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Running pgvector in production on Amazon Aurora PostgreSQL
A comprehensive operational guide for running pgvector on Amazon Aurora PostgreSQL in production. Covers choosing between HNSW and IVFFlat indexes (or no index at all for small/partitioned datasets), configuring distance operators (cosine vs inner product), scaling to millions of vectors with quantization and partitioning, managing HNSW index churn via REINDEX CONCURRENTLY or partition-based rebuilds, capacity planning for memory-resident HNSW graphs, and observability using pg_stat_statements, CloudWatch metrics, and custom recall tracking. Includes concrete SQL examples, recommended parameter values (m=16, ef_construction=128), and a two-stage binary quantization retrieval pattern for large datasets.