# Vector database

**Source:** https://promtable.com/glossary/vector-database

> A vector database stores embeddings and performs approximate nearest-neighbor search at scale, the persistence layer behind RAG and semantic search.

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A vector database stores embeddings and performs approximate nearest-neighbor search at scale, the persistence layer behind RAG and semantic search.

A vector database (or vector index) indexes high-dimensional embedding vectors using algorithms like HNSW, IVF, or ScaNN so you can find the top-k most similar vectors to a query in milliseconds even across millions of records. Managed options include Pinecone, Qdrant Cloud, Weaviate, Vespa, and Turbopuffer. Open-source self-hosted: Qdrant, Weaviate, Milvus, LanceDB. Postgres extensions like pgvector make it possible to keep vectors next to relational data. Choose based on filter-and-search needs, hybrid (vector + BM25) support, and operational profile.

## When to use

- Any production RAG system.
- Semantic search at >100k document scale.
- Personalization features driven by embedding similarity.

## Common mistakes

- Picking a managed DB before validating the embedding model — DB swaps are cheap, embedding swaps are not.
- Forgetting hybrid (vector + keyword) — pure vector search misses exact-match queries.

## Related terms

- [embeddings](https://promtable.com/glossary/embeddings)
- [rag](https://promtable.com/glossary/rag)
- [semantic-search](https://promtable.com/glossary/semantic-search)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/vector-database
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