Glossary · AI & Agents
pgvector
Overview
What pgvector Is
pgvector is an open-source extension for PostgreSQL that adds a native vector data type plus similarity-search operators. It lets you store AI embeddings—numeric representations of text, images, or other data—right alongside your regular tables and run nearest-neighbor queries with standard SQL. In other words, it turns the Postgres database you may already run into a capable vector store.
How It Works
You create a column of type vector, insert embeddings generated by a model, and query with distance operators to find the closest matches. pgvector supports exact search and approximate indexes (IVFFlat and HNSW) to keep queries fast as data grows. And because it's just Postgres, you can combine vector search with normal filters, joins, and transactions.
Why It Matters for Business
pgvector lets teams add semantic search and RAG without adopting a separate vector database, reducing cost and operational overhead. When we build RAG development and knowledge-assistant projects on a Postgres stack, pgvector often keeps the architecture simple—one database to back up, secure, and scale—while a managed service like Pinecone makes more sense at very large scale.
Where we use it
Related Zen in Tech services
How our team puts pgvector to work in real projects.
FAQ
pgvector — common questions
Is pgvector free?
Yes. pgvector is open-source and free under the PostgreSQL license. Many managed Postgres providers—including AWS RDS, Supabase, and others—support it, so you can enable it without extra software costs beyond your existing database hosting.
pgvector vs Pinecone—what's the difference?
pgvector keeps vectors inside your existing PostgreSQL database, which is simpler and cheaper for small-to-medium datasets. Pinecone is a dedicated, fully managed vector database that scales to billions of vectors with less tuning. The right choice depends on scale and operational preferences.
How many vectors can pgvector handle?
pgvector comfortably handles millions of vectors with HNSW indexing on well-provisioned hardware. Beyond that, or under very high query volume, a purpose-built vector database may perform better, but many production apps run entirely on pgvector.
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