads. The assumption has always been simple: bigger cloud platforms meant greater efficiency and resilience, enabling businesses to innovate at speed. balance cost, control, and performance.

But as explored in Part 1 of this series, that model is beginning to change. Rising cloud costs and the growing demands of AI workloads are forcing enterprises to rethink where applications and data should live.

Now, other powerful forces are reshaping enterprise infrastructure strategy. Sovereignty, geopolitical instability, and increasingly strict regulatory requirements are driving organizations toward more distributed, regional, and policy-aware cloud environments.

This article, the second in the three-part series Post-Cloud Strategy: What Comes After Hyperscale?, considers how sovereignty and security concerns are accelerating the rise of regional cloud providers and sovereign cloud initiatives. Part three, Hybrid by Design: Architecting the Next Enterprise Stack, will explore how enterprises are designing resilient hybrid and multi-cloud architectures that

Sovereignty is becoming a board-level concern

Data sovereignty has evolved far beyond a compliance checkbox. Increasingly, it is shaping strategic infrastructure decisions at the highest levels of enterprise leadership.

Alexandra Thorer, chief growth officer at BCS Consultancy, says enterprises are moving away from the idea of cloud as a single, borderless platform. “Across the market, organizations are increasingly seen moving away from purely centralised cloud models and toward more regionally segmented architectures,” she says. “Rather than treating cloud as a uniform global layer, many enterprises now design around jurisdictional boundaries, particularly where regulatory scrutiny is highest.”

That shift is especially visible in Europe, where concerns around cross-border data transfers and digital autonomy are intensifying. Adam Low, chief technology officer at Wire, says enterprise cloud strategies are now being shaped as much by governance and risk as by technical performance.

“Cloud strategies have fundamentally shifted from a sole focus on cost and scalability to a greater emphasis on control and risk,” Low explains. “Organizations are paying closer attention to where their data resides, who can access it, and which legal frameworks apply. This is driving increased adoption of hybrid and multi-cloud models, with greater emphasis on jurisdiction, governance, and resilience.”

One of the biggest areas of confusion is the distinction between regional cloud infrastructure and genuinely sovereign cloud environments. Regional cloud offerings may guarantee that data remains inside a specific geography, but sovereign cloud models go much further, focusing on operational governance and jurisdictional independence.

“In practice, the distinction is often framed less around location and more around control,” Thorer says. “There is a growing expectation, particularly among regulated industries, that a sovereign cloud should limit exposure to foreign legal frameworks, restrict administrative access to locally governed personnel, and provide greater transparency into operational processes.”

Speaking to ITPro, Mark Boost, CEO at Civo, argues that many organizations are only now beginning to recognize the long-term risks of relying heavily on foreign-owned cloud infrastructure.“Sovereignty is about jurisdictional control, rather than where a data centre is based,” Boost explains. “Even if data is stored in the UK, if your data is held by a US provider, it can be accessed by US authorities under the US Cloud Act. This means it is never truly sovereign.”

For many enterprises, sovereignty concerns are no longer theoretical. They are directly influencing workload placement and long-term infrastructure planning.

Geopolitical instability is reshaping infrastructure decisions

The cloud industry was originally built around the idea of abstracting global infrastructure. Increasingly, however, geopolitical tensions are reintroducing geography into enterprise IT strategy. Organizations are becoming more cautious about concentrating workloads in a specific jurisdiction or relying too heavily on a small number of hyperscale providers.

BCS Consultancy’s Thorer says enterprises are paying far closer attention to geopolitical risk than they were even a few years ago.“There is evidence of increased attention to jurisdictional risk, including concerns around sanctions, data access laws, and political stability,” she explains. “In some cases, this is leading to diversification strategies, avoiding concentration of data or workloads in a single geography or under a single provider.”

Those concerns are accelerating demand for regional cloud providers and sovereign infrastructure initiatives designed to reduce dependency on foreign-controlled platforms. Boost believes geopolitical instability has fundamentally changed how organizations think about cloud dependency.

The growing importance of digital sovereignty is also influencing government policy. Across Europe, governments are increasingly encouraging local cloud ecosystems, sovereign AI initiatives, and national investments in digital infrastructure. Mike Hoy, CTO at Pulsant, says enterprises are already responding to the changing environment by reassessing their dependence on public cloud.

“Within the UK, compliance with sovereignty and residency regulations is becoming the dominant factor in cloud strategies,” Hoy says. “The most immediate and obvious impact is the continued flight from public cloud.”

At the same time, hyperscalers are adapting rather than retreating. Many are introducing localized governance controls and sovereign cloud partnerships in response to growing customer demand for greater transparency and jurisdictional control. The result is an increasingly layered cloud market where organizations mix hyperscale services with sovereign infrastructure, regional providers, private cloud, and edge environments depending on workload sensitivity and operational priorities.

Security is no longer defined by scale alone

For several years, hyperscalers positioned scale as one of their greatest security advantages. Their enormous investments in cybersecurity resilience engineering and threat intelligence created a perception that global cloud platforms inherently offered the safest environments.

That advantage still matters. However, sovereignty concerns are reshaping how enterprises think about security itself. Organizations are increasingly evaluating not just whether infrastructure is technically secure, but whether they retain sufficient visibility, governance, and legal control over their data and operations.

Wire’s Adam Low says sovereign cloud models are forcing enterprises to rethink the relationship between security and control. “Global providers benefit from scale and highly mature security capabilities,” he says. “Sovereign and regional approaches can offer comparable levels of security when properly designed and operated. The trade-offs concern who takes greater responsibility for the security architecture and implementation.”

That creates a difficult balancing act for enterprise IT leaders. Hyperscalers provide vast security ecosystems, but sovereign environments may offer stronger alignment with local governance and reduced exposure to foreign legal frameworks. As a result, many enterprises are now adopting blended approaches in which sensitive workloads remain within sovereign or private infrastructure while other services continue to run on public cloud platforms.

Thorer says the market increasingly views security as a set of trade-offs rather than an absolute choice. “Sovereign and regional solutions are often associated with greater control over data access and reduced exposure to external jurisdictions,” she explains.

“At the same time, some organizations express concerns about the relative maturity of security capabilities, particularly when compared with the scale of investment and threat intelligence available to global providers.”

AI is adding even more urgency to these discussions. AI inference and machine learning workloads are generating enormous amounts of sensitive data, often requiring localized processing and lower-latency infrastructure. Tim Pfaelzer, SVP and general manager, EMEA at Veeam, says sovereignty requirements are intensifying operational complexity inside already fragmented data environments.

“Sovereign cloud initiatives are emerging as a way to address this by giving organizations greater control over data residency, governance, and regulatory alignment,” he says.

“Yet, for many enterprises, these initiatives increase the risk of fragmentation and siloes. Data is already scattered across hybrid and multi-cloud environments, so sovereign requirements simply add another layer of complexity to an existing problem.”

That challenge is pushing enterprises toward more portable and workload-aware infrastructure strategies that deliver the secure cloud services businesses need to meet their expanding data challenges.

The future cloud market will be hybrid and policy-aware

The rise of the sovereign cloud does not signal the collapse of hyperscale computing. Instead, it reflects a broader evolution in how enterprises think about infrastructure. Businesses are becoming more selective and policy-driven about where workloads should run. Rather than defaulting automatically to public cloud environments, enterprises are distributing applications based on compliance, resilience, latency, performance, and geopolitical exposure.

Mark Pestridge, managing director at Telehouse Europe, says the market is entering a more mature phase of cloud adoption. “The biggest change is that sovereignty has moved from a legal consideration to an infrastructure design decision,” Pestridge says. “Workloads are being placed more deliberately, with sensitive data often kept in-country while less sensitive services remain distributed across larger cloud platforms.”

That philosophy increasingly defines the emerging post-cloud era.

Pulsant’s CTO Mike Hoy believes the future will be driven not by abandoning hyperscalers, but by building more intelligent hybrid architectures. “Over the next three to five years, the UK will head into a multi-cloud, sovereignty-led era rather than a wholesale shift away from hyperscalers,” Hoy says. “It will be driven by decisions about where to place sensitive workloads, compliance, resilience, and operational control.”

Ultimately, enterprises are recognizing that no single infrastructure model can satisfy every requirement simultaneously. The future enterprise stack will likely combine hyperscale cloud platforms, sovereign cloud environments, edge computing, private infrastructure, and regional providers in carefully orchestrated hybrid architectures.

As explored in Part 1 of this series, the cloud-first era is evolving into something far more nuanced. The next phase of enterprise infrastructure strategy will not be defined purely by scale, but by flexibility, resilience, governance, and control.

Part 3 of Post-Cloud Strategy: What Comes After Hyperscale? explores how IT leaders are designing hybrid-by-default architectures that balance cost, performance, operational resilience, and sovereignty across increasingly distributed environments.

The Pentagon has handed a $9.7 billion deal to Dell to provide – and sort out – Microsoft licenses.

The five-year deal – known as the Microsoft Department of War Enterprise Software Agreement II Core Enterprise Technology Agreement (CETA) – was awarded to Dell Federal Systems, and will see Dell as the main contractor providing Microsoft 365, cloud subscriptions, and software licensing for Microsoft products, which have long been in use by the defense agency and military.

"By providing enterprise access to Microsoft 365 advanced cloud subscriptions and critical on-premises licensing, this CETA acts as part of the digital connective tissue essential for Combined Joint All-Domain Command and Control, CJADC2," said Defense Department Chief Information Officer Kirsten Davies at a press briefing, according to reports.

She added: "This ensures our war fighters have the tools for just-in-time data sharing, supports our pivot to AI and data analytics, and undergirds uninterrupted operational continuity for our most sensitive and disconnected environments."

The aim is that the deal will give the Pentagon one supplier for Microsoft products, rather than multiple smaller contracts, helping to reduce redundancies in licensing. The agency believes that bringing together smaller IT budgets from across departments into one deal could save as much as $422 million each year.

"This second-generation blanket purchase agreement will streamline and consolidate critical Microsoft software and services across the Department of War, the intelligence community, and the US Coast Guard," said Defense Department Chief Information Officer Kirsten Davies at a press briefing, according to CNBC.

CETA will cover US intelligence agencies, the Coast Guard, and the military, including the Air Force, Army, Navy, Marines, and Space Force.

Previous deals

The CETA deal is the successor to the $1.76bn, five year Micrsoft Enterprise Services contract awarded in 2019, which was designed to give the intelligence community and defense agencies access to Microsoft technology and support as part of a pivot to the cloud.

That same year, the Pentagon awarded Microsoft a massive $10bn cloud deal known as JEDI, but two years later, it was scrapped following challenges by AWS that included accusations that then-President Trump had told the Department of Defense to "screw Amazon".

In 2022, Microsoft and AWS were both tapped by the Pentagon for a new joint cloud project worth $9bn.

Friendly relations

This time around, government representatives stressed the deal followed a competitive procurement process – but reports have been quick to point to Michael Dell's $6.25 billion pledge to fund so-called Trump accounts for children, as well as his wider support for Trump.

In return, Trump earlier this month said people should "go out and buy a Dell" – helping send the company's stocks soaring.

The officials announcing the deal said Dell beat out rivals in the procurement process. "The vendors were all evaluated based on competition, comparison to General Services Administration GSA schedule pricing, and overall chain of value to the department," said acting Navy Chief Information Officer Barry Tanner in a press briefing, according to reports. "Going through the process of evaluation, they came out on top."

Tata Consultancy Services (TCS) has expanded its global cloud portfolio with the launch of SovereignSecure Cloud in Europe, designed specifically for governments, public sector enterprises, and regulated industries.

EU organizations face a broad range of regulations, including the General Data Protection Regulation (GDPR), the Digital Operational Resilience Act (DORA), the NIS2 Directive, and the Cyber Resilience Act (CRA).

And promising sovereignty across data, operations, and digital infrastructure in the EU, TCS said its new offerings will enable enterprises to strengthen regulatory compliance, achieve digital autonomy, and enhance security, without compromising on speed, agility, and interoperability.

SovereignSecure Cloud for the EU, said TCS, is based on a multi-layered approach. It comprises a sovereign cloud layer delivered through hyperscalers, providing scale and the flexibility to operate securely within the EU regulatory framework.

The national sovereign cloud layer enables country-specific localization while bringing operations under a unified control plane. And its enterprise cloud services layer, meanwhile, leverages the EU-specific TCS Enterprise Cloud Framework, a unified orchestration and control layer designed to enable enterprises to dynamically apply the appropriate level of sovereignty across data, operations, and technology based on workload, risk, and sector.

"European organizations are looking to strike a balance between addressing supply chain and sovereignty risks while ensuring leverage of frontier technologies to be globally competitive," said Sapthagiri Chapalapalli, head of Europe at TCS.

"TCS SovereignSecure Cloud solutions mark an important milestone for TCS in Europe, as our customers can now benefit from a pragmatic approach to cloud that ensures resilience and sovereignty that is contextualized to the enterprise."

Alongside the launch, TCS is also introducing the TCS Sovereignty Consulting and Delivery Framework in the EU, with the aim of helping organizations become a 'minimum viable sovereign enterprise', with the right balance between control and flexibility.

Using a risk-based approach, said TCS, it categorizes workloads by importance and applies the right level of sovereignty to each, focusing effort on the areas with the greatest impact to mitigate risk.

The launch of TCS SovereignSecure Cloud in the EU follows its rollout in India last year, followed by expansions into Kenya, East Africa, and the Philippines.

And, said TCS, it builds on the firm's strength in the European region, where it has been operating for more than 45 years, with an existing workforce working from 58 offices across the region, with 10 data centres and 21 delivery locations across Europe.

"Harnessing sovereign AI unlocks sovereign cloud adoption advantages. Sovereign AI, which is built and operated within national boundaries, enables enhanced operational efficiency and supports local regulations compliance," said Pradeep Devalla, head of sales and solutions, cloud unit – growth markets.

"Sovereign AI and sovereign cloud collaboration facilitate the creation of advanced, culturally attuned digital services that protect sensitive information and reinforce digital sovereignty."

For more than a decade, the enterprise technology conversation revolved around a single assumption: move to the cloud. Amazon Web Services, Microsoft Azure, and Google Cloud promised scalability, agility, and reduced infrastructure complexity.

Organizations embraced cloud-first strategies at speed, accelerated further by remote work demands, digital transformation initiatives, and the rise of AI-powered services.

Now, however, the mood is shifting.

Enterprises are not abandoning hyperscale cloud platforms, but they are becoming more selective about how and where they use them. Research from Gartner, suggests that by 2027, 90% of organizations will adopt hybrid cloud infrastructure. Cloud repatriation is growing in popularity.

Escalating operational costs, mounting concerns around digital sovereignty, and growing complexity in cloud environments are forcing IT leaders to ask a more nuanced question: what workloads actually belong in the cloud?

This article is the first in a three-part series examining the rise of post-cloud strategies. In part two, we'll explore the geopolitical and regulatory forces driving sovereign cloud adoption, while part three will look at how enterprises are designing hybrid architectures that balance cost, resilience, and control.

The hidden economics of public cloud

Cloud computing was originally sold as an economically more efficient model. Rather than investing heavily in on-premises infrastructure, businesses could pay only for what they used. That flexibility remains valuable, especially for rapidly scaling services or unpredictable workloads. The problem is that hyperscale pricing models have evolved into highly complex ecosystems that can be difficult to predict and manage.

Jay Litkey, SVP of cloud and FinOps at Flexera, says enterprises are increasingly being caught out not by headline compute costs or rising storage fees, but by secondary charges that quietly accumulate over time.

“Hyperscaler pricing has become much more granular over the last few years,” he explains. “The surprises are usually not the obvious things, such as compute or storage. It is often the layers underneath that catch people out, such as data transfer between regions, storage sitting in the wrong tier, duplicate environments, or commitment-based discounts that looked smart when they were signed but are no longer aligned to actual usage.”

Those hidden costs are becoming especially problematic for organizations running data-intensive workloads. Analytics platforms and AI systems generate enormous amounts of data movement, which can trigger substantial egress and replication charges.

Indeed, AI adoption can complicate costs massively. AI inference workloads and large-scale model training are driving fresh waves of unpredictable spending. James Brooks, lead for hybrid solutions at Hewlett Packard Enterprise, emphasizes that AI costs are now emerging as a major contributor to cloud bill escalation.

“The most common source of ‘cloud bill shock’ is rarely the primary service,” Brooks says. “Secondary and consumption-based elements such as data egress charges, long-term storage costs, AI inference and token usage, and over-provisioned or idle resources, are where costs quietly compound.”

For many enterprises, the result is a growing mismatch between expected cloud savings and operational reality.

Governance failures are amplifying cloud sprawl

While hyperscale economics are under scrutiny, many experts argue the biggest issue is not the cloud itself but how organizations govern it.

The ease of provisioning resources has created a culture of rapid consumption. Litkey says the core problem is that cloud adoption often outpaces the maturity of governance. “Cloud makes provisioning incredibly easy,” he explains. “Teams can spin things up in seconds and, without the right controls in place, that flexibility can create sprawl very quickly.”

This challenge is particularly acute for enterprises that migrated workloads quickly during earlier cloud transformation initiatives. In many cases, organizations simply lifted and shifted legacy infrastructure into hyperscale environments without redesigning architectures for cloud-native efficiency.

Justin Sharrocks, general manager, EMEA at Trusted Tech, says governance immaturity remains one of the biggest contributors to overspending. “Weak workload design, unclear ownership, and low FinOps maturity tend to drive inefficiency,” Sharrocks says. “Cloud pricing hasn’t necessarily increased; it’s become easier to mismanage.”

The challenge for IT leaders is that cloud costs are often distributed across multiple departments and teams. Engineering prioritizes speed and innovation, finance focuses on predictability, and operations teams concentrate on performance and resilience. Without shared accountability, visibility becomes fragmented.

James Peet, Practice, director for cloud and digital transformation at Ensono says organizations are increasingly integrating financial controls directly into architecture decisions. “FinOps needs to shift from looking backwards at what’s already been spent to shaping decisions before that spend happens,” he explains. “The organisations getting this right are making cost part of how systems are designed and built.”

Sovereignty concerns are changing workload placement decisions

Cost pressures alone are not driving post-cloud strategies. Regulatory scrutiny and current geopolitical uncertainty are increasingly influencing where organizations place workloads.

What was once considered a compliance issue has become a board-level strategic concern. Peet says organizations are paying much closer attention to who controls their data and which legal frameworks apply to it. “Data sovereignty has moved into day-to-day decision-making for leadership teams,” he says. “Organizations are looking more closely at who can access their data, and which legal frameworks ultimately apply.”

What was once considered a compliance issue has become a board-level strategic concern. Peet says organizations are paying much closer attention to who controls their data and which legal frameworks apply to it. “Data sovereignty has moved into day-to-day decision-making for leadership teams,” he says. “Organizations are looking more closely at who can access their data, and which legal frameworks ultimately apply.”

This shift is particularly visible in regulated industries such as financial services, healthcare, government, and critical infrastructure. Organizations operating in these sectors increasingly want guarantees around data residency, operational resilience, and jurisdictional control.

Mark Duff, VP international regions at Mitel, says concerns around sovereignty are now influencing broader infrastructure strategy. “Security, compliance and digital sovereignty are becoming deciding factors in technology decisions,” Duff says. “Data sovereignty, once a regulatory concern, is now becoming a strategic, board-level priority.”

These concerns are accelerating interest in sovereign cloud environments, regional cloud providers, and localized infrastructure models. Some enterprises are also reconsidering private infrastructure for workloads involving highly sensitive data.

James Brooks says geopolitical instability and regulatory complexity are fundamentally reshaping infrastructure decisions. “Data sovereignty and geopolitical risk have moved from secondary concerns to primary drivers of infrastructure strategy,” he explains. “Organizations are reassessing workload placement in response to regional instability, increased geopolitical tension, and growing operational resilience requirements.”

Importantly, most organizations are not moving entirely away from hyperscalers. Instead, they are becoming more selective about where workloads reside and how data is processed.

Hybrid infrastructure is becoming the default enterprise model

As enterprises reassess their dependence on hyperscale, hybrid and multi-cloud strategies are emerging as the dominant operational model.

Rather than centralizing everything in public cloud environments, organizations are increasingly distributing workloads across hyperscalers, private infrastructure, sovereign cloud platforms, and edge environments based on cost, latency, compliance, and performance requirements.

