TLDR Tech / Data
The Postgres Developer's Guide to Vector Index Tradeoffs
Vector search in Postgres becomes an index-design problem once tables reach millions of vectors, filters enter the query path, and recall/latency tradeoffs start affecting product quality. Exact search is best for small datasets and recall baselines. HNSW is the default read-heavy ANN choice when data fits in memory, IVFFlat reduces memory and maintenance costs at the expense of more tuning, and StreamingDiskANN via pgvectorscale targets large indexes that outgrow RAM. Hybrid search with BM25 plus vectors in Postgres improves recall by combining semantic matching with keyword relevance.
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