Vector Stores: Pinecone, Qdrant, pgvector, Chroma, FAISS
Comparing managed and self-hosted stores, HNSW and IVF index types, running Qdrant in Docker and pgvector in Postgres, when to choose what.
Run Qdrant in Docker and pgvector locally. Load 1000 vectors into each, measure top-5 query latency and compare. Create an HNSW index in pgvector and re-measure — how much faster did it get?
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Help me choose a vector store. Volume: <number of vectors>, dimension: <…> Infrastructure: <do you have Postgres / k8s / managed-only> Requirements: <privacy, filtering, budget, latency> Compare Pinecone / Qdrant / pgvector / Chroma along these axes and give a recommendation with index type (HNSW/IVF) and parameters.
- Using FAISS as a "database" and being surprised it has no filters or networking — it's an index library.
- Forgetting to create an HNSW index in pgvector — search does a seq scan and crawls on large tables.
- The index metric (cosine) doesn't match how the embeddings are normalized.
- Choosing Pinecone for private data without accounting for data leaving to the vendor.