This transition reflects a broader recognition that not all workloads behave the same way.

Krystal Mattich, VP infrastructure and trust at Brain Corp, says the rise of physical AI is accelerating the need for distributed infrastructure. “The cloud-first era was built for digital workflows,” Mattich tells ITPro. “But physical AI introduces a different set of constraints: continuous sensor data, real-time decision-making, unreliable connectivity, and strict requirements around latency, safety, and data control.”

Edge computing is becoming increasingly important in industries that require processing large volumes of real-time data locally. Manufacturing, logistics, healthcare, and autonomous systems increasingly require infrastructure capable of operating independently from centralized hyperscale platforms.

Mattich emphasizes that enterprises are beginning to rethink the role of the cloud itself. “The cloud remains unmatched for scale, but the next phase of infrastructure involves using it more deliberately, not by default,” she says. “The key question shifts from ‘Can we centralize this?’ to ‘Where should this intelligence live to be most effective?’”

That philosophy increasingly defines the emerging post-cloud era. The future enterprise stack is unlikely to belong entirely to hyperscalers, private data centers, or sovereign cloud providers alone. Instead, it will consist of carefully orchestrated hybrid environments where workloads move dynamically based on operational requirements and business value.

Litkey says the era of default cloud adoption is already fading.“I do not think ‘post-cloud’ means moving away from cloud,” he says. “I think it means moving away from the default cloud.”

That shift may ultimately define the next decade of enterprise infrastructure strategy.

That shift may ultimately define the next decade of enterprise infrastructure strategy.

This article is Part 1 of the series Post-Cloud Strategy: What Comes After Hyperscale? In Part 2, we examine how sovereignty, security, and geopolitical pressure are accelerating the rise of regional and sovereign cloud environments.

TD Synnex has announced the availability of its StreamOne White Label Storefronts for partners across the UK and Ireland.

The distributor said the move will help the channel scale cloud services and improve the customer experience.

Built on the company's StreamOne Ion aggregation platform, the white-label storefronts will enable partners to create their own branded cloud marketplaces through which customers can directly purchase, provision, and manage cloud services.

The self-service offering is designed to give partners flexibility over how they deliver cloud services while maintaining full control over elements such as branding, pricing, and customer relationships.

Channel partners can configure storefronts with their own URLs and determine exactly which solutions and services their customers have access to, alongside controls for subscription scaling. Multiple storefronts can also be created and tailored to different customer groups, with access protected through multi-factor authentication.

In an announcement, TD Synnex said the platform will enable partners to trigger cross-sell and upsell suggestions within their storefronts at key points of the lifecycle to boost average order values and help drive further revenue opportunities.

The firm added that storefronts can be tailored to partner requirements with support from its cloud team, while training via the TD Synnex Channel Academy and customer-facing marketing materials are also available.

Driving recurring cloud revenue growth

TD Synnex's UK&I storefronts launch comes as channel partners across the region increasingly look to strengthen and scale their recurring revenue models, while also improving the digital purchasing experience for customers.

Commenting on the move, Simon Bennett, TD Synnex's managing director of advanced solutions for the UK and Ireland, said the new storefronts are designed to help partners meet customer expectations as they continue to evolve.

"With StreamOne White Label Storefronts partners can give today's digitally-led customers the convenience, speed, and self-service control they expect," he explained.

"Backed by the power of our industry-leading StreamOne cloud platform, they can scale-up quickly and build stronger and more profitable business, differentiate in a crowded market, and take their cloud services to the next stage of evolution."

Red Hat is expanding its sovereign and private cloud capabilities in a move it says will give organizations greater control over tech stacks and data.

The expansion of sovereignty features will offer users five new capabilities for those operating under Red Hat Confirmed Stateside Support and Red Hat Confirmed Sovereign Support for the EU, the company said.

Chief among these is an expanded compliance framework to automate audit preparation, with new Compliance Profiles for the Red Hat OpenShift Compliance Operator.

These, combined with Red Hat Advanced Cluster Security for Kubernetes, allow organizations to automate technical reviews.

The idea is to make it easier to generate appropriate evidence for regional and industry regulations such as NIS2, GDPR, and DORA, and to keep up with new regulations as they evolve.

Elsewhere, Red Hat is also launching a new cross-platform installer, delivering automated, pre-configured isolated computing platforms across Red Hat Enterprise Linux, Red Hat OpenShift, and Red Hat Ansible Automation Platform.

"By enforcing operational guardrails at launch, these landing zones turn reference architectures into deployable infrastructure," said the firm.

"This approach reduces time-to-value for hardened and compliant workloads without requiring manual configuration of baseline controls."

New Red Hat OpenShift, Lightspeed features

Announced at the company’s flagship summit in Atlanta this week, enterprises will also gain access to a new service provisioning interface. The company said this aims to help partners and customers deploy virtual machines (VMs), clusters, and AI service on OpenShift more efficiently.

These tools can then be used to provide GPU as a Service, Models as a Service, and Inferencing as a Service as components within private clouds, helping to maintain control of the AI model lifecycle.

Notably, on-premises telemetry for data sovereignty is a key focus for the company, with Red Hat Lightspeed now providing cost management tools for OpenShift that remain entirely within customer-controlled environments.

"This capability gives organizations comprehensive visibility into cloud spend while maintaining data residency, helping to eliminate the need to transmit operational data across sovereign boundaries," the company said in an announcement.

Software supply chain gains

Red Hat says it plans to localize the software supply chain to help mitigate risks associated with regional disruptions.

Efforts on this front will start in the EU initially, with in-region content delivery allowing customers and partners to download Red Hat Enterprise Linux locally.

The company plans to expand this regional network to additional products by the end of 2026.

"Innovation should not be a trade-off for control. Whether an organization is meeting jurisdictional mandates or reclaiming its data from proprietary silos, we are providing the capabilities and platforms to build a more self-determined future," said Red Hat senior vice president and chief product officer Ashesh Badani.

"Red Hat is focused on helping the organizations that use these technologies to drive the next decade of AI and cloud innovation on their own terms.”

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Westcon-Comstor has announced an expansion of its cloud marketplace capabilities through participation in Microsoft’s resale enable offers (REO) program.

Designed for channel-led selling, the initiative combines hyperscaler reach with value-added services to help support scalable marketplace growth.

Software vendors can authorize approved channel partners to create and manage private offers on their behalf via Microsoft Marketplace, with the Redmond giant handling customer billing and payment collection.

Westcon-Comstor will also provide a range of value-added services designed to help partners build scalable marketplace practices – including partner enablement, incremental revenue tools, as well as support across renewals and the full customer lifecycle.

By combining Microsoft Marketplace with its own technical expertise, the distributor said partners can unlock cloud commit budgets, accelerate deal cycles, and expand customer spend while maintaining control over the relationship.

“Hyperscaler marketplaces are redefining how enterprise software is bought and sold, but success for channel partners and vendors depends on turning activity into repeatable business,” explained Peter Woest, Westcon-Comstor’s cloud marketplace partnership director.

“By wrapping Microsoft Marketplace with our value-added services and technical marketplace expertise, we’re making simplicity and scale a reality and allowing partners to quickly establish, build and grow their Microsoft Marketplace business.”

The reseller's participation comes amid significant cloud marketplace growth. According to research from Omdia, cloud marketplace software sales are expected to grow from $30 billion in 2024 to $163 billion in 2030, with partners tipped to be the driving force behind most marketplace spend by next year.

Westcon-Comstor said the REO program is already gaining traction, with the firm supporting partners in onboarding to marketplace-driven opportunities with vendors such as Palo Alto Networks and Infoblox.

The distributor also revealed it is in discussions with “several other vendors” regarding future collaboration opportunities with Microsoft REO.

Commenting on the move, Microsoft’s marketplace channel lead, Darren Sharpe, said the priority is to help partners build an effective, platform-first marketplace practice, backed by a strong ecosystem support.

“Westcon‑Comstor is helping partners drive momentum through REO, enabling vendors to extend their reach while keeping the channel at the centre of the customer relationship,” he added.

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Frustrated with “sovereign cloud" offerings that it says are nothing of the sort, CISPE has launched a new framework aimed at helping enterprises check if they’re getting exactly what they’re paying for.

CISPE’s Sovereign and Resilient Cloud Services Framework has been developed, defined, and tested by European cloud infrastructure providers, the trade body said in a statement.

The new scheme will allow businesses to easily select cloud services that have been audited to verify their sovereign and resilience credentials.

The aim is to provide a clear, certifiable definition of control in cloud services, helping customers and public authorities identify offerings that give them effective control over data, infrastructure, workloads, and operations.

“Think of the CISPE Sovereign Badge as ‘puncture-proof tyres’ on a car: it guarantees immunity from external interference with your cloud services or data," said Francisco Mingorance, secretary general of CISPE.

"By contrast, the CISPE Resilient Cloud Service Badge represents ‘run-flat tyres’ — you may encounter disruption, but you can continue your journey without losing control.”

The framework has two strands. First and foremost, sovereign services must ensure control by design – meaning they're owned, governed, and operated within the relevant jurisdiction and prevent foreign powers from accessing, interfering with, or disrupting them.

Meanwhile, resilient services ensure control by capability, CISPE said. Even where some non-sovereign elements exist, customers retain control through robust technical and operational safeguards.

These include customer-managed encryption, portability, independent backups, and the ability to switch providers or redeploy workloads.

CISPE said that more than 40 services have already been declared against the CISPE framework, including sovereign and resilient European AI assistant services, public cloud, Kubernetes, and storage offerings. More compliant services are expected in the coming weeks, it said.

Tackling “sovereignty washing”

The trade group has long been concerned about “sovereignty washing”, arguing last year that the EU Cloud Sovereignty Framework did little to reduce reliance on American cloud providers and hyperscalers.

"Rather than bringing clarity, the Framework muddies the waters by introducing a murky 'sovereignty score' that averages the impossible with the irrelevant," it said.

"In practice, most European cloud service providers are likely to score lower than foreign hyperscalers under this system – perhaps that’s the idea – preserving the status quo under a cloak of “sovereignty."

Last month, the group wrote to Henna Virkkunen, the EU’s EVP for Tech Sovereignty, expressing concerns about the upcoming Cloud and AI Development Act (CAIDA).

The letter called for a number of actions aimed at shoring up sovereignty capabilities across the region, including:

• Sovereignty by control, focusing on ownership, governance, and legal protection.
• Stronger resilience where sovereign services are not available.
• Reserved procurement shares for EU-based cloud providers.
• A policy reinforcing competition and interoperability.
• Taxpayer-funded investment in European companies.

"This first comprehensive European cloud policy should strengthen Europe’s digital capacity by prioritizing procurement and investment in sovereign European solutions that foster a competitive cloud ecosystem," the trade group said.

"If it instead enables 'sovereignty washing' or procurement strategies that further entrench the dominance of non-European hyperscalers, it risks undermining the very objectives it seeks to achieve."

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Good morning from Las Vegas, where Google Cloud is kicking off its annual event. ITPro's Rory Bathgate is live on the ground, and ready to cover all the announcements and customer stories from the opening keynote. Titled 'The agentic cloud', expect to hear from CEO Thomas Kurian and other executives as they explain how Google Cloud is preparing itself for the coming wave of AI agents.

Google Cloud already made a number of announcements this morning, which we've covered here:

• Google Cloud announces eighth-generation TPUs, boasting AI training and inference leaps
• Google expands Gemini Enterprise, consolidates Vertex AI services to simplify agent deployment

Good morning from Las Vegas! I'm here on the ground at Google Cloud Next, where the opening keynote is just minutes away from kicking off.

And we're off, with what appears to be an AI-generated opening animation including Google Cloud products, customers, and achievements.

Thomas Kurian has now taken to the stage. He's kicked off the keynote by acknowledging the 75% of Google Cloud customers who use the firm's AI tools.

Google's advantage is its full-stack approach, he says. To tell us more, we're now hearing from Sundar Pichai, CEO at Google (via a pre-recorded video address).

Pichai says that in 2022 Google invested $32 billion in CapEx, and now expects to spend $175-185 billion in CapEx.

Today, Pichai says, Google is using agents not only to produce code, but migrate codebases.

In a recent example, Pichai adds, Google developers found that they could migrate code 6x faster with AI agents. This approach is now informing the entire company's approach to development.

Security teams are also benefiting from AI agents, he says, with security operations center agents triaging thousands of unstructured threat reports each month.

Pichai says that businesses are focused on how to manage thousands of agents. For this purpose, Google Cloud has revamped Gemini Enterprise as a "mission control" application. Read more in our write up here.

Kurian is now back on stage, to talk more about Gemini Enterprise.

"In bringing Google Al to every employee and every workflow, Gemini Enterprise is now the end-to-end system for the agentic era," he says.

"The connective tissue between your data, your people and your goals, it transforms disconnected processes into a single intelligent flow. This is our blueprint for the agentic enterprise."

Zooming in on specific features, Kurian summarizes some of the key benefits of the newly-unified Gemini Enterprise Agent Platform. This new offering connects existing Vertex AI features with wider agent deployment and management tools, to help equip businesses with a comprehensive dashboard for their AI agents.

Google Cloud is leaning into its partnerships, and Kurian proudly announces that models such as Claude Opus 4.7 will be available on the platform as default.

In a tease, he also acknowledges Google's partnership with Apple, which will see Gemini underpin AI personalization features within the iOS ecosystem.

"Earlier this year, we announced a monumental partnership with one of the world's most iconic brands, that'll bring the power of our technology to users everywhere around the world," he said.

"We're collaborating with Apple as their preferred cloud provider to develop the next generation of Apple foundation models, based on Gemini technology. These models will help power future Apple Intelligence features, including a more personalised Siri coming later this year."

Kurian also proudly announces that Gemini Enterprise agents were used by NASA for key elements of the Artemis II moon mission.

Managing agents can be a major task for IT teams, especially if agentic actions can't be easily tracked or observed. Features such as Agent Observability help streamline these processes, Kurian says, as covered in more detail in our write-up linked above.

Google Cloud really wants workers to collaborate with agents and this is what Projects in Gemini Enterprise is all about. It's a collaboration platform that combines human and AI-generated work, with built-in functionality for sharing docs to Microsoft 365.

We're now hearing from Virgin Voyages about how it's using Gemini Enterprise to streamline its customer and crew experience.

The firm has reduced its production timeline by 60% and raised month on month sales by 28%.

Using Google Distributed Cloud Edge, Virgin Voyages also runs Gemini models securely on its cruise ships.

It's time for a demo to put this all in context. Here to show us how it all works is Erica Chuong, manager, Applied Al Forward Deployed Engineering at Google Cloud.

With one prompt, Chuong can research interior design trends in her demo identity as an employee at a furniture retailer.

Based on the results, Chuong can also ask Gemini Enterprise to generate landing page videos for her website using Veo 3. She can then ask Gemini to automatically inform the relevant developer about the changes needed for the website.

With that, we're back to Kurian, who is running us through some customer examples.

Walmart is using Gemini Enterprise to improve its in-store operations. It's providing each worker with a Pixel Fold, with Gemini on device helping them to answer customer queries and solve supply chain problems.

Google Cloud also worked closely with Team USA in the 2026 Winter Olympics.

Shaun White, Olympic gold medallist for snowboarding, has just appeared in a blast of fog and accompanied by arena-wide fake snow to tlel us more.

Jason Davenport, arena tech lead for Developer Experience and AI at Google, is on stage alongside White to run us through some of the innovations that Google has made for winter sports.

Using a tool built by Google DeepMind, Google Cloud can analyze snowboarding footage, mapping a wireframe over White and tracking stats such as rotational velocity, flight dynamics, and tuck compression.

Moving on, we're now hearing from Amin Vahdat, SVP and chief technologist, AI and Infrastructure at Google.

Vahdat starts on the subject of Google Cloud's AI Hypercomputer, its end-to-end energy, software, and infrastructure stack that powers its data centers.

But Vahdat is really here to formally announce TPU 8t and 8i, Google Cloud's newest custom chips for AI workloads. We've covered these in more detail here.

The TL;DR for these chips is that Google Cloud has now split its TPUs into two distinct offerings, one optimized for training and the other for inference.

This is crucial as the hyperscaler looks to support massive AI agent demand, with huge leaps achieved for reducing latency and improving its networking technologies to loop ever more chips into distributed clusters.

These chips are underpinned by Google Cloud Axion, its in-house CPU that Vahdat explains offers 2x price performance vs x86 instances.

Google Cloud doesn't solely rely on TPUs, nor does it expect its customers to go all-in on the specialized chips. Vahdat announces that Google Cloud will be one of the first cloud providers to gain access to Nvidia Vera Rubin NVL72, which provides many exaflops of inference per data center rack.

In practice, TPUs are already speeding up workloads. Citadel Securities is already using Google's previous generation TPU, Ironwood, to speed up its workloads many times over, reducing tasks that used to take months or weeks to just minutes.

These systems can't be managed by humans alone, Vahdat says:

"You cannot have humans managing and troubleshooting configurations: you need a cloud that drives itself. We have used some model context protocol to turn every Google Cloud service into a tool that agents can orchestrate directly by integrating Gemini reasoning into our own telemetry. The system now performs autonomous root cause analysis identifying and fixing this configurations before you even realize."

All of this is underpinned by Google Cloud's vast data fabric and pipeline, with Gemini built-in as standard.

Here to explain more is Karthik Narain chief product and business officer at Google Cloud.

Today, Google Cloud is announcing Knowledge Catalog, a universal context engine for enterprise AI, he says.

Knowledge Catalog continuously enriches enterprise data by analyzing logs and profiling data in the background.

"Now, the second that image or PDF hits Google Cloud Storage, they are instantly packed, enriched, and made agent-ready," Narain explains. "Zero manual data engineering, powered natively by Gemini, the Knowledge Catalog goes even deeper, reading files, autonomously extracting entities, mapping relationships and learning about your unique business, semantics."

Another major announcement today is a new corss-cloud lakehouse, that aims to eliminate data silos by connecting data across all of an enterprise's cloud platforms.

Combined with Knowledge Catalog, this means Google Cloud is working harder than ever to enrich enterprise data with context as standard. It also applies across partner data platforms, including Palantir, Salesforce, and Workday.

In a demo, Yasmeen Ahmad, MD, Product Management, Data and AI Cloud at Google Cloud, shows attendees how Google Cloud's new data breakthroughs are streamlining operations.

In the example, Ahmad collates data from thousands of ingredient PDFs to review a recipe for a new flavour of froyo that is suitable for soy-sensitive customers.

Ahmad can ask for Gemini to collate the data, cite sources to confirm ingredient allergens, research market size, and produce a revenue projection. Gemini is able to identify soy in an ingredient that may have been missed by human workers, then call the relevant research agents and models to pull together the business plan Ahmad asks for.

We're moving quickly here, straight onto AI powered security. Here to tell us more is Francis deSouza, COO and president, Security Products at Google Cloud.

Machine-speed security is an imperative, deSouza says, as the time between initial access by hackers and handoff to secondary threat groups has dropped from eight hours to 22 seconds.

To tackle this issue, Google is leaning on telemetry from Mandiant, VirusTotal, and Chrome and deploying triage agents to cut response times from 30 minutes to 60 seconds.

Google's acquisition of Wiz expands this protection even further. deSouza is now introducing Yinon Costica, co-founder of Wiz.

"Our mission from day one has been to help customers protect everything. They've been running. We began by unifying code, cloud, and runtime context to move at developer speed. But Al has fundamentally changed the environment," Costica says.

Wiz has launched red team, blue team, and green team agents to automatically identify, investigate, and fix critical risks faster than human security teams could.

"In the AI era, the best defense is to continuously use AI against ourselves in order to give the defenders the first mover advantage over the attackers," Costica says.

"And this is exactly what the Wiz Red Agent does. It validates every single exposure that we identified, and you can think of it as a friendly hacker that continuously scans your outside like an elite red team."

Costica is now showing us an example of a red agent having found an authentication bypass vulnerability within an enterprise AI agent.

"It's not a potential risk anymore, this is a validated risk," he says, adding that the Wiz AI Application Protection Platform (AI-APP) allows researchers to address these risks directly within the platform.

To achieve this, a user can hand the vulnerability data directly to a green agent, which can produce code to fix the risk as quickly as possible.

