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Both are production-grade vector stores; the right pick depends on what you already run and how much volume you expect.pgvector lives inside Postgres 1pgvector — open-source vector similarity search for Postgresgithub.comAdds a `vector` column type and IVFFlat / HNSW indexes to Postgres for cosine, L2, and inner-product similarity., so embeddings sit alongside your relational data — joins, transactions, and a single backup story all keep working. It is excellent up to roughly 10 million vectors per index on a tuned instance 2Supabase blog — Choosing the right vector index for pgvectorsupabase.comBenchmarks IVFFlat vs HNSW at 1M, 5M, and 10M vectors on a 4 vCPU / 16 GB instance.; past that, the IVFFlat / HNSW indexing gets memory-hungry and Pinecone's purpose-built shards start winning 3Pinecone scaling guide — vectors per pod and serverless tiersdocs.pinecone.ioPod and serverless tiers, target QPS, and recall guarantees for 10M to 1B vectors..Pinecone removes operations entirely: managed cluster, automatic scaling, hybrid (sparse + dense) search 4Pinecone hybrid search overviewdocs.pinecone.ioCombines sparse BM25-style and dense vector signals in one query for higher recall on lexical matches.. The trade-off is a separate data plane — you keep the embedding *and* its metadata in two places, and you pay both the storage and the egress cost.For most AI products under ~5M vectors, start with pgvector. The day your search latencies pass 200ms p95 or your indexing cost outweighs your Postgres bill, migrate; the data shape is identical so the cutover is mostly an operational decision, not a re-design.
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