Vector Database

DEFINITION

A specialized database designed to store, index, and query high-dimensional vector embeddings efficiently. Vector databases power semantic search, RAG pipelines, and similarity-based recommendation systems.

A vector database stores embeddings — high-dimensional numerical representations of data — and supports approximate nearest-neighbor (ANN) search: given a query embedding, find the database entries with the most similar embeddings.

Popular options: Pinecone (managed, scalable), Supabase pgvector (PostgreSQL-integrated), Weaviate (open-source), Qdrant (Rust-based, self-hosted), Chroma (lightweight, good for prototyping). Choose based on scale, hosting preference, and whether you need SQL-compatible queries alongside vector search.

Tools That Use Vector Database

Supabase

9.1/10

Open-source Firebase alternative with pgvector for AI apps

Free / $25/mo ProView Review →

Pinecone

8.7/10

Managed vector database built for production AI search

Free / Serverless from $0.04/1M readsView Review →

Related Terms

Embeddings

Numerical vector representations of text, images, or other data that capture semantic meaning. Embeddings allow AI systems to measure similarity between concepts by comparing positions in high-dimensional vector space.

Retrieval-Augmented Generation

A technique that enhances LLM outputs by retrieving relevant information from an external knowledge base before generating a response, combining generative power with accurate, up-to-date data.

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