Back onstage, deSouza says Google Cloud is helping the Los Angeles Department of Water and Power secure critical infrastructure ahead of the 2028 Olympics. Morgan Stanley has also chosen Wiz within Google Cloud as a key component of its cloud security strategy, he adds.

Moving quickly on, Carrie Tharp, GTM COO & VP, Customer Experience at Google Cloud, is here to give examples of how the firm's agents are helping improve customer experience.

For example, Papa Johns is using Food Ordering Agent to streamline the pizza ordering process for customers.

Best Buy is using Gemini Enterprise for Customer Experience to autonomously guide customers through complex questions about product specs.

Home Depot is also leaning heavily on Gemini Enterprise, having been a Google partner for years, to build its 'Magic Apron' shopping assistant.

It's already seeing a 10% increase in sales conversions compared to traditional interactions, and is aiming to provide even more agentic experiences to customers via its website.

In India, Reliance is using Gemini to provide customers with product recommendations based on natural language requests such as "plan a birthday party".

Google is also acting as its own internal supplier, providing agents for offerings such as YouTube TV. In a demo, Patrick Marlow, senior product manager at Google Cloud, shows us how a telephony agent can answer questions about his YouTube TV plan including switching to Spanish mid-conversation.

All of these interactions are handled via CX Agent Studio, which allows IT workers to view the workflow of agents involved in customer interactions. They can also test the agents directly within the interface.

With a drop-down menu, Marlow can easily add a new sub-agent dedicated to a new promotion offer.

YouTube TV created this multilingual telephony agent in just six weeks, which Marlow says shows how Google Cloud allows leaders to "iterate at the speed of your business".

Moving on to Workspace, we're now hearing from Yulie Kwon Kim, VP of Product, Google Workspace at Google Cloud.

"We all spend half our day finding information, and the other half figuring out what to do with it," she says. To solve this issue, Google Cloud is introducing Workspace Intelligence, which embeds agents throughout productivity tasks.

In a demo, Kim shows us how a worker at a regional furniture brand can interact with Gemini directly within Google Chat.

Kim asks Gemini to find a specific merchandising playbook from last quarter, with a prompt describing it as "the one with the chart showing regional sales".

Kim can then ask Gemini to use a skill called Regional Campaign, to create a deck with a plan for Organic Living. This prompts Gemini to pull together organizational data to produce a deck inside Google Slides.

With that, it's back to Kurian to cover how Unilever is using Google Cloud at scale.

The consumer giant is using Google Cloud to speed up its AI deployment, with a goal to connect data more intelligently and strengthen its relationship with retailers.

Unilever has already deployed an agent via Gemini Enterprise Agent Platform, to speed up procurement analysis and decision-making.

Finally, Kurian pledges that Google Cloud is committed to letting its customers "choose your own destiny", with cross-platform support and by directly assisting independent software vendors and SMBs to adopt agents via Gemini Enterprise.

And with that, the opening keynote is over. ITPro will continue to bring you the latest from Google Cloud Next 2026, so be sure to check back regularly for news and analysis.

Google Cloud Next 2026 is just days away, and by the end of the week IT leaders will have a huge list of new products and offerings across the hyperscaler’s cloud, AI, and security portfolios to pore over.

This year’s event finds Google Cloud in a strong position. In Alphabet’s Q4 earnings, it reported a 48% year on year increase in Google Cloud revenue, driven primarily by demand for its AI platform and core cloud products.

Much of this spending, to date, has been from enterprises embarking on AI adoption and looking to use embedded Gemini features within their workplace. The value proposition going forward is for far more radical changes within the workplace, with AI agents automating more roles and delivering firm return on investment for IT leaders.

Businesses have had a year of hearing about the changes agents can usher in, and the value they can deliver. If they’re sold on that vision, they need to know which platforms deserve their investment now – which makes this year’s event especially pivotal for Google Cloud.

The opening keynote title this year is particularly vague: ‘The agentic cloud’. Needless to say, AI agents will be a key focus here, as with every major tech event right now. But Google Cloud has a unique opportunity here to explain why it’s the best vendor for a truly AI-native cloud, and how its agents can deliver better performance at a lower cost than its competitors.

Neither Microsoft nor Amazon create frontier, trillion-parameter AI models. If Google plays its cards right, it can capitalize on this first-party offerings gap to entice enterprise customers further into its cloud ecosystem. Google Cloud Next 2026 must answer two key questions: why Gemini? And why Google Cloud?

Infrastructure as a USP

The biggest bottleneck for AI training and inference at scale continues to be compute. Nvidia and AMD are going head to head trying to service an explosion in demand for hardware that can run AI workloads, and even Arm has entered the first-party chip market with a CPU designed for running AI agents at scale.

What makes Google stand out here is its global cloud infrastructure, which smoothly handles an unimaginable amount of data in the form of the world’s searches, YouTube videos, emails, and more. Beyond scale, Google has also spent years specializing its chips for AI workloads, resulting in its tensor processing units (TPUs).

Last year, we heard about Ironwood, Google’s TPU v7 designed to provide the raw computational power needed to train and run frontier AI models at the enterprise level.

I wouldn’t be at all surprised if this year’s TPU upgrades, which I’ll call TPU v8 for ease, come in the form of two distinct offerings. One, like last year’s Ironwood, would push the boundaries of compute in direct competition with the likes of Nvidia Rubin. The other could be fully optimized for AI inference, to efficiently meet the massive inference demands of AI agents.

Microsoft announced such a chip in January, the Maia 200, which it said offers better performance per dollar than Google TPUs and AWS Inferentia.

Rival labs can claim their models are better than Gemini, and credibly so in the case of the latest GPT and Claude Opus releases. But none train and run their models on in-house hardware that was designed in collaboration with their developers.

This has a knock-on effect for the cost of running agents. If Google Cloud can demonstrate that TPUs are the best choice for low-cost deployment of AI agents – and potentially low-effort, through platforms like Vertex AI Agent Builder – it will win hefty business investment.

Anthropic recently signed a deal for 3.5GW extra TPU capacity, in addition to an October 2025 deal to take control of up to a million TPUs in 2026. This is good news for Google, but it also demands answers such as which chips will Anthropic use and what, if any, are the benefits of choosing Gemini over Claude on Google hardware?

There’s a twist in the tail this year, in the form of OpenAI’s repeated infrastructure cancellations, first dropping its plans for Stargate UK and next for Stargate Norway. Not only do these roadblocks for OpenAI free up room for Google Cloud to shout about its infrastructure successes, Google itself is reportedly looking to rent the newly-vacant capacity at Nscale’s Stargate UK cluster.

This could provide headroom for more regional expansion in the UK, as well as diversify Google’s hardware stack in the region further (Nscale will use Nvidia chips in the facility).

If Google Cloud can knit all of this together, it can make a strong argument for the resilience, reach, and power of its AI infrastructure.

All eyes on Wiz

Last year’s event took place just before Google completed its $32 billion acquisition of Wiz, which will be rolled into Google Cloud while retaining its brand. Nearly one year on, I’m expecting to hear a lot about how Wiz has been integrated into the Google Cloud Platform (GCP) and where it’s already delivering results.

The addition of Wiz seriously expands Google Cloud’s ability to detect and protect cloud assets, with the two now combining their proprietary AI approaches in a unified security platform. But the other major benefit for Google is what Wiz brings in the multi-cloud domain.

In its announcement of the acquisition, Google highlighted that Wiz will continue to work on “all major clouds,” and that by combining forces Google Cloud customers will be better equipped to protect themselves across all their cloud environments.

With threat actors now using AI as a standard tool for attacks, enterprises and small businesses alike could be won over by the promise of automated cloud security that extends across their entire cloud estate.

Leaders could also be more willing to increase their Google Cloud spend in the knowledge that they can still protect assets in other cloud environments, which would allow Google to undermine the USPs of competitors such as AWS and Microsoft Azure.

I’ll be listening closely to what those within Wiz have to say about the deal, and where Google Cloud thinks the firm will best slot into its operations.

Will Gemini 4 be announced?

A question that’s undoubtedly on the minds of many attendees ahead of Google Cloud Next 2026 is whether a surprise release for Gemini 4 is on the cards.

While model release cycles are undoubtedly speeding up – Anthropic has announced five frontier models in as many months, including its gated cybersecurity model Claude Mythos – I think it’s quite unlikely that we’ll see a fully-fledged Gemini 4 at the event.

Google has a track record for this. Its frontier Gemini models tend to release around February or March, with talks at Google Cloud Next focused heavily on the enterprise capabilities of the latest model but never taking the event as an opportunity to announce a brand new one.

Gemini 1.5 Pro was the focus at Google Cloud Next 2024; Gemini 2.5 Pro was the focus at Google Cloud Next 2025. This year, we’re just a few months on from the launch of Gemini 3.1 Pro and unless Google DeepMind breaks form to speed up releases, technical demos will likely focus on the practical uses for this latest model.

Sorry AI fanatics, I just don’t see Gemini 4 being on the roster.

Expect lots of mentions for Google DeepMind’s less enterprise-focused models, though, such as image generator Nano Banana 2, music generator Lyria 3, and video generator Veo 3.1. Is the link between garish AI images and enterprise bottom lines tenuous at best? Absolutely. Will I be posting photos of said images being shown off at the opening keynote? Without a shadow of a doubt.

Nevertheless, Gemini will once again be the star of the show at Google Cloud Next 2026. Expect to hear about how it powers many of the features announced in the opening keynotes, as well as how businesses can use it to build more automation into their daily activities.

Rory Bathgate will be covering Google Cloud Next live from Mandalay Bay, Las Vegas between 22-24 April. To stay up-to-date with the latest news and announcements from the conference, follow our live blog and subscribe to the ITPro newsletter.

The European Commission has awarded a tender worth up to €180 million to procure sovereign cloud services for EU institutions, bodies, offices, and agencies over six years.

The tender was launched in October 2025 with the aim of strengthening the digital sovereignty of EU bodies and encouraging the market to offer sovereign solutions.

Requirements included strategic, legal, operational, and environmental considerations, along with supply chain transparency, technological openness, security, and compliance with EU laws.

Four providers and consortiums have been awarded contracts under the scheme, including:

• A Luxembourgish-French partnership led by Post Telecom, with partners CleverCloud and OVHcloud
• Germany's StackIT
• France's Scaleway
• A Belgian-French-Luxembourgish partnership, led by Proximus

The latter of these uses services from S3NS, a joint venture between Thales and Google Cloud. Clarence and French-based AI firm Mistral are also involved in the Proximus-led consortium.

"What’s interesting here is that technological sovereignty isn’t simply a theoretical concept: it translates into actual infrastructure and platforms with players able to operate together at a production level," commented Quentin Adam, CEO of Clever Cloud.

"This is also proof that European organizations can cooperate effectively and drive progress across the entire ecosystem.”

EC justifies Google Cloud involvement

The Commission justified the involvement of Google Cloud by saying that non-European technologies can meet the minimum level of sovereignty required by operating within a 'strict and appropriate' framework.

"While sovereignty was an important factor, the selected providers had to demonstrate that they can provide reliable, state-of-the-art technology and services, with a strong focus on fully managed services (PaaS), developer experience and automation," said the Commission.

"All awarded providers deliver a high level of technical quality for that scope, showing that EU providers are closing the gap. All awarded providers also achieve strong security ratings, demonstrating an excellent coverage in terms of security certifications."

High expectations

The providers were required to hit a Sovereignty Effectiveness Assurance Level (SEAL) of SEAL-2 - Data Sovereignty - requiring them to abide by EU laws and regulations without the need for additional technical measures by customers to protect their data.

Most of the awarded providers, in fact, reached SEAL-3 – Digital Resilience level – meaning that their service, technology or operations are immune from supply chain disruption from non-EU third parties.

"By successfully introducing sovereignty in its cloud procurement, the Commission leads by example in advancing Europe’s digital sovereignty, setting a benchmark for secure, compliant, and values-based cloud adoption across the public sector," the Commission said.

"The success of the tender highlights the high quality of European providers, demonstrating their ability to meet the Commission's strict criteria. It also shows that even non-European technologies, when operated within a strict and appropriate framework, can meet the minimum level of sovereignty required."

In future, the Commission plans to apply the sovereignty criteria it's developed to assess and enhance sovereignty across the digital services that it provides to its departments and other EU bodies.

It will also publish an updated version of the Sovereign Cloud Framework, based on what it's learned from this tender. process, which will be available to all.

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AI isn’t creating new classes of vulnerabilities, according to research from Wiz – but it is expanding the range of where well-known risks can appear.

Analysis from the cloud security firm found eight-in-ten cloud breaches last year were caused by basic mistakes. Common vulnerabilities, misconfigurations, and exposed secrets all ranked among the leading causes of breaches, Wiz found.

While the company called for enterprises to shore up basic best practices, the situation is being exacerbated by rapid AI adoption, which is creating larger, more complex attack surfaces.

"What changed was not the existence of these risks, but the environments in which they appeared and the speed at which they could be exploited," the company said.

Tried and tested methods

Wiz noted that the most common entry points in 2025 weren’t novel cloud-specific exploits or advanced identity bypass techniques, but familiar weaknesses in exposure management, credential handling, configuration, and end-user security.

Wiz said this highlights that threat actors are still recording success by capitalizing on the basic fundamental mistakes made by enterprises.

Elsewhere, the majority (53%) of pre-access malicious actions were reconnaissance and discovery-related techniques, for example.

This showcases the increased investment among threat actors on mapping environments and testing trust boundaries, according to Wiz.

AI is expanding attack surfaces

More than 85% of organisations are now using some form of AI, according to Wiz, which is creating new attack surfaces for security teams to monitor and shore up.

Notably, researchers warned this is increasing the number of places where familiar issues – such as misconfigurations or exposed credentials - could appear.

Given these services are often tightly connected to sensitive data, privileged identities, and high-value compute resources, the implications for poor practices on this front are dire.

Attackers are using AI at scale

While AI is creating new threats for organisations, Wiz warned the technology is also being used by threat actors to accelerate attacks.

This has become a common recurring talking point in recent months, with a slew of studies warning about the increased use of the technology for nefarious purposes.

Hackers have been observed using AI to dissect threat intelligence reports and reverse engineer malware, for example, or to create more convincing phishing lures.

Wiz noted, however, that attackers haven’t replaced tried-and-tested techniques with AI. Instead, they’re using the technology to accelerate reconnaissance, automate actions, and scale workflows.

Researchers said threat actors are now incorporating AI tooling into operations in a variety of ways, including AI-assisted malware execution, abuse of AI-based CLI tools such as Claude, or Gemini, and for environment reconnaissance after gaining initial access.

What can defenders do?

Given the key initial access vectors highlighted by Wiz, researchers said enterprises should sharpen their focus on identifying which assets are externally reachable and which risks are exploitable from the outside.

Continuous visibility into exposure and potential attack paths can also help teams focus on risks that are realistically exploitable.

Wiz also urged enterprises to treat pre-compromise reconnaissance as a detection opportunity, providing they can react swiftly.

The early part of the operations require malicious actors to not only gain some level of privileged access to a network, but conduct internal reconnaissance to understand where they are and how to accomplish their goal,” the report notes.

“This creates an opportunity for defenders to identify malicious activity before they are able to accomplish their goals.”

The number and severity of incidents involving compromised packages, CI systems, SaaS integrations, and automation workflows showed how inherited trust can extend impact beyond a single environment.

Wiz added that defenders should maintain visibility into trusted relationships across development pipelines, third-party services, and identity federations, and correlate these relationships with exposure and identity risk to reduce downstream impact.

"Security teams that maintain visibility into exposure, identities, and how risk propagates across cloud, development, and AI systems are better positioned to detect and disrupt attacker activity before it escalates."

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Sovereign cloud technology investment is growing quickly. Indeed, analyst firm Gartner predicts that global spending in this sector will reach $80 billion in 2026, representing a 36% annual increase. What was once treated primarily as a compliance issue has quickly evolved into a strategic priority for enterprise IT leaders.

For channel partners, this shift signifies a fundamental change in how enterprise infrastructure should be designed, deployed, and managed. Customers are no longer concerned solely with where their data resides; they want assurance that their entire digital ecosystem, from networks to analytics pipelines, complies with the relevant legal and regulatory frameworks.

Sovereignty is often treated as a single configuration choice. However, in practice, it is a complex architectural discipline. In a world of hybrid clouds, distributed applications, and globally flowing data, no single “sovereign template” can meet the needs of every organization.

Partners who recognize this complexity and design infrastructure accordingly can position themselves as trusted advisors, while those who treat sovereignty as a checkbox risk falling behind.

Global scale, local regulation

Traditionally, enterprise IT strategies have favored standardization. Uniform platforms simplify procurement, reduce operational complexity, and enable organizations to scale efficiently across regions. Data sovereignty requirements, however, rarely align with these global templates.

Regulatory frameworks vary widely across different jurisdictions. Some emphasize data residency, requiring information to remain within national borders, while others focus on encryption standards, operational oversight, or access governance. And in highly regulated industries such as financial and legal services, these frameworks can be particularly intricate, evolving in response to national and geopolitical priorities.

For multinational companies, this creates a delicate balancing act. A single architecture may satisfy requirements in one region but fall short in another. Companies can over-engineer solutions, adding unnecessary costs and operational complexity, or underestimate local mandates, leaving themselves exposed to regulatory penalties.

Channel partners are increasingly called on to navigate this complexity on behalf of their customers. A retailer expanding into Asia, for instance, will face very different sovereignty requirements than a European financial institution operating under strict supervisory oversight. Treating both scenarios the same is neither practical nor efficient. Instead, infrastructure needs to be adaptable, flexing to local rules without fragmenting the entire IT environment.

But this flexibility introduces what architects often call the “complexity tax.” As systems adapt to multiple jurisdictions, operational overhead rises, with maintenance, patching, monitoring, and skills requirements all increasing. Partners will deliver the most value when they can help customers strike the right balance between achieving global scale and ensuring local compliance.

Engineering sovereignty end-to-end

Data sovereignty extends beyond location. While it matters where data is stored, it is only one piece of a much larger puzzle. The greater challenge is maintaining control across the entire lifecycle.

Organizations must consider who can access the data, under which legal jurisdiction, and how it moves across networks. To this end, encryption standards, backup locations, analytics platforms, and cross-border data transfers all carry implications for sovereignty. Even locally stored data may fall under foreign legal reach depending on a provider’s corporate structure.

AI adds another layer of complexity. High-performance machine learning often requires centralized datasets, yet regulations may mandate that these datasets remain within sovereign boundaries.

Sovereignty is therefore a full-stack concern. Channel partners delivering cloud, connectivity, and managed services must think beyond individual products and ensure control spans storage, networking, compute, analytics, and governance layers simultaneously.

Organizations that excel in this environment are those that map data flows from end to end. They know where data originates, how it moves, how it is processed, and who ultimately controls access. This level of visibility is critical not only for compliance but also for managing risk and maintaining customer trust.

Maintaining visibility

As enterprise infrastructure becomes increasingly distributed, maintaining oversight grows more challenging. Hybrid cloud, edge computing, and multi-cloud architectures create operational silos, each with its own tools, policies, and access controls. Over time, these fragmented environments can obscure data flows, creating governance risks for IT leaders.

One approach currently gaining traction is the “single pane of glass” for infrastructure visibility. The aim is not to centralize everything under one vendor, but to establish unified monitoring and policy management across multiple environments. With the right tools, organizations can track cross-border data flows, enforce encryption standards, and audit access controls from a unified operational view.

Crucially, these platforms must be carefully designed themselves. Centralized monitoring cannot become a loophole allowing sensitive data to leave a jurisdiction. Here, channel partners can guide customers in building federated systems that maintain compliance without limiting operational agility.

Performance still matters

Compliance is essential, but it should not come at the expense of user experience. End users expect low latency, reliable services, and seamless digital experiences, wherever they are. If infrastructure meets regulatory requirements but fails to meet these expectations, it will ultimately undermine the business it is meant to support.

Modern sovereignty strategies address this through distributed infrastructure models that keep services close to users while maintaining regulatory control. Edge computing, regional cloud deployments, and optimized connectivity all help achieve this balance. For channel partners, this is a reminder that sovereignty is about aligning compliance, security, and performance to deliver tangible business outcomes.

