pgvector

pgvector is an open-source PostgreSQL extension first released in 2021 that adds native vector data types and similarity search to the world's most popular relational database. With pgvector installed, developers can store embeddings in regular PostgreSQL columns, build HNSW or IVFFlat indexes, and run vector similarity queries alongside SQL joins, transactions, and existing application logic. This unification eliminates the operational overhead of maintaining a separate vector database for many small to medium-scale RAG workloads, and it inherits PostgreSQL's mature replication, backup, security, and AI compliance ecosystem. pgvector supports cosine, Euclidean, and inner product distance metrics with optional binary and half-precision storage to reduce memory footprint. Major cloud providers including AWS RDS, Azure Database for PostgreSQL, and Google Cloud SQL all offer managed pgvector. AI governance teams favor pgvector when they want vector search inside their existing governed database rather than introducing a new system with its own access controls, encryption, and audit log requirements.

pgvector + Centralpoint for on-premise governance: Centralpoint supports pgvector as a fully on-premise vector backend that lives inside your existing PostgreSQL footprint, alongside any LLM in the model-agnostic stack. Prompts stay local, tokens are metered, and chatbots backed by pgvector embed across portals with one line of JavaScript and full audit logs.


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