Vector Database

Vector databases store and index embeddings, enabling fast nearest-neighbor search across millions or billions of vectors. They're the storage layer underneath most RAG systems.

Leading options in 2026 include Pinecone, Weaviate, Qdrant, and pgvector (a Postgres extension). For most small-to-medium teams, pgvector is enough — running it inside your existing Postgres avoids operational overhead. Larger scale, latency-sensitive workloads benefit from purpose-built vector databases.

The practical considerations operators care about: query latency at your data size, hybrid search support (combining vector and keyword queries), cost scaling, and integration with your existing stack. Pure vector search is rarely the right answer alone — hybrid is.