A new advisory opportunity

Global supply chains, distributed teams, and cross-border collaboration are now integral to enterprise operations. At the same time, though, regulatory fragmentation is increasing. Governments are introducing new data protection frameworks, tightening oversight, and placing greater emphasis on digital sovereignty.

Organizations face the dual challenge of providing global agility while ensuring local compliance. This presents an opportunity for channel partners to expand their advisory role. Customers now need guidance not only on cloud deployment, but also on architectural strategy, governance models, and long-term infrastructure planning.

Sovereignty is not a one-time project or the selection of a particular cloud region. It is an ongoing operational discipline that evolves alongside legislation, technology, and geopolitics. In today’s distributed digital economy, sovereignty is defined not just by where data resides, but by the intelligent design of the architecture that surrounds it.

Partners who understand this and help their customers build flexible, transparent infrastructure will be well-positioned for the next phase of enterprise IT.

Oracle is betting big on the scalability and reliability of its cloud offerings, as it looks to convince customers that off-the-shelf AI agents grounded in secure data can meet business needs better than the bells and whistles of its competitors.

In the opening keynote at Oracle AI World Tour London, Oracle CEO Mike Sicilia said mass adoption of generative AI has rapidly accelerated the pace of innovation and demand for new breakthroughs.

“Expectations in the age of AI haven't simply risen,” Sicilia said, “they've reset entirely and remarkable things that felt out of touch just a few years ago, a few months ago, and in some cases just a few weeks ago, are now the new normal.”

To meet this demand, he said, Oracle is looking to reengineer the cloud behind the scenes so that enterprises can adopt, deploy, and manage their data and enterprise AI tools as easily as possible.

Nothing Sicilia said on stage would have come as a great surprise to attendees. In fact, the most surprising thing to me throughout the keynote was the confidence with which Oracle is promoting its ‘stay the course’ approach to cloud and AI in 2026.

Scalable cloud for reliable AI

Oracle’s pitch is simple: scale and stability. Rather than becoming overcomplicated with AI additions, Oracle intends for Oracle Cloud Infrastructure (OCI) to facilitate AI adoption. This is a ‘more is more’ approach to customer choice.

Sicilia argued that only with dependable cloud infrastructure and the option for customers to choose their own AI model, data formats, and cloud residency, can AI success really be achieved.

“It’s no secret that at Oracle, we are building the world’s largest AI cloud,” Sicilia said.

“We think that this will all show up where the work actually happens, and that is servicing the AI directly in the applications and workflows that you're already living in, right at your fingertips. Whether you're in finance, HR, manufacturing, retail, healthcare, financial services and beyond.”

At Oracle AI World Tour London 2026, the firm unveiled 22 new Fusion Agentic Applications in line with this goal, powered by off-the-shelf AI agents trained in enterprise-specific tasks including identifying sales opportunities, coordinating supply chain operations, and automating cash flow.

We’ll have to wait and see if these agents deliver a real return on investment.

While data and fine-tuning play a major role in the success of AI deployment, Oracle sticks out as a hyperscaler without AI models of its own. Google Cloud offers its customers first-party Gemini offerings and Microsoft Azure boasts privileged access to OpenAI’s model catalog. AWS – which doesn’t spring to mind when I hear ‘frontier model developer’ – has put in work with its Nova model family, even if analysts question its confidence in the offering.

Oracle, by contrast, has no models of its own, nor an exclusivity deal with a major frontier lab. Though it was quick to form partnership with AI developer Cohere in 2023, whose models form the basis for Oracle NetSuite’s generative AI features, Oracle has subsequently bet on an agnostic approach.

Let’s get this out of the way: there’s obvious merit for the strength of OCI for running intense compute for enterprise AI. This is particularly when it comes to tasks such as training and fine-tuning LLMs, as Steve Miranda, EVP of Oracle Applications Development, explained to assembled media at the event.

“We have significant technical advantage in OCI, especially around the speed of network and security, which is why the Nvidia infrastructure runs best on OCI, which is why most, if not all, the top large language models in the world have chosen OCI to train their models,” said Miranda.

I suspect, however, that this won’t matter so much at the enterprise level. Business leaders aren’t generally looking to train or even fine-tune their own LLMs, they’re looking to deploy whichever pre-made AI tool delivers them the most value.

Customers willing to forgo flexibility and an open ecosystem in the name of the latest end-to-end AI deployment may find their head turned by Azure with its OpenAI tie ups, or the first-party model developer Google Cloud.

In the face of this competition, Oracle’s commitment to openness reads more like a bet against AI innovation, not for it. The offerings that Oracle thinks are make or break for customer retention aren’t AI at all, but Oracle Databases.

Indeed, Oracle has even gone to the length of partnering with other hyperscalers to make Oracle AI Database services available alongside competitor offerings. In these circumstances, customers can make use of OCI Exadata hardware directly next to, or even in, data centers run by AWS, Azure, and Google Cloud for low latency ease of access. This could be a major draw for customers looking for a low-friction, multi-cloud approach.

Nathan Thomas, SVP of product management, OCI at Oracle, explained how customer demand has driven OCI to support a varied, multi-cloud strategy.

“We hear from customers all the time that they want to use their AI pipelines in those environments,” he said.

“They want to be using Vertex, they want to be using Copilot, Sagemaker, Bedrock, and they want to have their data local to that, low latency, high performance, accessibility to it. But they still need to take all the governance with them.”

Over the long-term, Oracle is betting that customers will stick with it for both its openness and consistent, full-stack approach. Frankly, it’s too late to now change course – it can’t compete with the likes of Google Cloud for walled-garden AI capabilities and it seems to know this.

“There is nothing special, from a technical perspective, about the agents we build,” Miranda said, adding that the framework Oracle lets customers run its agents in a wide range of agentic frameworks.

Customers may choose stability, and neutrality, over specialization and the potential for lock-in when they choose their cloud provider. The uphill challenge for Oracle is convincing customers that this ‘nothing special’ approach is enough to give them an edge over competitors.

A simple and scalable foundation for enterprise data

Sicilia says one of Oracle’s major strengths is there’s “only one version of OCI”, allowing customers to plan for deployment without having to plan for compromises or region-specific versions.

“The form factor, the physical location, whether or not it's consumed in a multi-cloud environment, it doesn't matter,” he said.

“There's one version and that is, we think, to the customer’s advantage because we're able to look at all of the services, 100% of the services, no matter how big or how small the data center is, no matter how big or how small the consumption model is, as well as all the same privacy, security, cyber defenses.”

Thomas explained that Oracle’s approach allows customers to trust in a consistent application experience. He added that specializing in the creation of small clouds has fed directly into Oracle’s capacity for running private, distributed cloud regions directly in customer data centers, an offering he described as “a huge part of our sovereignty play”.

While digital sovereignty is a huge focus for hyperscalers, Oracle hopes its full-stack approach – as outlined by Sicilia – will help it stand out from the competition.

“This is an area where we really feel like a lot of the other cloud vendors have been let off the hook,” Thomas said.

“There's some sort of idea that, ‘hey, what OCI is doing is super special or different’. We kind of think that's backwards. You know, every cloud really should by default be able to scale down, like we've got all 150 OCI web services available on all of those form factors, the pricing is the same in all those environments, and it’s still metered billing the way that we have in any public region.”

Oracle is striving to meet rising demand for sovereign cloud in Europe amid geopolitical uncertainty and a push for reduced reliance on US cloud providers. The strength, in practice, of sovereign cloud controls compared to those offered by competing hyperscalers depends on the level of separation customers desire and unknowns such as the US CLOUD Act.

Like its competitors, Oracle argues that it offers ‘operator sovereignty’, allowing workloads segregated from the public cloud to be run not just within specific regions but by staff who are residents of that region. But this wouldn’t be enough to protect EMEA data from the US CLOUD Act, should Oracle choose to comply with a government directive to access data.

With geopolitics where it is, I suspect Oracle is making the argument a few years too late. European giants such as Airbus reportedly moving critical apps to a sovereign European cloud and White House PR surrounding Oracle becoming TikTok’s US secure cloud provider may have strengthened the association between the firm and the US government in the minds of EMEA customers.

It feels to me like sentiment toward US cloud providers across EMEA only goes in one direction right now, no matter how enticing the offer. In these conditions, Oracle may need to do more to lure new customers in and this may mean going beyond the slow and stable approach to which it’s currently committed.

Cloud maturity is having a direct impact on AI adoption, new research suggests, with only 14% of firms at the highest level of expertise.

According to NTT Data's new report, Cloud-led innovation in the era of AI: The new rules for driving value with cloud, while 99% of organizations say AI is increasing demand for cloud investment, 88% say current levels of spending are putting AI, cloud native, and modernization initiatives at risk.

Notably, as expectations rise, there's a disconnect between ambition and reality, the company warned. While the cloud is seen as essential for innovation, fewer than half of organizations said they were satisfied with its impact, or with the progress their modernization efforts were making.

“AI is accelerating faster than enterprise cloud maturity,” said Charlie Li, president, global head of cloud and security at NTT Data.

“Cloud has moved well beyond infrastructure and is now the execution layer for AI. Organizations that fail to evolve their cloud foundations risk constraining the growth and value of their AI investments."

Cloud leaders or organizations that indicated they were “cloud evolved” – the most advanced in terms of cloud adoption and impact, and with solid business performance – are significantly better positioned to capitalize on AI, said NTT.

The firm's advice is to develop cloud and AI strategies in tandem: AI demand is rising, it said, but alignment is uneven. chief AI officers (CAIOs) were 22% more likely than CIOs and CTOs to say that AI increases cloud investment needs, while AI is cited as the top cloud skills gap.

Cloud architecture choices are crucial, with organizations increasingly adopting a mix of public, private, hybrid and sovereign cloud models. Nearly all respondents told NTT they expect private cloud growth and sovereign cloud adoption to grow 50% in two years.

However, despite widespread agreement that cloud should drive innovation, half said legacy applications and data platforms are holding it back. Modernization is the top priority for the next two years.

Similarly, as investments stall and environments become more complex, more than half cited cloud cost management challenges, with organizations expecting a threefold increase in fully managed cloud platforms.

Mastering the cloud

Organizations should reset their cloud transformation KPIs, according to NTT Data.

Although AI is critical in helping organizations shift from technical to business metrics for cloud initiatives, adoption remains uneven. Nearly half of cloud leaders used AI in their last cloud migration project, compared with just a third of the rest.

Elsewhere, NTT Data noted that while security is the top cloud investment priority, confidence remains uneven, with 68% of the leaders feeling highly confident, compared with 36% of all others.

Leaders are also much more likely to define clear roles and responsibilities backed by regular audits.

"Our clients who are succeeding are treating cloud as a value creator, not a technology initiative," said Li.

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Red Hat has expanded its partnership with Google Cloud in a bid to supercharge legacy infrastructure modernization and cloud migration.

As part of the deal, Red Hat OpenShift will now be integrated with the Google Cloud console, making life easier during onboarding and when managing workloads alongside Google's own native compute.

Similarly, Red Hat said OpenShift Virtualization is now available on Red Hat OpenShift Dedicated on Google Cloud, letting customers use traditional virtual machines (VMs) and modern containers in the same place.

“Red Hat’s hybrid cloud vision is built on consistency – the ability to run any workload, anywhere, with the same operational model," said Mike Barrett, vice president and general manager of Hybrid Cloud Platforms at Red Hat.

Red Hat OpenShift on Google Cloud Console

The integration of Red Hat OpenShift with Google Cloud console will help streamline workload optimization on the platform, the company said, and simplify onboarding processes.

"Red Hat OpenShift Dedicated users can natively validate Google Cloud prerequisites before transitioning to a guided cluster provisioning flow within the Red Hat Hybrid Cloud Console," the company noted.

The integration will also simplify billing and procurement, with pay-as-you-go billing through Google Cloud Marketplace that applies their Red Hat subscription against Google Cloud spend.

Beyond that, the deeper collaboration allows for native service integrations, with support for services including Google Cloud Secret Manager, Certificate Authority Service, and Workload Identity Federation.

“Our customers are constantly looking for ways to simplify their infrastructure and accelerate innovation without sacrificing performance," said Nirav Mehta, vice president of Product Management for Google Cloud Compute Platform at Google Cloud.

"Customers now have a smoother path, enabling them to run both virtualised and containerized workloads consistently on Google Cloud's global, secure, and performant infrastructure."

Red Hat OpenShift Virtualization

Red Hat OpenShift Virtualization is designed to help enterprises modernize legacy infrastructure at their own pace, using modern containers in a way that lets old and new work together.

The tool allows traditional VMs to work seamlessly with more modern containers and serverless workloads on a consistent Kubernetes platform, the company said, letting users have a single interface, toolset and operational practices across workloads.

Additional tools, such as a migration toolkit and Red Hat Ansible Automation Platform, also aim to make shifting to modern systems easier.

Beyond upgrades and migration, RedHat OpenShift Dedicated on Google Cloud offers easy scaling and more predictable performance, Red Hat said.

"This extended collaboration with Google Cloud further empowers organisations with comprehensive cloud native capabilities of Red Hat OpenShift, whether they need to accelerate application development or streamline migration to the cloud," said Barrett.

"Together, Red Hat and Google provide a clear, unified path for organizations to modernize their entire application portfolio, helping them manage both their traditional VMs and containerized applications on a single platform."

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A cloud industry group has filed an antitrust complaint with the European Commission, claiming that Broadcom is seeking to use unfair software licensing terms to control the European cloud market.

Cloud Infrastructure Service Providers in Europe (CISPE) is aiming to prevent Broadcom from terminating VMware’s Cloud Service Provider (CSP) partner program at the end of this month, saying it represents an existential threat to European cloud providers.

“With the termination of the Broadcom program allowing access to VMware virtualization software, businesses - both cloud providers and their customers - are being irreparably damaged by Broadcom’s unfair actions, which we believe are illegal," said CISPE secretary general Francisco Mingorance.

"After imposing outrageous and unjustified price hikes immediately following the acquisition of VMware, Broadcom is now applying the ‘coup de grâce’. We need urgent intervention to force them to change. The only way to stop bullies is to stand up to them.”

Earlier this year Broadcom announced plans to terminate the program in Europe, keeping only a minority of specially-picked partners and excluding most European CSPs from selling VMware products, CISPE claimed.

The trade group claims these changes will create virtual monopolies in some markets with vendors entirely dependent on Broadcom.

What CISPE wants from Broadcom

CISPE is now asking the European Commission to implement interim measures. It wants the decision to terminate its VCSP partner program to be immediately suspended, with the readmittance of European cloud service providers, allowing them to continue to sell VMware.

It also wants the reintroduction of the ‘white label’ program, axed by Broadcom in 2025, which previously allowed SMB and smaller cloud providers to offer VMware software as part of their cloud solutions.

Finally, CISPE believes that explicit protections against retaliation from Broadcom may be needed, with a system of fines to ensure compliance with these terms.

Broadcom is locked in an EU battle

The plan to terminate VMware’s Cloud Service Provider partner program came on top of other changes following Broadcom’s acquisition of VMware.

These included price hikes, bundling, and demands for up-front payments alongside minimum commitments based on potential, rather than actual, usage.

CISPE said these moves have increased costs for customers by more than 1,000% in some instances.

"In 2024, within just a few weeks, VMware changed its prices, licensing model, ordering processes, and more — without providing customers with the necessary information or sufficient time to adapt," said Danielle Jacobs, CEO at Beltug, the Belgian Association of CIOs & Digital Technology Leaders.

"To this day, our members continue to contact us about the difficulties they face. The announced shake‑up of the previously well‑functioning ecosystem between VMware partners and end users has only added to the challenges and uncertainty."

CISPE's complaint supports a similar request from Voice e.V, which represents many of the largest IT customers in Germany, filed in May last year. Broadcom has rejected the allegations.

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HPE has announced a major expansion of its Private Cloud AI portfolio, with a particular focus on data sovereignty capabilities.

Unveiled at Nvidia GTC in San Jose, California, updates to the service will include an air-gapped configuration for HPE Private Cloud AI. This will allow enterprises to ensure data isn’t exposed to external networks and kept in a secure, isolated location.

In a press briefing ahead of the annual conference, Dale Brown, head of growth for AI solution at HPE, said the move comes in direct response to the growing enterprise focus on sovereignty and security, as well as evolving regulatory compliance requirements.

“We’re meeting customers where they are,” he said, adding that there are three overarching considerations at play for organisations with regard to sovereignty.

“The interesting thing about sovereignty, if you notice, sovereignty always has to be put in context of customer need and use-cases,” he told assembled media.

“So security might mean completely disconnected from the internet, but also sovereignty might mean that the data can’t leave a particular country and/or a particular principality.”

Brown added that the solution “provides even extra security” through a private cloud model, “bringing AI to the data” but also offering enterprises a higher degree of control of how data is processed, and where.

HPE Private Cloud AI capacity boost

HPE also announced new capacity scaling capabilities for Private Cloud AI customers at Nvidia GTC. This includes the ability to scale network racks up to 128 GPUs, marking an increase from the previous limit of 64.

The company said this is aimed primarily at allowing customers to accommodate “larger, more demanding AI workloads” while still maintaining a “consistent operational experience”.

Again with a focus on sovereignty, HPE confirmed that the Private Cloud AI service, as well as HPE ProLiant servers and HPE AI Factories, will support the latest Nvidia Nemotron open models.

These form part of Nvidia’s Agent Toolkit, which helps streamline the deployment of agents in on-prem and sovereign infrastructure environments for enterprises operating in critical or regulated industries.

Brown told journalists that HPE is also adding a raft of new “additional configurations and deployment modalities” for Private Cloud AI customers.

The service includes access to an array of Nvidia AI Enterprise software and blueprints, for example, including Nvidia’s updated AI-Q blueprint for AI agents.

New Nvidia Omniverse blueprints for digital twins are also available, the company revealed.

“Ultimately, our partnership with Nvidia has been all about removing friction, repeatable patterns, speeding time to value with proven best tools and techniques between our companies,” he said.

“We’re super proud of how we’ve co-engineered these solutions consistently.”

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Amazon Simple Storage Service (S3) is 20 years old, and while simplicity was, as the name suggests, the original inspiration, it has since become a sprawling platform.

But that doesn’t mean simplicity and ease of use aren’t still core tenets of the storage service, according to Andy Warfield, vice president and distinguished engineer at Amazon Web Services (AWS).

Speaking to ITPro ahead of the anniversary, Warfield said simplicity is still the “singular most important aspect” of S3. When it launched in 2006, a brief blog post outlined the core goal: “Amazon S3 is the storage for the internet”.

With a simple Representational State Transfer (REST) interface, users could PUT (store objects) and GET (retrieve them later) with relative ease. Warfield told ITPro that “parking the service” behind this architecture made it simple and easy for customers to adopt.

“At the time S3 launched in 2006, a lot of the verbs that we were using and a lot of the ways that the team approached presenting storage [were] actually really guided by the existing HTTP verbs,” he explained. So things like the GET and PUT support.”

“It made it incredibly easy to adopt. I think one thing that kind of taught the team, and it’s been true through the lifetime of S3, is that we do best when we focus on the customer, which you hear from us all the time, but on delivering for the customer in a way that is as simple as possible to consume so folks don’t have to do extra work.“

An explosion of growth for Amazon S3

To say Amazon S3’s growth over the last two decades would be a gross understatement. When it launched in 2006, S3 offered a total of one petabyte of storage capacity, spread across several hundred storage nodes in three data centers – it also had a maximum object limit of 5GB.

Compare that to 2026, and the numbers are jarring. As ITPro reported at AWS re:Invent 2025, the service now offers a maximum object size of 50TB, marking a ten-fold increase on the original limit.

Additional figures detailed by the company emphasize the sheer scale of S3 compared to its early days. Today it boasts:

• 500 trillion stored objects globally
• 200 million data requests per second
• 123 Availability Zones worldwide
• 39 AWS Regions

S3’s rapid-fire growth coincided with – and benefited from – an enterprise data explosion during the late 2000s and early 2010s. Indeed, Warfield notes that in 2006, you would have been “hard pressed to find something that you wanted to put in an object that was larger than 5GB”.

But with growing camera resolutions as a simple example, the rapid accumulation of enterprise data, and the formation of data lakes, demand surged. In 2026, Warfield admits the numbers are still rather mind-boggling.

“One that stands out to me is that the service processes over a quadrillion requests every year,” he said.

