Vector Databases Compared: Picking the Right Store for Your RAG Pipeline

Every vector database claims to be the fastest at approximate nearest neighbour search, and at the scale most companies actually operate at, the difference between them on that dimension will never be the thing that determines whether your RAG pipeline works. The decision that actually matters is closer to your existing infrastructure and operational maturity than to a benchmark leaderboard.

The questions worth asking instead

• Do you need hybrid search — dense vector plus keyword/BM25 — because pure semantic search misses exact-match queries (part numbers, error codes, names) that your users actually type?
• Can it run alongside your existing data, or does it require a separate system your team now has to operate, monitor and back up?
• How does it handle metadata filtering at scale — most real RAG queries need "search within this tenant, this document type, this date range," not pure open-ended similarity search.
• What is the re-indexing story when your embedding model changes? This happens more often than teams plan for, and some stores make it painful.

We use Qdrant as a default for most client builds — not because it wins every synthetic benchmark, but because its filtering model, self-hosting story, and operational simplicity match the constraints most of our clients actually have: a small team, a need for tenant isolation, and no appetite for operating a bespoke search cluster.

Postgres with pgvector is underrated

If your data already lives in Postgres and your scale is in the low millions of vectors, pgvector removes an entire system from your stack — no separate service to operate, back up, and secure, and your retrieval query can join directly against your existing relational data. Teams reach for a dedicated vector database by default when the honest answer, at their actual scale, is "you already have one."