Topic hub · 7 claims
Vector databases — storing and searching embeddings at scale
Databases optimized for similarity search over dense vector embeddings. The retrieval backbone of every production RAG pipeline.
Why dedicated vector DBs
Standard databases (Postgres, MySQL, MongoDB) handle exact-match + range queries. Vector queries are different: given a 1536-dimensional query vector, return the K nearest neighbors by cosine similarity from a corpus of millions of vectors, in <100ms. The data structures (HNSW, IVF, PQ) and tuning trade-offs are non-trivial. Dedicated vector DBs ship those primitives.
The four main options
FAISS (Facebook AI 2017) is a library, not a database — fastest, no service to run, embed in your app. Pinecone (founded 2019) is the managed-cloud leader — easiest production deployment, costs scale with index size. Weaviate, Qdrant, and Milvus are open-source + managed-cloud — Qdrant is the easiest local + production option for most teams. Chroma is the simplest dev-loop option (single-file SQLite-backed).
The Postgres option
pgvector — a Postgres extension — has matured enough by 2025 that for teams already on Postgres, adding pgvector beats adding a separate vector DB. Trade-off: pgvector's similarity-search performance lags purpose-built vector DBs at >10M vectors, but is competitive below that threshold.
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Defined terms (3)
- Vector database
- A database optimized for storing and similarity-searching high-dimensional vector embeddings. Foundational to RAG retrieval at scale.
- HNSW
- Hierarchical Navigable Small World — the dominant approximate-nearest-neighbor algorithm. Used by FAISS, Pinecone, Weaviate, Qdrant, pgvector.
- pgvector
- Postgres extension that adds vector storage + similarity search. Lets teams already on Postgres avoid a separate vector DB.
All claims in this topic (7)
- Chroma vector database·publicly released on 2023-02-14 by Chroma Inc.(1.00 · 2 sources)
- FAISS·introduced in Johnson, Douze, Jégou 2017 — Facebook AI Similarity Search(1.00 · 2 sources)
- Milvus vector database·publicly released on 2019-10-15 by Zilliz(1.00 · 2 sources)
- pgvector·publicly released on 2021-04-20 by Andrew Kane — Postgres vector extension(1.00 · 2 sources)
- Pinecone·founded in 2019(0.90 · 2 sources)
- Qdrant·founded in 2021(0.85 · 2 sources)
- Weaviate·founded in 2019(0.85 · 2 sources)