“We have tens of thousands of customers who, each individually, have objects that are spread over more than 10 million hard drives,” Warfield added, noting that the prospect of an individual enterprise building a storage setup of that scale as a “wild thing to think about”.

Underpinning enterprise innovation

Amazon S3 quickly moved beyond being a simple object storage service. Indeed, it became the underpinning foundation and enabler of customer data innovation.

The service helped kickstart the creation and subsequent growth of data lakes, and today, more than one million of these are stored on AWS. Warfield described this as a “structural aspect of why S3 is successful”.

Enabling enterprises to host data in a single source and all in one place helped break down long-running siloes that many had battled for years. It created a “shared foundation” for data.

“The data gives them this incredible flexibility and velocity to move quickly. And the data ends up being non-zero sum in terms of future value,” he said, reflecting on discussions with one particular customer.

“They often find that data they built for one system allows them to move into another opportunity.”

S3 in the age of AI

S3’s flexibility in adapting to customer demands over the last 2o years has been a key factor in its longevity and success - along with that of the company at large. AWS still boasts a large hyperscale market share o alongside Google Cloud and Microsoft Azure, for example.

With the advent of generative AI raising the stakes for big tech providers, the storage service is once again evolving to accommodate skyrocketing storage demands.

Indeed, the company is heavily focused on positioning S3 as the critical foundation for AI workloads. At AWS re:Invent in December, the company officially cut the ribbon on S3 Vectors, which has generated significant excitement across the company.

When it comes to AI, vector search is used to identify similarities between specific data points. At the time, the company described vectors as a "numerical representation of unstructured data created from embedding models".

Uploading, storing, and querying vectors is costly, though which is something this particular service aims to remedy. The company claims it can reduce costs on this front by up to 90%.

“The thing that we really wanted to do with S3 Vectors was to get a vector indexing service that had the simplicity and elasticity of S3 that you could just pick it up and use it with just ten vectors, but scale to billion and trillions of vectors and pay something that was closer to storage costs because it was anchored on hard drives,” Warfield explained.

“We’ve seen incredible growth on it.”

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The Met Office has marked one year since it transitioned to cloud supercomputing hosted in Microsoft Azure to generate its critical weather data.

Best known for its weekly weather forecasts, the Met Office is a trading fund within the UK government. But beyond its role at a consumer level, it’s also a critical partner for government and industry as the unique provider of past and present weather data, as well as detailed projections for real-world decision making.

Charles Ewen, chief data and information officer at the Met Office, told assembled media that since partnering with Microsoft in 2024, the Met Office has worked to improve its scalability, flexibility, and resilience while also markedly increasing its compute capacity.

This is the Met Office’s first foray into enterprise-scale cloud computing, and as of last year, has seen the organization move away from on-premises computing for its forecasts.

Microsoft now hosts a cloud cluster for the Met Office, which delivers approximately 60 petaflops of computational performance for functions that could normally only be carried out on a scientific supercomputer, such as climate simulations and weather forecasting.

Ewen explained that cluster is one of the top five CPU clusters in the world, with 1.8 million cores operating at three-nines uptime.

The Met Office’s history of innovation

The Met Office has a storied history of supercomputing, having first used the EDSAC computer at Cambridge in 1952 and in 1959 adopting its first full-time forecasting computer in the form of a Ferranti Mercury computer dubbed ‘Meteor’.

In the decades since, the organization has been through numerous upgrades and reinventions. Even the past 18 years have marked major shifts.

When Ewen joined the Met Office, it was still authoring its own relational databases, he noted, adding that even then it was “an odd thing to be doing”.

The Met Office is now chasing collaboration, agility, scalability, and resilience across all its systems – a process that Ewen described as a significant overhaul.

Chasing resilience

Indeed, uptime and operational resilience were key drivers for the Met Office in partnering with Microsoft and migrating to its cloud supercomputer offering.

One year on from going live on Azure, the Met Office is now recording 99.95% availability for its integrated services, 99.77% supercomputer availability, and 100% critical workload availability.

Erin Chapple, CVP for Commercial Solution Areas at Microsoft, explained that the Met Office is making full use of Azure’s managed services, validated models, robust data workflows, and resilience.

“The platform is delivered with telemetry really built in as the standard and at the scale we operate, it allows us to focus on large-scale identification and resolution of incidents,” Chapple said.

“So the observability and the levels of automation that we have right really have eclipsed what we have been able to deliver in [the] past on premises, leading to identification of issues faster and quicker resolution. This also really enables us to support long running workflows in a consistent manner with minimal or zero impact on those jobs.”

This last point is critical for Met Office operations, Ewen explained, as many of its ongoing scientific research projects last for “ten or more years” and need constant variables to produce usable results.

“So the ability to maintain that direction of travel, to continue to develop pioneering science and understanding, to be able to implement that in next generation codes on new technologies, is vital.”

Ewen added that with the built-in telemetry possible on Azure, the Met Office can now identify and address large-scale incidents at a level not before possible.

“So the observability and the levels of automation that we have really have eclipsed what we have been able to deliver in the past on premises, leading to identification of issues faster and quicker resolution,” he explained.

For digital sovereignty purposes, Microsoft offers workloads based in the UK and provides dedicated protections for data sovereignty.

New frontiers

In February, the Met Office announced it had implemented a new and improved weather model, which would not have been possible without its Microsoft partnership.

The new model allows for more accurate local and global forecasts, driven by improved model physics, a greater range of aircraft data, and an overhaul of the Met Office’s prediction system.

Practically speaking, this means the Met Office is now able to produce more realistic rainfall forecasts, better cloud cover forecasts – hugely important for helping schedule aircraft take-offs, for example – and better UK temperature predictions.

The new model is also able to extend forecasting to 10 days, allowing for better medium-range predictions that feed directly into private sector and military decision-making.

Ewen said the Met Office and Microsoft have now formed a true partnership and that the two will continue to work together on meteorological and climate breakthroughs.

Although the bulk of the organization’s work is rooted in computational physics, Ewen acknowledged that AI is a potential catalyst for innovation. The Met Office will continue to assess the role a hybrid approach to AI and traditional compute can play, he explained, but he cautioned against focusing on it too much.

“People are struggling because they're looking through the narrow lens of, typically, AI at the moment and before that, it was cloud,” he said.

Going forward, Ewen said the Met Office will build on its trusted partnership with Microsoft to maintain its national reputation and deliver groundbreaking research.

“Of course, real world progress is measured in our outputs,” Ewen said.

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Datadog is planning to expand into the UK with the launch of a new data center presence offering local storage for operational data.

According to the firm, the UK data center is expected to open later this year and comes in response to accelerating cloud adoption across regulated industries, along with evolving data governance and security requirements.

Keeping data in-region also allows organizations to reduce latency and use Datadog’s observability and security platform from a single UK-resident environment – crucial for companies operating in regulated environments such as government, banking, healthcare, and higher education, the firm said.

“As more organizations modernize and run critical systems in the cloud and deploy AI, where operational data is stored has become a practical constraint, not just a compliance question,” said Yanbing Li, chief product officer at Datadog.

“For the public sector and highly regulated industries such as financial services and healthcare, storing data locally is critical. The UK data center presence gives customers a way to adopt modern observability and security without compromising in-region data storage.”

Capitalizing on cloud adoption

Cloud adoption continues to accelerate among regulated organizations across the UK, with 82% of financial services firms surveyed by the London Stock Exchange Group (LSEG) now operating in multi-cloud or hybrid environments.

In the public sector, meanwhile, annual digital technology spend exceeds £26 billion, with around 60% of IT systems running on cloud infrastructure, according to GOV.UK figures.

Crucially, companies are having to deal with evolving UK data governance, including changes introduced under the Data (Use and Access) Act 2025, which has increased focus on where operational data is stored and processed.

“The UK is one of the fastest adopters of cloud and AI technologies in Europe. Organizations here are modernizing quickly while facing increasing scrutiny around data governance and security," said Steve Barrett, VP EMEA at Datadog.

"Cloud adoption is now the norm and AI is becoming a second wave on top of it — exponentially increasing operational complexity. Expanding our regional footprint now ensures organizations have trusted, local data storage as they scale cloud and AI securely and reliably.”

The company already operates in North America, Asia and Europe, with plans for a new data center in Australia.

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Rising cloud storage fees are severely impacting IT budgets, new research shows, as UK enterprises ramp up infrastructure spending.

Wasabi’s 2026 Cloud Storage Index report found 48% of storage costs now go toward fees, rather than actual capacity. The findings mark the fourth consecutive year in which IT leaders identified this as a key budgetary strain.

“One of the results of this fee-heavy pricing structure is budget excess,” the company said. Indeed, nearly half (46%) of UK respondents said they exceeded allocated budgets for cloud storage in 2025.

This overspending is typically due to a combination of factors, including increased storage use, growth, and migration. Wasabi noted, however, that fees have been a growing part of the problem in recent years.

“84% of UK respondents cited at least one fee-related reason why their spending on public cloud storage exceeded budget expectations,” the company said.

Cloud storage fees a major pain point

These mounting costs come at a critical time for enterprises, Wasabi said, with many ramping up investment in AI projects and transformation projects.

Storage infrastructure now forms a key underpinning component in AI initiatives, the study noted.

Only one-quarter of respondents said they had recorded a positive return on investment (ROI) with AI, yet nearly half (48%) said they’re confident in recording positive returns if infrastructure improvements can be achieved.

“When asked about the challenges they face implementing AI projects and solutions, the top response was data storage challenges like cost, data access, and management,” Wasabi said. “This highlights the key role data and storage play in a successful AI solution implementation.”

As a result, the majority of UK firms plan to increase spending on this front over the next year. Notably, nearly two-thirds (62%) of budgets will be specifically allocated to data, storage, and compute capacity build-outs.

"As organizations scale AI initiatives, they face mounting data storage and data quality challenges that can quickly erode ROI if not managed effectively," said Dave Friend, founder and CEO at Wasabi Technologies.

"Cost-efficient, reliable storage is essential to ensure high-quality data is readily available for AI models, enabling better outcomes without unsustainable infrastructure costs.”

Changing budget priorities

This surging infrastructure investment marks a shift away from traditional software-focused spending, according to Andrew Smith, director of strategy and market intelligence at Wasabi Technologies and a former IDC analyst.

Just 36% of spending is now allocated to AI-related software or SaaS solutions, highlighting the increased importance of hardware spending as an enabler of AI innovation.

“When we look at revenue allocations at the highest level of the public cloud services market – the vast majority comes from software/SaaS, not infrastructure services (IaaS),” Smith commented.

“What’s fascinating to see within our survey results this year is how most AI budget allocation is going toward infrastructure, not SaaS. In other words, it’s the complete opposite of what we might expect from a traditional market standpoint, and it’s a great illustration of the critical role cloud storage and cloud infrastructure services play in this generational build-up of AI-enabled solutions and services.”

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Cybersecurity vendor Forescout Technologies has partnered with cloud security provider Netskope to expand zero trust security coverage across enterprise networks.

The new integration combines Forescout’s real-time device intelligence with Netskope’s AI, cloud security, and private access controls to deliver zero trust for managed and unmanaged IT, OT, IoT, and IoMT devices.

The pair said the joint solution continuously adapts access decisions based on device posture and risk to ensure zero trust is enforced, regardless of where devices connect.

Unlike traditional zero trust network access (ZTNA) deployments that focus exclusively on north-south cloud traffic, the Forescout-Netskope integration secures east-west communications at the local network level.

In an announcement, Forescout CEO Barry Mainz said the collaboration will help organizations shrink their attack surface as digital environments continue to grow in complexity.

“The volume and variety of device types are exploding, along with the number of applications, users and access points,” he explained.

“By joining forces with Netskope, we are bringing together two best-of-breed solutions, granting customers complete visibility and control over their environments, with policies that automatically adjust as conditions change, and enabling north-south and east-west security policy enforcement.

“This is a gold standard of how ‘Universal’ Zero Trust Network Access is employed in practice, not just as a model.”

Addressing enterprise blind spots

The duo said the move addresses a crucial enterprise challenge in which disparate and disconnected security tools are often managed by siloed teams, creating blind spots and restricting control and policy enforcement.

To overcome this hurdle, the new integration applies granular zero trust policies universally, identifies unmanaged or hidden endpoints, and adapts access decisions in real time based on behavior, device health, and application sensitivity.

Other benefits include improved containment of cyber threats through its east-west local traffic controls, streamlined compliance with frameworks such as HIPAA, NIST, and CIS, as well as automated enforcement to reduce reliance on manual security updates

Sanjay Beri, CEO of Netskope, said a zero trust approach has become essential to secure data and ensure business resilience in the cloud and AI era.

“Our integrated solution with Forescout was designed for the scale, speed, and diversity of today’s modern enterprises, and provides the cohesive, centralized secure access organisations need,” he commented.

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Microsoft has expanded its Sovereign Cloud for AI and regulated environments, as CEO Satya Nadella hypes up the full extent of AI support the firm now offers.

Azure Local disconnected and Microsoft 365 Local disconnected are now generally available, allowing organizations to run critical infrastructure and productivity apps with Azure governance and policy controls, without the need for cloud connections.

Used via Microsoft 365 Local, customers get full control of their data, access, and compliance across the productivity suite, with core server workloads such as Exchange Server, SharePoint Server, and Skype for Business Server supported until at least 2035.

For the first time, qualified customers will also be able to use Foundry Local to run multimodal LLMs inside their on-premises, private cloud environments.

Speaking to assembled media at the Microsoft AI Tour in London, Alistair Speirs, GM for AI Infrastructure at Microsoft, said Foundry now covers “workstation class” infrastructure, including edge and air-gapped hardware, in addition to locally-hosted small language models (SLMs) on IoT devices, desktops, and laptops.

This includes tailored support for racks with AMD and Nvidia chips. Speirs added that there are currently no plans for Foundry Local to support Maia 200, with Microsoft primarily focused on AMD and Nvidia hardware.

“We’ve built the Maia chip so that it’s so focused on Azure it knows exactly where it’s running in the data center,” Speirs said.

He noted that this allows Microsoft’s global infrastructure to leverage an “unprecedented” amount of control over the chip.

Microsoft is doubling down on sovereign cloud

In the event keynote Microsoft CEO Satya Nadella said the firm is committed to sovereignty and aims to meet growing customer demands on this front.

"As you build out the data center, the requirement that now is absolutely front and center for everybody is how to ensure that in every region and every country you have sovereign controls," he said.

Nadella added that sovereign workloads form a “portfolio” for organizations to meet data residency and confidentiality requirements, across Sovereign Public Cloud, Sovereign Private Cloud, and Microsoft’s Sovereign Partner Ecosystem.

Speirs told assembled media that Microsoft is seeing new regulations targeting AI, cybersecurity, or data privacy introduced every four days.

To date, he added, over 1,000 global policy initiatives have been introduced across 69 countries – making compliance very complex.

Microsoft has positioned Azure Local disconnected as a key continuity option for sovereign environments, as well as classified and isolated workloads.

This is because it allows businesses to maintain ownership and control over their core environments, regardless of supply chain disruptions or geopolitical uncertainties.

“If you were to build some sovereign cloud infrastructure five years ago you would have made some assumptions,” said Speirs.

“But policies change, regulations change, geopolitics change. And so the ability to be able to flex and change that infrastructure is something we're really looking to build out so that as customers’ needs change, we help match that infrastructure as well.”

Nadella warned that organizations must ensure increased sovereignty doesn’t itself lead to increased risk and data exposure.

“Cyber resilience is a signals game – cyber resilience, fundamentally, is about having global intelligence to help you with your cyber resilience,”

“And so this is something that you want to think about which is, whenever you have a sovereign operation, how do you ensure that the sovereign operation does not lead to some cyber exposure?”

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Sumo Logic is expanding its EMEA footprint with planned availability of its AI-powered security solutions on the AWS European Sovereign Cloud and Swiss Data Center region.

The move aims to bolster support for European organizations' data privacy, sovereignty, resiliency, and security needs as they push ahead with digital and AI strategies.

Sumo Logic said the expansion will enable businesses to deploy its security analytics and SIEM capabilities while keeping data in-country or within designated time zones as demand for sovereign cloud services grows.

According to research from IDC, 63% of organizations are now more likely to adopt sovereign cloud services due to recent geopolitical events, with spending tipped to reach more than $400 million by 2029.

Despite the growing market, Eric Avery, global head of infrastructure and data at Sumo Logic, said strict data sovereignty regulations are creating barriers for many organizations – particularly across regulated sectors such as healthcare, finance, and the public sector.

“Previously, enterprises in highly regulated industries have been limited in their choice of cloud security solutions while meeting compliance requirements like Switzerland’s FADP,” he explained.

“Sumo Logic's innovations around SIEM and agentic AI, combined with AWS' European Sovereign Cloud infrastructure, provides the ideal response.”

AWS sovereign cloud and Swiss expansion

Under the expansion, Sumo Logic’s Intelligent Security Operations platform will be made available for deployments on the AWS European Sovereign Cloud to help customers operate cloud workloads in an independently governed environment while using Sumo Logic to log, track, and secure deployments.

The security vendor also confirmed plans to roll out its platform in Switzerland in an effort to better support customers that require faster in-country data processing in line with the Swiss Federal Act on Data Protection (FADP), as well as GDPR obligations.

By expanding its footprint to the AWS Swiss Data Center, Sumo Logic said organizations operating within Swiss borders will be able to leverage its agentic AI-driven log analytics and advanced SIEM capabilities to boost compliance, while benefitting from a faster, low-latency environment.

“We deliver reliable security operations that help organizations stay secure and compliant while running services locally with the performance and scale they need,” Avery added.

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The exodus from VMware after its acquisition by Broadcom may not have been the stampede that was predicted, but customers are working to reduce their footprint regardless.

Customers voiced serious concerns about changes to its operating model in the wake of the acquisition, including rises in licensing costs and the removal of a series of popular licenses and products altogether.

Indeed, research from Civo at the time found more than half (51.9%) of VMware customers said they were considering ditching services while 48.7% said they were actively exploring alternative providers.

Fresh research from CloudBolt now shows that while the shift away from VMware wasn't immediate, organizations are actively unwinding dependence in a measured, workload-by-workload process.

"Two years ago, the market was dominated by knee-jerk speculation and worst-case projections,” said CloudBolt CMO Mark Zembal.

“This latest study separates noise and speculation from reality. The fear has cooled, but the pressure hasn’t — and most teams are now making practical moves to build leverage and optionality — even if for some that includes the realization that a portion of their estate never moves off VMware.”

Some are sticking with VMware because things haven't turned out as badly as feared.

In 2024, 73% of customers expected VMware costs to more than double, CloudBolt found, yet only 5% of respondents to this most recent study have seen that happen.

The majority (88%) are still concerned about future price increases, however, and these worries are shaping decisions now as the real squeeze begins. Around 86% reported they are actively reducing their VMware footprint, but progress is slow as this is a time-consuming and laborious process.

“The process of unwinding a decade of process dependencies is taking 18 to 24 months," commented one survey respondent. "This sideways abstraction is far more complex than a standard cloud lift-and-shift, leading to a significant loss of confidence in our ability to exit quickly enough to avoid the next renewal cliff.”

VMware strategies are all over the place

Notably, more than half (56%) of respondents said they’ve changed their VMware strategy more than once since the takeover. Just over half (54%) are staying with VMware while actively reducing dependence in a phased approach.

Nearly three-quarters (72%) of migrated workloads are heading to public cloud IaaS, with Hyper-V/Azure Stack (38%) and SaaS replacements (34%) also popular components of the mix.

“Enterprises aren’t just asking what they want to do — they’re confronting what they can execute safely,” said Rod Squires, CEO of CloudBolt.

“The panic phase is over. Now it’s execution: reducing dependency, managing dual realities during transition, and building optionality before the next renewal decision tightens the window – and slams the budget.”

A number of big tech vendors have capitalized on the disruption over the last two years by offering VMware alternatives or services aimed at assessing their dependency, such as Pure Storage.

Outright alternatives touted by competitors include Proxmox VE, Microsoft Hyper-V, XCP-ng & Citrix Hypervisor, and Red Hat OpenShift Virtualization.

Earlier this week, Cisco threw its name into the mix with a teaser for NFVIS-for-UC, a hypervisor for running its own applications.

