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DirectoryAI & ML PlatformsVector Databases & RAGpgvector

pgvector

Open Source

About pgvector

pgvector provides open-source vector similarity search for Postgres. It allows vectors to be stored alongside other data in a Postgres database. It supports exact and approximate nearest neighbor search. It supports single-precision, half-precision, binary, and sparse vector types. It supports multiple distance metrics for comparing vectors. It can be used from any language that has a Postgres client.

Used for

  • Storing vectors with the rest of application data
  • Finding the nearest neighbors to a vector
  • Finding the nearest neighbors to a row
  • Getting rows within a certain distance
  • Scaling to large numbers of vectors using quantization

From pgvector's own website, read 2026-10-24. How we build listings.

How to evaluate Vector Databases & RAG

This is how the CIOPages Research Team evaluates this category. It is not an assessment of pgvector. It comes from our Vector Database & AI Search buyer guide.

25%
Retrieval Quality & Index Options
ANN algorithms offered (HNSW, IVF/IVF-PQ, DiskANN, GPU/CAGRA), achievable recall@k at your latency budget, quantization for memory savings, and whether the index supports real-time inserts or needs rebuilds
20%
Hybrid Search & Filtering at Scale
Native vector+keyword (BM25 / sparse) fusion, reranking, and — critically — in-graph metadata pre-filtering that holds recall under selective filters rather than degrading to brute force or empty result sets
20%
Scale, Performance & Freshness
Vector ceiling per node and horizontally, p95/p99 latency and QPS under your real filters, multi-tenancy and namespaces, multi-region, and how fast new or updated embeddings become queryable (insert-to-searchable lag)
15%
Operating Model & Lock-In
Managed SaaS vs. self-hosted vs. bring-your-own-cloud, license terms (Apache 2.0 / BSD vs. proprietary), data-export and migration path, and how much sync/ETL you take on if vectors live apart from the source data
10%
Security, Governance & Residency
RBAC and API-key scoping, encryption in transit and at rest, SOC 2 / ISO 27001 / GDPR posture, data residency and VPC/air-gapped options, and inherited governance (e.g. Unity Catalog, platform IAM) where applicable
10%
Developer Experience & Ecosystem
Client SDKs and API ergonomics, LangChain / LlamaIndex / framework integrations, built-in or hosted embedding and reranking, docs quality, and operational maturity (backups, observability, upgrades)

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Quick Facts

github.com/pgvector/pgvector
CategoryAI & ML Platforms
SubcategoryVector Databases & RAG
DeploymentSelf-hosted

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