Choosing a vector database for enterprise AI

What a vector database actually does

A vector database stores embeddings, which are lists of numbers that represent the meaning of a passage, alongside the original text and metadata. At query time it finds the stored vectors closest to the question's embedding.

That sounds like a niche capability, but in practice the hard parts of enterprise retrieval are familiar database problems: filtering, access control, freshness and operations.

The three main options

Most teams end up choosing between three families. Each can work well; the right one depends on what you already run and what your data looks like.

• Postgres with pgvector: vectors live next to your relational data, with transactions, joins and familiar tooling.
• Dedicated vector stores: purpose-built for large vector collections, with managed scaling and fast approximate search.
• Search engines with vector support: strong keyword search, faceting and relevance tuning combined with semantic similarity.

Questions that decide it

Benchmarks of raw query speed rarely decide enterprise projects. These questions usually do.

• Permissions: can you filter results by user, team or tenant efficiently inside the query?
• Hybrid search: do you need exact matches on part numbers, names and acronyms alongside semantic matches?
• Freshness: how quickly must an edited document be searchable, and how will you re-index?
• Scale: are you indexing thousands of documents or hundreds of millions of chunks?
• Operations: who will back it up, monitor it and patch it, and is that team already stretched?

A sensible default

If your organization already runs Postgres well, pgvector is a strong first choice. You keep one system of record, enforce permissions with ordinary SQL, and avoid a new operational dependency. Many internal knowledge bases fit comfortably in this model.

Move to a dedicated store or search engine when you have evidence: collection sizes that strain your database, latency targets you cannot meet, or relevance needs such as faceting and custom ranking that a search engine handles natively.

Whatever you choose, keep the embedding pipeline separate from the store. That lets you switch databases or embedding models later without rewriting ingestion.

Validate with your own data

Build a test set of 50 to 100 real questions with known source documents. Measure how often the correct passage appears in the top results for each candidate setup. This retrieval hit rate predicts answer quality far better than vendor benchmarks.

Retrieval is only one part of a RAG system. For the bigger picture, read RAG vs. fine-tuning for enterprise data, and see how retrieval fits into a request flow in LLM orchestration patterns for production.

Our enterprise AI integration services include this evaluation as part of discovery, before any infrastructure is committed.