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Wasabi Technologies has raised $70 million in funding in a move the company says will propel it into direct competition with hyperscale cloud providers like AWS, Google Cloud, and Microsoft Azure.

The investment round was led by L2 Point Management and included funding pledges from existing investors like Fidelity Management & Research Company as well as flash storage provider Pure Storage.

The funding round values Wasabi at $1.8 billion and marks over $600 million raised by the firm to-date.

In a statement, Wasabi said the funding will enable it to ramp up AI infrastructure expansion plans and broaden its global footprint. David Friend, co-founder and CEO of Wasabi Technologies, said this represents a huge seal of approval for the company.

“We’re ushering in the next generation of cloud storage, powering data-intensive workloads like generative AI and autonomous systems,” Friend commented.

“This funding underscores Wasabi’s strong market position and continued growth as enterprises and AI developers alike seek a better, more predictable alternative to the hyperscalers.”

Founded in 2015, Wasabi launched its flagship “Hot Cloud Storage” service two years later, offering enterprises high-performance storage options for data-intensive applications.

“Hot” cloud storage streamlines access to data sources that are accessed frequently, as opposed to “cold” data which is typically unused. Wasabi says this approach improves efficiency for enterprises, and crucially, reduces overall costs by avoiding egress fees and vendor lock-in.

Wasabi has capitalized on the AI boom

Wasabi’s popularity has soared since the advent of generative AI in late 2022, with enterprises faced with burgeoning workloads and storage bottlenecks.

The firm’s AI product range has grown rapidly on the back of this boom period, first with Wasabi AiR, and more recently with the launch of Wasabi Fire, an NVMe storage option for AI and machine learning training.

Today, the company boasts more than three exabytes of data under management and operates in 16 global regions.

“AI is changing the way enterprises manage and store data. Wasabi is demonstrating strong execution in delivering scalable, low-cost cloud storage built for AI ready data environments without added complexity or unpredictable costs,” said Krishna Gidwani, VP for strategy and corporate development at Pure Storage.

“Our investment reflects a shared focus on building the next generation of AI infrastructure that is intelligent by design and simple to deploy and operate.”

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IBM has launched a new cloud platform designed to help organizations build, deploy, and manage AI-ready sovereign environments.

Unlike other sovereign solutions, IBM said its Sovereign Core makes sovereignty an inherent property of the software itself.

Rather than relying on policy overlays or provider-managed control planes, it uses always-on controls and user-owned control planes.

Similarly, ongoing compliance capabilities are embedded directly into the software, helping organizations to produce regulator-ready proof on demand and without manual, audit-driven processes.

This, the firm said, allows organizations to maintain direct operational authority over software operations, deployment decisions, and system configurations, with no need to involve out-of-region vendors.

All authentication, authorization, encryption keys, and access management remain within jurisdiction boundaries under customer control.

IBM Sovereign Core will be available in tech preview next month, with full general availability planned for the middle of this year.

What to expect with IBM Sovereign Core

According to IBM, Sovereign Core generates, stores and manages comprehensive operational data, system telemetry, and audit trails within the sovereign boundary, including automated identity.

Elsewhere, AI model deployment and hosting, local GPU clusters, local inference execution and agent operations also take place under local governance, without the need to export data to external providers.

"The sovereign AI conversation has focused on data residency, but that's only part of the equation," said Sanjeev Mohan, principal, SanjMo.

"IBM Sovereign Core addresses the harder question: who controls the system and can you prove it to regulators? IBM takes a holistic approach spanning data, operations, technology, and assurance, with continuous monitoring," the company said in a statement.

"As AI moves into production, that kind of ongoing accountability becomes non-negotiable."

Customers can choose to deploy IBM Sovereign Core in the environment of their choice – whether that's an on-premises data center, supported in-region cloud infrastructure, or through IT service providers.

Approved providers under the scheme include Computacenter in Germany, or Cegeka, which operates in Belgium and the Netherlands.

Sovereignty in the spotlight

The move from IBM is the latest in a mass market pivot toward data sovereignty, with a host of major cloud providers now offering these services.

This week, for example, AWS announced the general availability of its dedicated European Sovereign Cloud service, aimed at allowing European customers to keep data in-region.

Enterprise concerns about data sovereignty have been creeping in across the European Union (EU) in recent years, spurred on by new legislation.

Gartner predicts that by 2028, 65% of governments worldwide will introduce some technological sovereignty requirements to improve national infrastructural independence and protect from extraterritorial regulatory interference.

Additional research from the consultancy shows more than three-quarters of all enterprises will have a digital sovereignty strategy by 2030, often based on sovereign cloud.

Long-term, this will be a booming market, according to Gartner. By 2028, the sovereign cloud market is projected to grow by 4.5 times, reaching $169 billion globally.

"Businesses are facing growing pressure to innovate while meeting tightening regulatory requirements and recognizing the importance of controlling how sensitive data and AI workloads are accessed and operated," said Priya Srinivasan, general manager for IBM software products.

"This shift is creating an urgent need for sovereign solutions that deliver AI-ready environments."

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AWS has announced the general availability of its European Sovereign Cloud, touting plans to expand the service across the continent.

The new scheme will offer European-based customers a fully independent cloud located entirely within the EU. This means enterprise data will be hosted and stored solely in the region and kept separate from other AWS Regions worldwide.

The service will be operated out of Germany, according to AWS, and underpinned by more than €7.8 billion in investment.

AWS said it plans to expand options to other European countries as part of the move, with new AWS Local Zones earmarked for Belgium, Portugal, and the Netherlands.

“The AWS European Sovereign Cloud’s unique approach provides the only fully featured, independently operated sovereign cloud backed by strong technical controls, sovereign assurances, and legal protections designed to meet the needs of European governments and enterprises for sensitive data,” the hyperscaler said in a statement.

The move by AWS comes amidst an intense focus on sovereign cloud for European customers. Spurred on by customer demands and regulatory scrutiny, a host of major cloud computing providers have launched - or are in the process of rolling out - sovereign cloud services.

Last year, for example, Microsoft launched a new sovereign cloud scheme covering private and public cloud, along with the launch of dedicated National Partner Clouds in France and Germany.

So what can customers expect from the new AWS Sovereign Cloud project?

What to expect from the AWS European Sovereign Cloud

First and foremost, the new sovereign cloud setup is “physically and logically separate” from other global AWS Regions, enabling European enterprises to meet stringent regulatory requirements on data residency and privacy.

Enterprises operating on the sovereign cloud will have “full control over where their data is stored”, the company said. This includes complete control over all metadata they create – down to roles, permissions, resource labels, and configurations.

Control of sovereign Identity and Access Management (IAM) data, billing, and usage metering systems is also guaranteed.

Crucially, AWS said the new setup places a strong focus on “operational autonomy”, meaning it’s operated “exclusively” by EU residents and has no dependence on non-EU infrastructure.

“Its unique design enables it to continue operations indefinitely,” the hyperscaler explained. “Even in the event of a communications disruption with the rest of the world”.

This aspect of the sovereign cloud scheme is noteworthy, particularly amidst growing concerns about potential US overreach and frosty transatlantic political relationships.

Last year, Microsoft was forced to reassure customers it would take legal action against the US government in the event the Trump administration pressured providers to shut down services in Europe.

The comments from Microsoft President Brad Smith followed the signing of a memorandum in early 2025 to defend US companies from “overseas extortion” in response to the EU Digital Markets Act (DMA).

Reliance on US tech providers has become a key concern for IT leaders in both the UK and EU over the last two years. A study from Civo in May 2025 showed 60% of respondents think the UK government should cut its use of US cloud services over data protection and privacy concerns.

AWS said that in the event of “communications disruption”, AWS employees located in the EU will have “independent access to a replica of the source code needed” to maintain the service.

Security and compliance gains

Elsewhere, AWS said the new sovereign cloud gives enterprises access to “leading technical and compliance controls”. These controls come in the form of the AWS Nitro System, which allows enterprises to enforce strict security boundaries and access restrictions for data held in the region.

“Nobody, including AWS employees, can access customer data running in Amazon EC2,” the company said.

“AWS also provides advanced encryption, key management services, and hardware security modules that customers can use to further protect their content. Encrypted content is rendered useless without the applicable decryption keys.”

The hyperscaler has also launched a new “Sovereignty Reference Framework”, which aims to provide enterprise customers with information and guidance on sovereignty requirements in the region, helping streamline regulatory compliance.

“Customers can use the third-party validated ESC-SRF auditor report to demonstrate clear and enforceable sovereignty assurances,” AWS said.

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Amazon Web Services (AWS) has quietly raised the prices of EC2 Capacity Blocks for machine learning, upping them by around 15%.

First spotted by The Register, the changes mean the cost of a p5e.48xlarge instance has risen from $34.61 to $39.80 per hour across most regions, while the pricing for p5en.48xlarge has gone up from $36.18 to $41.61.

There's an even bigger increase in the US West (N. California) region, where p5e rates have risen from $43.26 to $49.75.

Amazon EC2 Capacity Blocks allow GPU capacity to be booked in advance at a locked-in price, and are mostly used by large organizations with substantial cloud budgets, generally for business-critical ML training.

This gives customers guaranteed GPU capacity at predetermined times, allowing them to reserve capacity for a fixed time window, ranging from one day to several weeks, paying an agreed rate up-front.

The blocks can be reserved up to eight weeks in advance.

"With Amazon EC2 Capacity Blocks for ML, you can reserve just the amount of accelerator capacity you need to run your machine learning workloads. EC2 Capacity Blocks pricing consists of a reservation fee and an operating system fee," AWS explained in promotional materials.

"The reservation fee is charged up front at the time you schedule the reservation. Your Capacity Block is charged at the prevailing rate at the time of purchase, even if the Capacity Block is scheduled to start after the price is updated."

Why has AWS increased prices?

Capacity Blocks have always been subject to variable pricing, based on supply and demand and adjusted every quarter based on expected market conditions.

AWS has previously reduced pricing three times: once in 2024, and twice in 2025.

While demand for compute power for AI and machine learning continues to grow, supply is limited, with a global shortage of advanced GPUs.

The change comes just a few months after AWS cut prices for some GPU instances, particularly for On-Demand and Savings Plans, by up to 45%.

The hyperscaler specifically highlighted price cuts at its annual re:Invent conference in Las Vegas in December.

Capacity Blocks were not included in those reductions, though.

"Regular price reductions on AWS services have been a standard way for AWS to pass on the economic efficiencies gained from our scale back to our customers," the company said at the time.

"These pricing updates reflect the AWS commitment to making advanced GPU computing more accessible while passing cost savings directly to customers."

The next review of pricing is set for April 2026.

A spokesperson for AWS told ITPro: “EC2 Capacity Blocks for ML pricing are dynamic and vary based on supply and demand patterns, as described on the product detail page.

“This price adjustment reflects the supply/demand patterns we expect this quarter. AWS’s commitment to not raise pricing on fixed pricing models like On Demand and Savings Plans remains unchanged.”

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Global spending on cloud infrastructure continues to skyrocket, new research shows, as enterprise demand for AI moves from early tests into full production deployment.

New figures from Omdia show global spending on cloud infrastructure services reached $102.6 billion in the third quarter of this year, up 25% year-on-year and marking the fifth consecutive quarter in which growth has been above 20%.

Amazon Web Services (AWS), Microsoft Azure, and Google Cloud kept the same market rankings as in the previous quarter – collectively accounting for 66% of global cloud infrastructure spending. Together, they saw 29% year-on-year growth.

AWS’s revenue grew by 20% – its strongest performance since 2022 – with a 32% market share. Omdia attributed this growth to an easing of compute supply constraints and incremental demand driven by its partnership with Anthropic.

Microsoft Azure maintained its place as the world’s second-largest cloud provider during the quarter, with a 22% market share and 40% year-on-year revenue growth.

Meanwhile, recorded 36% year-on-year growth and increased its market share to 11%, mostly thanks to enterprise AI offerings.

Backlog levels among leading cloud providers continued to rise, with all three reporting further increases during the third quarter.

AWS, for example, reported a total backlog of $200 billion by the end of the quarter, while Google Cloud saw levels increase to $157.7 billion, up sharply from $108.2 billion in Q2.

Hyperscalers target platform gains

Omdia noted that ss enterprises look beyond AI platforms' model capabilities towards multi-model strategies and agent-based applications, hyperscalers are moving towards platform-level AI capabilities.

AWS, Microsoft Azure, and Google Cloud are all integrating proprietary foundation models alongside an expanding array of third-party and open-weight models.

This approach by the trio is centered around leveraging managed platforms and services such as Amazon Bedrock, Azure AI Foundry, and Vertex AI’s Model Garden to expand support.

“Collaboration across the ecosystem remains critical,” said Rachel Brindley, senior director at Omdia.

“Multi-model support is increasingly viewed as a production requirement rather than a feature, as enterprises seek resilience, cost control, and deployment flexibility across generative AI workloads.”

Real-world impact still a struggle

Notably, many organizations are struggling with real-world deployment, and hyperscalers are stepping up investment in agent build-and-run capabilities, Omdia revealed.

Recent examples of this trend include AWS AgentCore and Microsoft’s Agent Framework, which provide standardized foundational capabilities aimed at helping enterprises more efficiently build, deploy, and operate AI agents in production settings.

“Many enterprises still lack standardized building blocks that can support business continuity, customer experience, and compliance at the same time, which is slowing the real-world deployment of AI agents,” said Yi Zhang, senior analyst at Omdia.

“This is where hyperscalers are increasingly stepping in, using platform-led approaches to make it easier for enterprises to build and run agents in production environments.”

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Google Cloud has announced the first details of its new Partner Network, which is set to formally roll out in the first quarter of 2026.

The cloud giant is positioning the revamped channel initiative as a shift away from tracking program work to recognizing partner contributions across the full customer lifecycle.

Available to all partner types and sizes, the framework introduces a Diamond tier for the highest-performing partners, new competencies, as well as improved automation across the program.

The program will roll out in Q1 2026 and will include a six-month transition window to help partners adjust to the new structure.

In a blog post, Google Cloud said the initiative’s three core pillars – simplicity, outcomes, and automation – will better reward successful co-sale efforts, high-quality service delivery, as well as shared innovation with ISVs.

“Google Cloud Partner Network is being completely streamlined to focus on real-world results,” commented Colleen Kapase, Google Cloud’s VP of channels and partner programs.

“This marks a strategic shift from measuring program work to valuing genuine customer outcomes”

Google Cloud's streamlined framework

According to the tech giant, the new Partner Network has been designed to recognize partners for contributions such as pre-sales influence, co-innovation, and pre-sales support.

There’s also greater recognition for investments in skills, real-world experience, and successful customer outcomes.

On the structure front, the initiative is moving from a two-tier to a three-tier model with the introduction of a new Diamond category to the existing levels of Select and Premier.

This will be reserved for the highest-performing partners that consistently deliver "exceptional customer outcomes” across Google Cloud and Google Workspace, the hyperscaler said.

New competencies on the way

Partners will also see current specializations replaced with a new competency framework as Google Cloud reimagines how it rewards technical and sales capabilities.

New competencies will assess partners on two factors: capacity – measured through certifications and sales credentials – and capability – which will be tracked through validated pre-sales and post-sales contributions.

These competencies will be earned independently of tier status, with an additional Advanced Competency level signaling higher designation.

Increased automation

Across the entire program, Google Cloud is also doubling down on its automation focus.

Building on the introduction of its Earnings Hub and Statement of Work Analyzer tools, the Partner Network Hub will now use AI-driven automation to track partner progress automatically.

Successful customer engagements will be applied across all eligible tiers and competencies to help drive down time-consuming administrative and reporting tasks.

“This radical simplification eliminates redundant reporting and ensures seamless, comprehensive recognition for the outcomes delivered,” Kapase said.

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Palo Alto Networks is set to shift key security workloads to Google Cloud as part of a multi-billion deal that expands the companies' existing partnership.

The security firm and cloud giant said the deal aimed to make it easier for companies to keep systems and data secure as AI is rolled out – a key concern among many in the industry thanks to a rise in AI-related security incidents.

"Every board is asking how to harness AI's power without exposing the business to new threats," said BJ Jenkins, President of Palo Alto Networks. "This partnership answers that question."

"We're removing the friction between security and development, providing a unified platform where the most advanced security is simply a native part of building what's next,” Jenkins added.

“Together with Google, we are embedding our AI-powered security deep into the Google Cloud fabric, turning the platform itself into a proactive defense system."

Moving to Google

The deal will protect live AI workloads on Google Cloud – making it easier for existing customers of both to maintain security policies and help boost Google Cloud adoption, the companies noted.

Part of this will see Palo Alto Networks migrate key internal workloads to Google Cloud, with Palo Alto shifting to Google Cloud's Vertex AI platform and Gemini large language models (LLMs) to power its AI agents.

Part of the deal will involve creating new security services powered by AI, Jenkins told Reuters.

Matt Renner, president and chief revenue officer at Google Cloud, said that the partnership would make life easier for companies that were already working with both providers on application and data security.

"This latest expansion of our partnership will ensure that our joint customers have access to the right solutions to secure their most critical AI infrastructure and develop new AI agents with security built in from the start," Renner said in a statement.

Neither company has publicly confirmed financial details of the deal, but a source told Reuters that Palo Alto would spend an amount "approaching $10 billion" over the next several years.

The companies have long had a tight partnership, with Palo Alto CEO Nikesh Arora formerly working at Google.

Securing AI infrastructure

Palo Alto Networks said its recent State of Cloud report revealed that 99% of respondents had identified at least one attack against their AI infrastructure of the previous year.

The new deal with Google Cloud is an attempt to address that challenge by adding security into multi-cloud infrastructure.

That includes end-to-end security for AI workloads and data on Google Cloud using Palo Alto Networks Prisma AIRS platform. This offers enterprises AI-driven firewalls to secure all types of cloud setups and a secure access service edge (SASE) platform to protect network access for remote workers, mobile devices, and branch offices.

"The deep alignment between the two companies ensures that customer solutions are pre-vetted and engineered to work together, removing the integration challenges and operational friction that can slow down security teams," the companies said in a statement.

"This allows customers to deploy protection faster, simplify compliance and gain a single, comprehensive view of security across their entire hybrid multi-cloud environment."

This is the cloud giant's latest security play. Earlier this year, Google announced plans to acquire Wiz security for $32 billion.

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Cohesity has announced an expansion of its strategic partnership with Google Cloud in a bid to strengthen cyber resilience and drive enterprise AI adoption through new product integrations.

Building on the pair’s existing long-term collaboration, the agreement includes new solutions geared towards helping organizations better protect their data, meet regulatory requirements, and unlock greater value through enterprise AI deployments.

Additions include integrations between Google Cloud’s AI and security offerings and Cohesity’s enterprise AI assistant, Gaia, its Data Cloud platform, as well as Cohesity DataProtect.

“This collaboration with Google Cloud represents a bold step forward in redefining how enterprises protect, secure, and gain insight from their data, while reframing sovereignty from a regulatory burden into a source of trust and advantage,” said Cohesity’s CEO and president, Sanjay Poonen, in an announcement.

AI-driven insights

At the heart of the agreement lies Cohesity’s enterprise AI assistant, Gaia. Building on existing integrations with Google Gemini models, the vendor plans to embed Google Cloud’s Vertex AI Search capabilities into Gaia to enable instant, grounded answers to structured user queries.

The tech giant said this will offer citations pulled from source files stored within Cohesity’s immutable data lake.

There’s also a new integration with Gemini Enterprise in the pipeline, which will allow AI agents built on Google’s platform to securely access historical enterprise data stored within Cohesity’s Data Cloud.

The firm said the integration will enable “seamless connection” with existing workflows.

Additionally, customers can now leverage a Google Cloud-hosted version of the Gaia platform, designed to offer improved performance and response through Google’s global infrastructure.

Security enhancements

On the security front, the Cohesity Data Cloud already incorporates Google Threat Intelligence to help customers detect and respond to threats to their data stores, with Google’s indicators of compromise (IOCs) allowing for both proactive hunting and reactive threat scanning within the Cohesity platform.

The collaboration also pairs Cohesity’s Cyber Event Response Team (CERT) with Google Cloud’s Mandiant Incident Response team to support joint customers in their recovery from potentially destructive cyber attacks.

The pair said they will offer deeper integration between the Cohesity Data Cloud and Google Security Operations to automate security operations workflows and detect threats hidden within backup environments.

