Vector Database
Last updated March 25, 2026
A vector database stores and searches high-dimensional embeddings for AI applications like semantic search and RAG.
Vector databases store numerical representations of data that enable similarity search for AI applications. They power features like semantic code search, RAG systems, and AI-powered recommendations. Supabase includes vector capabilities, and dedicated vector databases like Pinecone and Weaviate serve AI-heavy applications.
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Frequently Asked Questions
Why do AI apps need vector databases?
Vector databases enable semantic search — finding results by meaning rather than keywords. This powers RAG, code search, and AI recommendation systems.
Which tools include vector database?
Supabase includes pgvector for PostgreSQL. Dedicated options include Pinecone, Weaviate, Qdrant, and ChromaDB.
Do I need a vector database for my project?
Only if you are building AI features like semantic search, RAG, or similarity-based recommendations. Standard applications do not need vector storage.