What is a vector database?

In the enterprise world in 2026, vector databases are increasingly under discussion in the same breath as semantic search, retrieval-augmented generation (RAG), and other ‘AI-ready’ ways of finding and using information inside an organization.

The basic driver is straightforward: teams want systems that can retrieve results based on meaning, and similarity, rather than relying solely on exact keywords, rigid schemas, or purely structured querying.

To understand the sector, ITPro spoke to Devin Pratt, research director at IDC and Noel Yuhanna, VP and principal analyst at Forrester, about what a vector database actually is, where it fits compared to other enterprise options, and what organizations should weigh up.

Vector databases have gone from a specialist concept to a practical talking point for IT teams in fairly rapid fashion, largely because so many modern AI projects now depend on finding the right information quickly and reliably.

What is a vector database for?

At its simplest, a vector database is a system designed to make “similarity” a first-class way of searching and retrieving data.

Instead of treating a query as a strict match, it returns items that are closest to what you asked for, based on how the underlying information is represented, making it particularly relevant for organizations dealing with large volumes of unstructured data – documents, support tickets, product catalogues, and so on – where the intent of a question matters.

“A vector database is designed to search by semantic meaning rather than exact matches, making it fundamentally different from relational databases, key-value stores, or search engines,” Yuhanna explains.

“Vector databases are built from the ground up to perform fast, large-scale similarity search using specialized indexing and vector embeddings,” he says. “It makes them especially suited for AI use cases like semantic search, RAG, and modern AI applications.”

How do vector databases work?

In practical terms, vector databases rest on the idea that content can be represented in a form that makes “closeness” measurable, so a system can return results that are similar to a query, even when the wording is different.

This setup is what enables meaning-based retrieval across messy, unstructured information, where a keyword search might miss useful context, or return too much noise.

The database’s job, then, is not just to store these representations, but to retrieve the nearest matches quickly enough to be useful in real applications. A major reason vector databases can do this at scale is indexing. As Pratt notes, vector databases “implement advanced indexing techniques like HNSW, PQ, or DiskANN to optimize similarity searches.”

The details vary by product, and that is where performance and cost trade-offs appear.

Yuhanna points out that “different products use a variety of indexing methods… which can have significant implications for query speed, accuracy, and efficiency depending on the workload.”

What can you use a vector database for?

Most organizations look at vector databases when they need better retrieval over unstructured information. The common goal is to help people and systems find the most relevant material even when the query does not share the same wording as the source.

In that sense, vector databases are often less about 'adding AI' in the abstract and more about making search, discovery, and information access work properly at scale.

A related capability is combining similarity search with more traditional constraints, such as filtering by department, document type, customer segment, date range, or permissions.

Yuhanna argues that “The ability to perform hybrid searches that combine vector similarity with non-vector data, such as master data or metadata, will become essential for most enterprise use cases.”

Vector databases are often compared to relational databases, key–value stores, and traditional search engines, but the more useful comparison is usually between a purpose-built vector database and a general-purpose platform with vector indexing.

In other words, the decision is not always “vector database or nothing” – it is whether vector search is central enough to justify adopting a dedicated system, or whether you can meet your needs within the database and operational model you already run.

Yuhanna is clear that a general-purpose approach can be sensible in the right circumstances: “A general-purpose database with a vector index is sufficient when vector search is secondary, data volumes are moderate, or the application needs to combine vector search with non-vector data to provide a broader context.”

Native or integrated vector databases?

In 2026, most organizations will encounter vector search in two broad forms.

The first is a purpose-built, native vector database, where similarity search is the core design goal. The second is an integrated approach, where vector indexing and query capabilities are added into a general-purpose platform, often to align data, governance, and operations.

In practice, the right choice tends to depend less on ideology and more on what you are building, as well as how central vector search is to the application.

Pratt draws a clear line between the two options, noting that in a native approach “the vector store is purpose-built and optimized specifically for managing high-dimensional vectors,” while integrated platforms are increasingly common because teams want consolidation.

“Integrated vector databases are the clear platform of choice for storing and querying vector embeddings, as organizations favor unified, governed environments for agentic AI,” he adds.

More broadly, Pratt explains that vector search is increasingly paired with RAG as an embedded function within mainstream database architectures.

Pratt also makes the trade-off more concrete. Integrated options can “embed vector stores within a broader framework that combines traditional structured data management with vector processing capabilities”.

Vector databases: Pros and cons

In simple terms, the case for vector databases is strongest when an organization needs more effective retrieval across unstructured information, and when “similarity” is a better fit than strict matching.

Implementing this can unlock experiences where search feels less brittle, and where applications can pull back the most relevant context even when users do not phrase a question in the same way the source content was written.

In that sense, vector databases are often less about replacing existing systems and more about adding a capability that traditional services struggle with.

However, the trade-offs tend to appear after deployment, when systems move from a proof of concept to a service that has to meet performance, security, and operational expectations.

“The biggest risks organizations face after deployment typically include performance bottlenecks at scale, declining search relevance, insufficient data security controls, operational complexity, skills shortages, and a lack of clarity about what vector databases can deliver,” Yuhanna warns.

From Pratt’s perspective, governance concerns are also front of mind.

“Security vulnerabilities (26%) and data privacy breaches (24%) are the leading concerns when deploying agentic AI,” he says, which makes it important to treat vector search as an enterprise data problem, not just an experiment

How to choose and deploy safely

Choosing a vector database is rarely a matter of picking the “best” product in the abstract. Yuhanna urges leaders to filter by “security controls, governance capabilities, TCO, hybrid search, scalability, performance, customization options, and search relevance”.

He also cautions against over-simplifying the category: “Common misconceptions include assuming that all vector databases scale equally, that all can perform vector and non-vector searches in a single query, or that they all use the same vector indexing algorithm.”

Leaders keen to implement vector databases can also fall into the trap of focusing too much on the technology and not enough on assessing their specific organizational needs.

Pratt acknowledges this and warns that vector capability is easy to over-simplify during procurement: “The most misunderstood point is treating ‘vector support’ as a checkbox rather than validating fit across governance needs, indexing requirements, and performance at target scale.”

In practice, reducing risk means treating deployment as an ongoing operational discipline, rather than a one-off build.

“Companies mitigate these risks by implementing stronger security controls, continuously monitoring vector performance, managing the lifecycle of vector data, selecting appropriate indexing strategies, and focusing on specific, relevant use cases that vectors can support,” Yuhanna adds.

Vector databases in 2026 and beyond

Over the next year and beyond, the most important change is likely to be less about a single breakthrough feature and more about convergence.

As mainstream database platforms add vector capabilities, and more teams try to run vector search alongside existing operational and analytical workloads, the category is starting to look less like a standalone niche and more like an expected part of the broader data stack.

Techniques such as database sharding will be used to partition datasets across nodes, for the faster, distributed similarity searches relied upon by tools such as AI agents.

Both experts predict a convergence of native and integrated vector databases, with the core motivation being better support for AI deployment. But even as the market converges, Yuhanna argues the practical success factors will look familiar to anyone who has put a database into production.

“Enterprise-grade security, governance, and compliance, along with platforms that can scale automatically and reliably, will be critical to succeed.”