A planned cyber resilience SaaS solution will offer cloud-isolated recovery to help reduce recovery times.

Digital sovereignty

The expanded partnership also focuses on data sovereignty as compliance requirements continue to increase in complexity.

Cohesity is certified as a Google Cloud Ready – Regulated & Sovereignty Solutions partner, meaning customers can deploy the firm’s Data Cloud offering within the Google Cloud Data Boundary to comply with data residency and sovereign control requirements.

Cohesity FortKnox on Google Cloud also promises immutable, geographically-restricted cyber vaulting with policy-based controls geared towards meeting sovereignty requirements.

Additional highlights

Elsewhere, the partnership expands data protection capabilities for Google Cloud services such as Compute Engine and Cloud Storage, with additional support planned for GKE, BigQuery, and other data-centric services.

Cohesity and Google Cloud will also collaborate on joint go-to-market programs, co-selling initiatives, as well as integrated marketing campaigns. All components of the Cohesity Data Cloud are available via the Google Cloud Marketplace.

Google Cloud CEO, Thomas Kurian, said the agreement underlines the companies’ shared commitment to helping organizations “unlock the full potential of their data.”

“By combining Google Cloud’s AI and security innovations with Cohesity’s cyber resilience foundations, we’re enabling enterprises to accelerate AI transformation while building a more resilient, compliant future for customers everywhere in the world,” he commented.

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Cloud security teams are scrambling to keep pace with expanding attack surfaces, new research shows, largely due to the rapid adoption of enterprise AI solutions.

In a survey of more than 2,800 security executives and practitioners by Palo Alto Networks, 99% said they had experienced an attack against AI applications and services in the past year.

Meanwhile, the firm warned generative AI-assisted vibe coding is in use by 99% of respondents - but is generating insecure code faster than security teams can review it.

Of the 52% of teams that ship code weekly, only 18% say they can keep up with fixing the vulnerabilities the technology creates.

“As organizations aggressively scale cloud investments to power AI initiatives, they are inadvertently opening the door to sophisticated new attack vectors," said Elad Koren, vice president of product management at the firm's Cortex security platform.

Notably, Palo Alto warned attacks are getting faster, with breaches that took an average of 44 days in 2021 now taking as little as 25 minutes.

"The speed, scale, and sophistication we’ve observed over the past couple of years is incredible," said Haider Pasha, vice president and chief security officer, EMEA, at Palo Alto Networks.

Attackers are increasingly exploiting the foundational layers of the cloud, targeting API infrastructure, identity, and lateral network movement. API attacks, for example, are up by 41%, making them a primary entry point for sophisticated threats.

The top challenges for cloud security teams

Meanwhile, 53% of respondents cited lenient identity and access management (IAM) practices as a top challenge, saying that insufficient access controls are now a leading vector for credential theft and data exfiltration.

These findings align closely with a recent study from Okta, which also highlighted growing concerns about identity security.

An August survey from the firm found 85% of security leaders now view IAM as a critical security focus, marking an increase on the year prior.

Elsewhere, long-running issues with tool sprawl are adding insult to injury for cloud and security practitioners. Disparate tools are creating dangerous blind spots, the company noted, with respondents now managing an average of 17 cloud tools from an array of vendors.

The resulting fragmented data and context gaps are prompting 97% of respondents to prioritize consolidating their cloud security footprint.

SOC teams are struggling

Security operations center (SOC) staff are also struggling amidst a surge in cloud-related attacks, Palo Alto found. A key factor here lies in disjointed workflows and isolated data sources between cloud and SOC teams, the study noted.

This lack of alignment is stalling remediation efforts, with nearly one-third (30%) of respondents revealing they take more than a full day to resolve an incident.

To cope, researchers said cloud and SOC teams must merge, with 89% of organizations believing cloud and application security must be fully integrated with the SOC to be effective.

"Our research confirms that traditional approaches to cloud security are inadequate, leaving security teams to fight machine-speed threats with fragmented tools and slow, manual fix cycles," said Koren.

"Teams need more than just dashboards highlighting risks they can never burn down; they must transform with an agentic-first platform that spans code to cloud to SOC to finally operate faster than the adversary.”

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Enterprises running Nutanix will soon be able to build and operate their own sovereign cloud services following the release of several new features.

New features on the Nutanix Cloud Platform (NCP) will give customers “more choice in how they run and govern infrastructure”, the company said.

The move comes amidst a sharpened enterprise focus on data sovereignty and demand for greater cloud vendor flexibility.

Data sovereignty and vendor flexibility are two overlapping considerations that enterprises are grappling with at present, according to Nutanix. IT leaders face disconnected environments and disparate application estates, often running on sovereign services spanning multiple providers.

“As sovereign cloud architectures become a defining priority for enterprises, we’re introducing several enhancements to the Nutanix Cloud Platform that help customers meet these needs without giving up the advantages of a distributed cloud infrastructure,” said Thomas Cornely, executive VP of product management at Nutanix.

With this in mind, Nutanix wants to cut out the middle man by allowing customers to build their own, reducing complexity and reliance on major providers.

“These new capabilities give customers the clarity and control needed to draw their own sovereign boundaries across distributed environments and leverage the resiliency and flexibility that distributed clouds provide,” Cornely commented.

What to expect from the new Nutanix features

As part of the update, NCP will now offer users “orchestrated lifecycle management” covering “dark-site environments”, meaning they’ll gain greater visibility over distributed cloud environments.

New on-premises deployment options for the Nutanix Central solution, which aims to simplify cloud management, are also rolling out.

Elsewhere, Nutanix Data Lens will also run in on-prem environments. The Data Lens platform aims to bolster unstructured data security, governance, and resilience.

The company said these new options aim to further shore up governance and resilience capabilities.

Nutanix touts new automation tools

A new automation tool, Nutanix Infrastructure Manager, will also be rolling out in the wake of the update. This new service also targets greater environment visibility, the company said, streamlining deployments for users.

Nutanix Infrastructure Manager will give enterprises a single view of virtual local area networks (VLANs), for example, allowing for “centralized visibility and controls" across both on-prem and public cloud environments.

Nutanix said management of Kubernetes and AI environments will also gain a boost through the management tool.

Recovery options in the spotlight

Backup and resilience capabilities are a key focus in the update, according to Nutanix, with new features allowing customers to bolster application security and availability across multiple sites and regions during outages.

These capabilities, the company noted, are “essential for sovereignty-aligned environments” and look to address single-vendor dependencies.

“Teams can now apply sophisticated tiered disaster recovery options that match protection levels to each workload, for additional fault tolerance and cyber recovery resilience,” Nutanix explained.

“New capabilities help ensure business continuity even in the event of up to three site or region failures. Integration of multi-cloud snapshots into the tiered approach ensures an added layer of protection for cyber-resilience objectives.”

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Veeam and HPE have announced an expansion of their strategic partnership in a bid to streamline hybrid cloud operations and minimise risk across the enterprise.

Building on the pair’s initial partnership from earlier this year, the freshly expanded alliance will focus on new integrated solutions to help organizations better protect, recover, and leverage their data.

Additions include protection for HPE Morpheus VM Essentials software, as well as various integrations between HPE products and Veeam’s Data Platform.

“Trust, resilience, and availability are the new currency of business,” commented John Jester, Veeam’s chief revenue officer, in an announcement.

“Our strengthened partnership with HPE gives customers the agility and confidence to protect, recover, and leverage their data, wherever it resides.”

HPE Morpheus plugin

Among the fruits of the partnership is a new Veeam native integration plugin for HPE Morpheus VM Essentials, which will offer hypervisor-based image-level backup for virtual machines (VMs) running on the HPE platform.

Currently in beta with expected general availability scheduled for early 2026, the pair said the addition will provide more robust protection for hybrid workloads.

HPE has also validated HPE Morpheus Enterprise Software container services as a Veeam-ready solution to improve protection for containerized workloads.

Veeam Data Platform integrations

The companies have also announced expanded integration between HPE solutions and the Veeam Data Platform.

Businesses will soon be able to deploy HPE Private Cloud Business Edition together with Veeam as a unified alternative to fragmented, do-it-yourself data protection.

Veeam will provide improved data portability and resilience between VMware and VM Essentials, the pair said, as well as accelerated deployment, streamlined support, and a “dramatically simplified experience.”

By leveraging the latest version of HPE StoreOnce Catalyst, Veeam’s Data Platform can also now achieve up to 60:1 data reduction, while reducing incremental backup limits and boosting restore speeds to help drive down cost of ownership.

Additional highlights

Elsewhere, Veeam has introduced NVMe support for HPE Alletra Storage MP B10000 with new snapshot integrations for speedier backups and near-instant recovery for critical workloads.

New reference architectures designed to deliver end-to-end immutability across the wider Alletra portfolio are “expected soon.”

HPE and Veeam are also partnering on two new joint services: a Data Resilience and Security Posture workshop and a Disaster Recovery Capability Maturity Analysis offering.

The new additions leverage Veeam’s Data Resiliency Maturity Model (DRMM) to help organizations assess and strengthen their cyber resilience strategies.

“Today’s announcement demonstrates the continuous, deep alignment between HPE and Veeam in removing friction and risk from hybrid cloud and modern application environments,” said Patrick Osborne, senior vice president of hybrid cloud technology acceleration at HPE.

“Together, we’re delivering unmatched resiliency, operational simplicity, and innovation for our customers.”

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Welcome back to ITPro's live coverage of AWS re:Invent 2025. It's day-two here in Las Vegas, and we've got another action packed morning ahead of us.

Leading the opening keynote today is Swami Sivasubramanian, Vice President of Agentic AI at AWS. He'll be running us through all the developer-focused service and product announcements, and we're expecting another rapid fire session to buckle up.

CEO Matt Garman kicked things off yesterday with updates across all of AWS' key product lines, including Amazon S3 and Bedrock. Naturally, the key focus was new agentic AI capabilities for customers.

AWS customers will be able to get their hands on powerful new "frontier agents" aimed at streamlining software development practices, as well as the ability to build their own frontier AI models using Amazon Nova Forge.

IT modernization was also another key talking point, again with agentic AI capabilities for AWS Transform.

You can read more about all these announcements below.

• Now anyone can build their own frontier AI model

• AWS CEO Matt Garman says AI agents will have 'as much impact on your business as the internet or cloud'

• Amazon S3 just got a big performance boost

• AWS targets IT modernization gains with new agentic AI features in Transform

• 'Frontier agents' are here, and AWS thinks they're going to transform software development

While most of the big announcements from AWS are yet to be revealed this morning, we have had some big updates rolled out for the company's Transform service. First announced earlier this year, AWS has doubled down on the IT modernization platform with an array of new agentic AI capabilities.

As part of the move, enterprises will be given access to powerful new AI features aimed at supercharging legacy code modernization spanning a range of programming languages, with additional tools for mainframe modernization also announced.

You can read more about the latest update to AWS Transform in our coverage below.

• https://www.itpro.com/technology/artificial-intelligence/aws-targets-it-modernization-gains-with-new-agentic-ai-features-in-transform

It wouldn't be a tech conference without some music absolutely blasting out at 7.30am. The keynote theater is filling up now with a torrent of people making their way through the Venetian Hotel.

We're just a couple of minutes out from Matt Garman's keynote now and the excitement is building. A packed conference hall here.

Matt Garman has taken to the stage, hailing the company significant growth over the last year.

"Amazon Bedrock is now powering AI inference for more than 100,000 companies around the world," he says, giving a shout-out to the AgentCore service launched earlier this year. Huge growth within just a few months and thousands of companies flocking to the agentic AI service.

Infrastructure expansion continues at pace, Garman says. In the last year alone the company has added 3.8 gigawatts of data center capacity, more than any other company on earth.

Networking infrastructure is also expanding rapidly, he adds.

"None of what we do at AWS happens without builders, and specifically developers."

"AWS has always been passionate about developers," he adds. This is a key tenet of the company, and with developers fueling the company's sharp AI focus they've never been more important.

Developers face significant challenges, however. They're spending too much time dealing with bottlenecks. Freeing up time for developers is the key to building successful products, Garman notes.

Naturally, AI is helping solve this problem, and with the advent of AI agents the potential here is huge.

"This change is going to have as much impact on your business as much as the internet or the cloud," he says.

So what do businesses need to drive agentic AI adoption? This will be a multi-pronged approach, according to Garman. Starting from the foundational infrastructure level through to data storage and the tools and solutions needed to build and deploy agents.

Of course, AWS has been focusing heavily on all these fronts.

"You have to have a highly scalable and secure cloud that deliver the absolute best performance for your workloads."

And with that we have our first big announcement here at re:Invent, the launch of AWS AI Factories.

“With this launch, we’re enabling customers to deploy dedicated AI infrastructure for AWS in their own data centers for exclusive use for them," Garman says.

“We give them access to leading AWS infrastructure and servers, including the very latest Trainium and UltraServers.”

We’ve moved onto Trainium now, with Garman touting its potential for inference - a big talking point we expected to see here at re:Invent with the focus on Google’s TPUs growing and the company's recent deal with Anthropic.

“We’ve deployed over one million Trainium chips to date,” he says. And AWS isn’t stopping there. “We’re selling those as fast as we can make them.”

Anthropic is also using Trainium chips for AI training and inference through Project Ranier.

And we have another big product announcement today - Trainium3 UltraServers general availability.

"Trainium3 offer the industry's best price performance for large scale AI," Garman says. Big performance gains here.

4.4x more compute, 3.9 times more memory bandwidth. With UltraServers, customers have 144 Trainium3 chips at their disposal - that's 362 PFLOPS and 706 TB/s bandwidth.

AWS is well along on Trainium4 development, Garman reveals. This upcoming range will offer six-times performance, four-times memory bandwidth and 2x memory capacity compared to Trainium3. Huge performance gains that customers can expect to see next year.

Infrastructure is only one part of the story, Garman says as we move onto explore inference, another key focus for the company through its Bedrock service.

More than 100,000 customers are using Bedrock, according to Garman. But it’s the “volume of the usage that’s astounding”, he says.

“We now have more than 50 customers who’ve processed more than 1 trillion tokens through Bedrock. Incredible scale and momentum.”

So how does Bedrock work? It’s essentially a marketplace for in-house and third-party AI models, with customers able to pick and choose based on their own needs.

“We never believed there was going to be one model to rule them all,” Garman says, and Bedrock certainly shows that. It’s doubled the amount of models hosted on the service in the last year, underlining the varied demand from enterprise customers.

And with that we’ve got another big announcement for Bedrock with four new open weight models, including Nvidia's Nemotron and Google Gemma.

Mistral Large 3 and Ministral 3 are also coming to Bedrock Garman reveals.

AWS has its own in-house models, the Nova range, which is getting a big update with the Nova 2 series.

“Nova has grown to be used by tens of thousands of customers today,” he says.

This includes three separate models - Lite for cost-effective reasoning, Pro, the “most intelligent” model for complex workloads, and Sonic, a multi-modal option. Amazon Nova 2 Omni is also coming soon, Garman reveals. This is a multimodal option which excels in reasoning and image generation, perfect for marketers and creatives.

Nova 2 Pro is a key focus here, particularly given its use in underpinning agents, according to Garman. I get the feeling we’re building towards a big agentic AI announcement.

So, you’re an enterprise and want to build your own AI model - seems simple, right? It’s far from it.

Training from scratch is a time-consuming, expensive process and not a realistic expectation for most enterprises.

Building with open weight models help take the edge off, but you can only go so far Garman says.

“You just don’t have a great way to get a great frontier model,” he says. AWS wants to solve that.

Amazon Nova Forge is a new service that “introduces the concept of open training models”. Essentially, you can build your own frontier AI model by combining internal enterprise data with AWS open weight models picking up the slack.

“We’ve already been working with a few customers to test out Nova Forge,” Garman says. This includes Reddit, which has built its own frontier model using the service.

“We think this idea of open training models is going to completely transform what companies can invent with AI,” Garman adds.

We've officially moved onto agents, one of the "biggest opportunities that are going to change everyone's business," according to Garman. The company has already had moves toward ramping up customer agentic AI adoption.

The Bedrock AgentCore service, launched earlier this year, gives customers access to a range of custom built agents as well as the ability to build and deploy their own.

"AgentCore is truly unique in what it enables for building of agents," he says, adding that flexibility and choice is a key focus of the service.

"You only have to use the building blocks you need. We don't force you as builders to go down a single fixed path."

AgentCore is a source of immense excitement at AWS, it seems.

"The momentum is really accelerating," Garman says, with enterprise customers flocking to the platform at a rapid pace since its launch earlier this year.

Security is a recurring talking point with agentic AI, Garman says. Identity security-related considerations and guardrails are causing headaches for security teams as agents weave their way through data sources behind the scenes.

"You can't with certainty control what your agent does and does not go," he says. Giving enterprises tools to set clear boundaries for agents is critical.

With that in mind, the company announces Policy in AgentCore, which allows users to set strict rules for agents to help them "stay in bounds".

Garman is running us through an example here involving an agent operating in a customer service capacity. Users can set a limit on the size of refunds issued by an agent. Hoping to chance your luck and get a refund on something over $1,000? That won't work.

So now that you've secured your agents, how do you track performance? You've invested a lot of money in these shiny new tools, but are they actually delivering a return on investment or even helping customers?

Another new service unveiled today, AgentCore Evaluations, helps enterprises keep track of agent activities and performance based on real-world interactions with customers.

"Evaluations helps developers continuously inspect the quality of their agent based on real world behavior," Garman explains. "Evaluations can help you analyze agent behavior for specific criteria like correctness, helpfulness, harmfulness."

AWS has been quietly working away building its own range of agents in recent months, Garman says. Amazon Quick, for example, assists users in visualizing and analyzing data or automating workflows.

Employees across the company are using Amazon Quick and recording marked benefits so far.

"Today we already have hundreds of thousands of users inside the company," he says. "Teams are telling us they're completing tasks in one-tenth of the time it used to take."

Amazon is also using agents in customer service roles, with Amazon Connect already being used by a range of major customers such as Toyota, Capital One, or National Australia Bank.

"The Connect business passed the one billion annualized run rate mark," Garman says.

Moving onto developers now, Garman says agents have great potential here to help reduce workloads and speed up operations.

This is where platforms such as AWS Transform come in, helping modernize legacy and mainframe code. Thomson Reuters, for example, is modernizing more than 1.5 million lines of code a month during the process of moving from Windows to Linux.

Elsewhere, solutions like Kiro, AWS' AI coding tool, is helping drive developer productivity.

Kiro is one of a growing array of AI coding tools out there on the market in 2025, but Garman says the reception has been huge, with "hundreds of thousands of developers" using the platform globally since launch.

Kiro has been a huge success internally at Amazon so far. Last week, the company made the decision to make it the official development tool for teams across the company.

With agentic AI gains over the last year, there's huge potential for developers, Garman says, especially with "frontier agents".

These aren't your bog standard AI agents, they're more intuitive, autonomous, and powerful - for developers, the Kiro autonomous agent could be a game changer.

This AI agent essentially acts like "another member of your team", according to Garman, learning from the processes and practices from the team to continually improve.

"We think this will help you move much more quickly," Garman says. The agent is going to help shipping more code, more quickly.

But there's more to software development than just writing code, there are key security considerations at play here. The new AWS Security Agent will help underpin safe, secure software development.

"This agent will help you build applications that are secure from the very beginning," Garman says. "It embeds security expertise upstream and enables you to secure your systems more often."

Security Agent also helps with penetration testing, giving developers teams what was traditionally a laborious process one that's now on-demand. Huge improvements to broader software security here, but also speeding up the development lifecycle.

Want to learn more about the new frontier agents from AWS? Check out our coverage and interview on the announcement below.

• AWS says ‘frontier agents’ are here – and they’re going to transform software development

With faster programming and improved security processes, there's only one part of the equation missing. Deployment of software. With this in mind, the new AWS DevOps Agent helps investigate incidents and proactively work to improve application deployments.

We've still got a bunch of launches expected here. Garman says he's going to rally through the next few announcements - 25 in 10 minutes.

"Buckle up everybody," he says. We're going to cut to the chase here and distill things into simple bullet points covering key areas, starting with Amazon S3.

