Cloud infrastructure spending hit $102.6 billion in Q3 2025 – and AWS marked its strongest performance in three years

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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