Which vector database is best for enterprise RAG?

For most enterprise workloads the honest answer is that the choice matters less than teams expect, and the deciding factors are rarely retrieval performance. Operational fit, whether it can run inside your boundary, and whether it supports efficient filtering by the requesting user's permissions will affect the outcome far more than benchmark differences.

Do we need a dedicated vector database at all?

Frequently not. If you already run PostgreSQL, pgvector avoids a new system to operate, secure, back up and staff, and it is sufficient for a large share of enterprise corpora. Adding a specialised store is worth it when scale, latency or hybrid search requirements genuinely exceed what your existing database does well, and that threshold is higher than most first designs assume.

What should decide the shortlist?

Whether it can run inside the boundary your compliance position requires, and whether it supports efficient metadata filtering so retrieval can apply the requesting user's permissions at query time. A store that cannot do permission filtering well pushes you toward post-filtering, which degrades answer quality and means restricted content passed through your application.

How much does retrieval performance vary between them?

Less than the difference made by chunking strategy, embedding model choice and query construction. Teams routinely change vector store hoping to fix retrieval quality when the actual problem is upstream. Measure retrieval quality separately from generation before concluding the store is at fault.

Managed service or self-hosted?

It is the same question as for the model. If your compliance position requires data to stay inside a boundary you control, a managed service outside it is not available to you regardless of its merits. If not, managed removes real operational burden and is usually the better default.

Choosing a Vector Database for Enterprise RAG

Vector database selection absorbs a disproportionate amount of design time, and it is rarely where an enterprise RAG system succeeds or fails.

The differences between the main options are real but narrower than the positioning suggests, and they matter less than three things that are usually decided later: where the store is allowed to run, whether it can filter by the requesting user's permissions, and who operates it.

Start with the constraint, not the benchmark

If your compliance position requires that data stays inside a boundary you control, then a managed service outside that boundary is not on the shortlist regardless of how well it performs. That single question eliminates options faster than any evaluation, and it is worth answering first rather than after a proof of concept.

The same applies to region and residency requirements. Establish what you are actually obliged to do before comparing features, because half the comparison may be moot.

The axis nobody puts in the comparison table

Can it filter efficiently by permission at query time?

Enterprise retrieval has a requirement most vector database comparisons ignore: the system must only surface documents the person asking is entitled to read. That means carrying the user's identity into the query and filtering against current access rights, not filtering after results come back.

Stores differ meaningfully in how well they support high-cardinality metadata filtering at query time. A store that handles it poorly pushes you toward post-filtering, which has two consequences: answer quality degrades because the model may be left with too little context, and restricted content has already passed through your application layer, which may itself be reportable.

This should shape the shortlist before performance does. It is also the defect most commonly found at review, because it passes every functional test.

The honest comparison

| Axis | What actually matters | |---|---| | Deployment | Can it run inside your required boundary? This eliminates options first. | | Permission filtering | Efficient metadata filtering at query time, not post-filtering. | | Operational burden | Another system to secure, patch, back up and staff, or an extension of one you already run. | | Hybrid search | Combining keyword and vector matters more for enterprise corpora full of identifiers and jargon than pure semantic benchmarks suggest. | | Scale characteristics | Behaviour as the corpus grows and as filters get selective, which is not the same as peak throughput. | | Existing estate | If you already run PostgreSQL well, pgvector inherits your backup, HA and access-control practice. |

Why the default should be boring

If you already operate PostgreSQL competently, start with pgvector and make the specialised store earn its place. You inherit backup, high availability, access control, monitoring and the operational knowledge of a team that already runs it, and you avoid adding a system somebody has to be on call for.

For a large share of enterprise corpora that is sufficient. Corpora in these projects are usually smaller than teams expect: a few hundred thousand chunks is common, not billions.

Move to a specialised store when you have measured a specific requirement your existing database does not meet, rather than in anticipation of one.

When the specialised stores earn their place

Genuine scale, where corpus size or query volume exceeds what your existing database handles comfortably. Latency requirements that survive a real measurement rather than an assumption. Hybrid search or reranking features you have confirmed you need. Or a managed offering that removes operational burden you cannot staff, assuming your compliance position allows it.

All of those are legitimate. What is not legitimate is choosing a specialised store because retrieval quality is poor, when the actual cause is chunking, embedding choice or query construction.

The mistake worth avoiding

Teams change vector store hoping to fix retrieval quality far more often than the store is responsible for it.

Before concluding the store is at fault, measure retrieval separately from generation: build a set of questions with the documents that should be returned for each, and check whether the right documents come back at all. If retrieval is returning the wrong context, a different store will return the wrong context faster.

Where to go next

RAG at scale covers the surrounding architecture, and permission-aware retrieval covers the filtering requirement in detail, including how to test for it. For the broader picture see AI architecture.