# Vector Databases for RAG: A Practical Comparison

> How to pick a vector DB for RAG: indexes, filters, hybrid search, re‑ranking, and an evaluation checklist.

Published: 2026-03-01  
Canonical: https://aimastermindlab.com/knowledge-base/vector-databases-for-rag

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Vector databases differ by developer ergonomics and operational features more than raw recall.

Compare: index choices (HNSW/IVF/PQ), metadata filters, built‑in hybrid search, re‑rank integration, multi‑tenancy, and observability.

Start simple: HNSW first; add PQ only under memory pressure. Keep embedding dims consistent; rebuild after model changes.

Evaluate: run a golden set per size tier (10k/100k/1M) and record Hit@k and p95/p99; watch costs.

Decision tree: strict filters → pick strong filtering first; minimal ops → managed offering; heavy customization → self‑host open‑source.
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Source: https://aimastermindlab.com/knowledge-base/vector-databases-for-rag — AI Mastermind Lab (https://aimastermindlab.com)