• S3 object size limits are getting a big boost, a 10x boost to 50TB

• That's not all though, Amazon S3 Batch Operations are also now 10x faster

• Elsewhere, Amazon S3 Tables are also getting new intelligent tiering to help with cost optimization

• General availability of S3 Vectors

Elsewhere, we have some big database announcements.

• Increased storage capacities for RDS for SQL Server and RDS for Oracle are coming

• Optimized CPUs for RDS for SQL Server

• A big new database savings plan for customers - savings of up to 35%, according to Garman

And with that, the keynote session is over. We'll be back shortly with a roundup of everything we learned in the opening session. Thanks for following and remembered to keep tabs on our socials and newsletter for all content from AWS re:Invent across the week.

A steady stream of people entering the keynote theater now with just over 30 minutes until Swami kicks things off on day two.

Swami Sivasubramanian has taken to the stage now, asking the audience to think back to the first programme they built. Feeling rather left out at this point...

Good news though, Swami says "who can build is changing" as a result of generative AI and agentic AI. New AI-powered tools are lowering the bar for non-technical individuals.

So how is an agent different from a generative AI chatbot? Swami says imagine that traffic to a website goes down rapidly in a matter of days.

Using a chatbot, the response would be rather basic, but with an agent you'd get a response with deeper context and a concrete plan to solve the problem.

"The chatbot tells you what to investigate, the agent investigates, diagnoses the problem, and initiates the solution," Swami says.

Agents are the next big thing in the tech industry, and as we've seen already this week, AWS appears all-in. But building agents is a tricky process fraught with challenges and potentially disastrous pitfalls.

AWS wants to make that easier for customers, simplifying the process and enabling developers to build their own agents.

Strands SDK, for example, is helping supercharge developer building capabilities. The service was made generally available in July this year, and has seen great uptake already.

Now AWS is doubling down, offering support for TypeScript, one of the world's most popular programming languages. Elsewhere, Strands SDK will offer support for running agents at the edge - big potential in robotics here, Swami says.

Complexity is the biggest issue in building agents, Swami says. It's a long, difficult process in many cases. Ultimately, this slows down innovation.

Bedrock AgentCore seeks to address this, he adds. The service allows customers to build custom agents. The service has proved highly popular since its launch earlier this year and already boasts thousands of customers, Swami says.

"People love it because it works with any agentic framework and any model, giving you the freedom to use the tools that work best on your use case," he tells attendees.

"It's modular, so that you can mix and match using just the pieces you need for your solution," Swami adds. "AgentCore does the heavy work so you can focus on what matters most, creating these breakthrough experiences that solve real-world business problems."

And with that, we have two new capabilities for AgentCore. The first of these includes AgentCore Policy, which helps enterprises set more robust controls for agent interactions and behaviour.

Elsewhere, AgentCore Evaluations will help users keep tabs on how agents are performing, allowing them to fine-tune and tweak based on end-user feedback.

Another quick-fire announcement here with the launch of episodic memory capabilities for agents through AgentCore.

This, Swami says, will essentially give agents longer-term memory capabilities, helping them to remember and learn from past experiences.

Swami gives the example of air travel here as an area this capability could help. Travelling solo? get an agent to book a flight with a fairly quick turnaround at the airport. But travelling with family six months later, that same booking is going to be a disaster. Further down the line, using episodic memory, the agent would realize this and compensate.

Call me cynical, but this is just common sense. Why do you need an agent to remember a previous airport-related farce?

A tangible example of this being used in a business context would be great.

So far we've discussed how AWS is helping customers to build agents. Now we're moving on to look at making them more efficient.

Most agents are focused on basic functions right now, working away in the background, analyzing search results, writing code, or solving basic problems.

Scaling the capability of agents is the next step, giving them the ability to tackle more complex problems.

"Think of it as teaching your AI agent to be a specialist instead of a generalist. Like turning a family doctor into a cardiologist who is laser-focused on exactly what you need," he says.

Starting with pre-trained models makes sense here, and Swami says this is how most teams begin scaling agent capabilities. These are trained on curated agent-specific datasets.

"This creates permanent behaviour changes," he explains. "They don't require lengthy prompts, can dramatically improve performance on specific tasks."

But there's an issue here. Sometimes models can become too focused on specific tasks due to over training.

So what's the solution? Quality over quantity, essentially. Reinforcement learning, Swami says, helps build stronger, more intuitive agents.

"When an agent is troubleshooting a complex issue, you don't just want a final answer. You want the logical diagnostic steps," he says.

This is easier said than done, though. This is a costly process running between six-to-twelve months and requires deep in-house technical capabilities.

AWS has the solution here, who would've thought? Reinforcement fine-tuning is now available in Bedrock, Swami reveals. This will help markedly improve model accuracy and customization of AI agents.

All told, Swami says this delivers 66% accuracy gains. "That is how powerful these techniques are," he says.

Drilling down into deeper levels of customization, Amazon SageMaker AI is a big focus for AWS and customers. A new model customization feature, launching in preview, allows customers to tweak and fine-tune in-house or third-party models.

"With this release, you can customize popular models such as Amazon Nova, Llama, DeepSeek, and deploy them in a just a few steps," he explains.

"This comes with two experiences, and you can choose the right approach based on your comfort level - a self-guided approach for those who like to be in the driver's seat, and an agent-driven experience that uses an AI expert for folks who like to turn on autopilot in their cars."

Tweaking and fine-tuning AI models using AI agents. We're heading down the rabbit hole here.

We're joined now by Byron Cook, VP, distinguished scientist for automated reasoning at AWS, to discuss "neurosymbolic AI" and how the hyperscaler is working to make AI agents more trustworthy.

LLMs, as we know, can be tricked by bad actors, and that has downstream implications for agents. As a result, we introduce guardrails and essentially keep agents on a short leash.

But that limits their ability to deliver positive gains for users.

AWS uses automated reasoning to verify the outputs of the underlying LLM, which often provide instructions for the agents.

Automated reasoning is being used across a range of areas at the company, including storage and virtualization. Essentially critical areas where "failure is unacceptable".

We heard all about the new frontier agents AWS unveiled during yesterday's opening keynote - powerful new tools for developers spanning code generation, DevOps, and cybersecurity.

These will essentially act as teammates in the future, Sivasubramanian says. It's clear the company is pinning its hopes on a big push into the AI coding space with these new offerings.

They've got stiff competition though. Anthropic, OpenAI, Microsoft, GitHub, Google, and the various 'vibe coding' startups taking the industry by storm mean AWS will have stiff competition.

And that's a wrap on the day-two keynote. We've drilled down into a lot of the developer-focused product launches today, and we'll be sure to hear more as the week continues - tomorrow's keynote session is focusing specifically on infrastructure, a huge talking point in the age of AI.

We'll be back shortly with a comprehensive roundup of all the big talking points from both keynote sessions so far this week, so stay tuned!

Businesses that have adopted a multicloud strategy have a new option that could simplify their cloud deployments – as long as they use Amazon Web Services (AWS) and Google Cloud.

Rob Enns and Robert Kennedy — vice presidents of cloud networking at Google Cloud and network services at AWS, respectively — have published a blog post announcing a new networking collaboration that combines AWS Interconnect – multicloud, and Google Cloud Cross-Cloud Interconnect.

The offering, which hasn’t publicly been given a name of its own, will remove the complexity of ‘do-it-yourself’ multicloud integration and management, the authors said.

“This collaboration also introduces a new open specification for network interoperability, enabling customers to establish private, high-speed connectivity between Google Cloud and AWS with high levels of automation and speed,” they added.

Kennedy said the development “represents a fundamental shift in multicloud connectivity”.

Salesforce, a long-time AWS partner and customer, was revealed as one of the day-one users of the new offering. Jim Ostrognai, senior vice president of software engineering at the SaaS company, lauded the collaboration, saying it “accelerates [its] customers’ ability to ground their AI and analytics in trusted data, regardless of where it resides”.

News of the partnership between the two companies that have more commonly been more rivals than collaborators came one day ahead of Amazon Web Services CEO Andy Jassey’s keynote speech at AWS re:Invent.

The company’s annual conference, which takes place in Las Vegas from 1 to 5 December, is where the company gets to show off its latest products, partnerships, and technological advancements.

The company is typically tight-lipped about what will be announced in the run-up to the event, making the joint blog post with Google Cloud something of a rarity.

To find out what else the company has up its sleeves, check our AWS Re:Invent hub page throughout the week for the latest news as it’s released.

Given we’ll imminently mark the third anniversary of the launch of ChatGPT, a moment which sent the global tech industry into a state of flux, hype, and hyperbole, it’s appropriate that one of the old guard hyperscalers prepares for its annual conference.

AWS re:Invent, hosted at the Venetian Hotel in Las Vegas, officially kicks off next week, offering Amazon’s cloud computing wing an opportunity to get the last word in before the turn of the year.

The oldest and largest of the three major hyperscalers, AWS was caught off guard with the launch of OpenAI’s flagship model alongside Google in late 2022. Both companies spent most of 2023 scrambling to catch up while Microsoft reaped the early rewards of its close-knit relationship with the AI company.

But the gap has been closing. As we learned at Google Cloud Next in April, Google has gained ground over the last 18 months and the recent launch of Gemini 3 stole Microsoft’s thunder while it bombarded customers with agentic AI announcements at its recent Ignite conference.

With its competitors making bold moves in recent months, this begs the question of exactly how AWS plans to wow customers, both current and prospective. AWS’ long-standing reputation as the mature hyperscaler with deep pockets and a focus on easy integration could be the key differentiator moving forward.

Indeed, the company made great strides on this front in mid-2023 with the launch of its Bedrock service, offering customers access to an array of third-party and in-house models.

As ITPro noted at the time, this showcased AWS’ ability to cut to the chase and give enterprise customers a clear route to generative AI adoption – with the flexibility of choice – while others focused heavily on pushing a rather monolithic approach to the technology.

With the agentic AI frenzy taking hold throughout 2025, the company has further built on this initial foundation, expanding Bedrock in July this year with the launch of Amazon Bedrock AgentCore and the AI Agents and Tools service in AWS Marketplace.

The former allows customers to build and deploy agents, while the latter offers them the chance to pick and choose from an array of options delivered through AWS partners.

That flexibility of choice has been a hit with AWS customers so far, and we can expect to see more on this at re:Invent 2025.

A hardware battle is brewing

On the hardware front, it’s clear there’s a battle brewing in the AI chip domain, with industry stakeholders touting Google’s growing hardware strength centered on its tensor processing units (TPUs) as a potential challenge to Nvidia’s AI dominance.

For AWS, we can also expect to see a big push at the company’s annual conference. AWS has been ramping up development of its own in-house AI chips in recent years, spurred on by the acquisition of Annapurna Labs in 2015.

This focus on hardware initially saw the company unveil its Inferentia chip in 2018, followed by the launch of Trainium in 2022. Both of these have been used to underpin training and development of foundation models through the company’s partnership with Anthropic, for example.

With the enterprise focus now shifting from training to inference, AWS will be keen to tout the potential here for customers. The Trainium2 chip was unveiled by the hyperscaler at re:Invent 2023, boasting 4x faster training and 2x energy efficiency capabilities.

Last year’s conference, meanwhile, saw the company tease plans for Trainium3, again with 4x performance improvements and 40% gains on energy efficiency.

During Amazon’s third quarter earnings results this year, CEO Andy Jassy confirmed the launch of Project Rainier, a cluster of around half a million Trainium2 processors being used to train Anthropic models, with plans to increase capacity to around one million by the end of this year.

In an announcement in late October, AWS’ head architect for Trainium, Ron Diamant, described the project as “one of AWS’ most ambitious undertakings to date”. It’s safe to assume the company will be keen to provide an update here.

The Anthropic tug of war

While Microsoft has its poster child in OpenAI, AWS has been betting big on Anthropic. The hyperscaler invested $4 billion in the AI startup back in September 2023 and doubled down on its support for the company just a year later.

The latter of these pledges saw Anthropic choose AWS as its primary cloud provider and training partner.

AWS isn’t the only hyperscaler with eyes for Anthropic, however. Google pledged $2 billion in 2023, following up with an additional $1 billion in January this year.

The relationship here is complicated, and it’s clear Anthropic isn’t keen on settling for a one-stop-shop approach to infrastructure partnerships. October, for example, saw Google Cloud and Anthropic forge even closer ties, with the latter expanding its use of TPUs to drive compute capacity for training upcoming Claude models.

Anthropic said the deal, which will see it gain access to around one million TPUs, was secured specifically due to their “price-performance and efficiency” benefits.

AWS will be keen to react in the wake of this deal between the two, and while there’s nothing to suggest the trio can’t co-exist, with a Trainium3 announcement expected the hyperscaler will be betting big on its own cost and energy efficiency credentials to one-up Google.

With a chip war looming and enterprises continuing to push for a return on investment with AI, AWS will be relying on its sprawling industry ties and long-standing infrastructure credentials to show it can still combine scale and agility at re:Invent.

ITPro will be live on the ground at AWS re:Invent in Las Vegas from 1st to 5th December. Keep tabs on all our coverage via our live blog, social channels, and newsletter.

Amazon Web Services (AWS) is planning to invest up to $50 billion in purpose-built AI and high-performance computing (HPC) infrastructure for the US government.

The data center build, set to start next year, will see an extra 1.3 gigawatts of compute capacity added across AWS Top Secret, AWS Secret, and AWS GovCloud (US) regions across all classification levels: Unclassified, Secret and Top Secret.

Federal agencies will gain expanded access to AWS's AI services, including Amazon SageMaker AI for model training and customization, Amazon Bedrock for model and agent deployment, Amazon Nova, Anthropic Claude, and leading open weight foundation models, with the use of AWS Trainium AI chips as well as Nvidia AI infrastructure.

"Our investment in purpose-built government AI and cloud infrastructure will fundamentally transform how federal agencies leverage supercomputing,” said AWS CEO Matt Garman.

“We're giving agencies expanded access to advanced AI capabilities that will enable them to accelerate critical missions from cybersecurity to drug discovery. This investment removes the technology barriers that have held government back and further positions America to lead in the AI era."

The investment, the company said, will speed up discovery and decision-making across applications ranging from national security to scientific research and innovation, including autonomous systems development, cybersecurity, energy innovation, and healthcare research.

AWS said the move is aimed at supporting the priorities laid out in the US government's recently-published AI Action Plan, which promotes the development of AI infrastructure, as well as other advanced computing initiatives deployed on secure, US-based AI and cloud infrastructure.

AWS boasts deep government ties

The hyperscaler is already a major cloud provider to the US government, serving more than 11,000 government agencies including US intelligence communities, the Department of Defense, federal agencies and defense industrial companies.

In 2011, it became the first cloud provider to build infrastructure specifically for government security and compliance requirements with the launch of AWS GovCloud (US-West).

Since then, it's launched AWS Top Secret-East, the first air-gapped commercial cloud accredited to support classified workloads; AWS Secret Region, making it the first cloud provider accredited across all US government data classifications; and AWS GovCloud (US-East), AWS Top Secret-West, and AWS Secret-West regions.

"Federal customers and the supporting industrial base share a vision of AI and HPC convergence. This includes orchestrating expert AI models, agents, and natural language interfaces to enable researchers and engineers to explore complex problems through conversational interaction," said Amazon.

"This represents a fundamental shift from traditional HPC workflows to AI-accelerated discovery, where scientists can specify challenges and receive AI-driven recommendations backed by high-fidelity simulations and analysis."

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Dell Technologies argues that there's no longer a debate between public cloud or private – instead, the focus is on using all of the available options and finding ways to manage that complexity.

"The conversation around enterprise IT is changing," said Caitlin Gordon, Vice President of Multicloud Product Management for Dell Technologies in a blog post. "It’s no longer about choosing between public cloud and on-premises infrastructure."

"Nor is it a simple decision between running traditional or modern workloads. Today, the real challenge is how to manage all of these different environments and application types together, simply and consistently."

To help, Dell is expanding its existing partnership with Microsoft – which already offers AX System for Azure Local, later adding PowerFlex to the mix – and will offer support for Dell Private Cloud and PowerStore on Azure Local, Microsoft's system for running Azure services on in-house data center infrastructure.

"Microsoft and Dell Technologies share a vision of empowering businesses to achieve more through innovation and collaboration," said Dean Paron, VP of product management for Azure Edge Infrastructure at Microsoft.

"By bringing Microsoft Azure Local to Dell Private Cloud and PowerStore, we’re helping customers simplify their IT operations and unlock the full potential of their hybrid cloud strategies," he added.

The partnership comes amid a rising debate over cloud repatriation – the idea of moving workloads from the cloud back to on-premise infrastructure to help reduce costs, increase control, or improve flexibility.

While firms like Basecamp and Ahrefs have saved millions making such a move, hybrid remains the most popular option at 68% of companies, according to one survey.

Private Cloud on Azure

Gordon said the partnership delivered a first for private setups on Azure, and this latest announcement shows the duo want to take things further.

"Dell Private Cloud is the first Azure Local offering… a full-stack solution across compute, external storage, and networking, delivered from a single vendor, with end-to-end solution-level support included," Gordon said.

With Dell Private Cloud, Azure Local users will get automated lifecycle management, with end-to-end automation to help reduce complexity, as well as independent scaling, allowing compute and storage to be managed separately to reduce costs.

Plus, the system offers a disaggregated approach – as Dell puts it – that gives businesses a future-ready, adaptable system.

"This combination of Dell Private Cloud and Dell PowerStore delivers the simplicity, flexibility, and performance customers need to manage both traditional and modern workloads across their IT estate, Gordon said.

PowerStore for flash storage

The addition of PowerStore, Dell's enterprise-grade, all-flash storage solution, will give flexible scalability with built-in data protection, Gordon added.

"Always-on data reduction lowers storage costs without impacting performance, backed by the industry’s best 5:1 DRR (Disaster Risk Reduction) guarantee," Gordon added.

Early access for Azure Local with Dell Private Cloud and PowerStore will begin in Spring 2026, the company confirmed.

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Most UK IT leaders believe their organizations aren’t doing enough to meet their own sustainability targets, new research shows.

In a recent study from IT service management firm Flexera, 93% said sustainability is increasing in priority, yet 83% admit their organization needs to step up.

A confluence of issues are hampering success on this front, however. Rising cloud computing costs are straining budgets, cited by 69% of respondents, while nearly half (46%) said they feel overwhelmed by the volume of cost and usage data they receive.

These surging data volume means sustainability teams are swamped, preventing them from identifying key focus areas.

“Without full visibility of what their technology estate is costing, consuming and emitting, businesses cannot evidence clearly whether cloud and AI investments are advancing or undermining their climate goals,” said Marlon Oliver, senior vice president EMEA at Flexera.

Access to sustainability data is improving, the researchers found, with 79% of IT decisionmakers saying it’s easy to access such data within their organizations. However, sustainability goals often appear to be grouped with regulatory compliance, which is seen as a lower priority than AI integration, cost reduction and risk mitigation.

Flexera recommends integrating sustainability into technology choices, evaluating cloud migration, SaaS adoption, and AI investments through both cost and sustainability lenses.

Similarly, sustainability metrics should be used alongside financial and operational KPIs to guide investments and measure impact.

Always keep sustainability in mind

Crucially, the study noted that sustainability must be taken into account during IT modernization projects. Researchers said these considerations should be prioritized alongside areas such as security and data protection.

Collaboration across IT, procurement, and sustainability teams should also be encouraged, with clear ownership and incentives for sustainable outcomes.

"Closing the gap between sustainability ambition and execution requires integrating sustainability into every facet of IT —from strategy and procurement to daily operations," said Mark Bradley, senior manager, product management, at Flexera.

"By leveraging robust data, fostering cross-functional collaboration and embedding sustainability into decision-making, IT leaders can drive both environmental and business value."

The research comes as the UK prepares for new Sustainability Reporting Standards, expected to apply for accounting periods beginning in 2026.

The standards, which are aligned with the ISSB framework, will require large and listed firms to report more transparently on climate-related risks and impacts, including greenhouse gas emissions and energy use.

For many organizations, that will mean accounting for the footprint of technology operations, such as data centers and cloud infrastructure, where these are a material source of emissions.

“Boards are expected to show climate progress in ways that can be proven against reporting standards," said Oliver

"Right now, IT leaders are facing rising bills and data that do not add up to a clear story. And until that changes, organisations will find it difficult to deliver the results that regulators, and their own stakeholders, now expect."

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