Originally published on AI Tech Connect.
What you need to know This is a tuning guide, not a selection guide. Which database to run is a separate question — our vector database selection guide covers that. Here we assume the database is chosen and the job is making its index behave. Every index choice is a trade on one triangle: recall, latency and memory. You cannot maximise all three at once; you buy one with another. Tuning is deciding where on that triangle you want to sit. HNSW is the graph-based default. Three knobs — M, efConstruction and efSearch — set the balance. Higher values buy recall and pay in memory, build time and query latency respectively. IVF and product quantisation are the memory levers. IVF partitions the space so you search only part of it; PQ compresses vectors up to roughly 97% at a measurable recall…
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