What it means
An embedding turns a piece of text into a long list of numbers positioned so that similar meanings land near each other. "How do I reset my password?" and "I forgot my login" end up close together despite sharing almost no words. A vector database stores those numbers and answers one question very efficiently: what is nearest to this?
That is semantic search, and it is the retrieval half of RAG. Keyword search fails when the user's words differ from the document's words; embedding search handles that naturally. In exchange it can miss exact matches — product codes, names, precise identifiers — which is why serious systems usually combine both, an approach called hybrid search.
Why it matters
Vector storage went from a specialist tool to standard infrastructure very quickly, because every RAG system needs it. Notably, it no longer requires adopting a new database: the major general-purpose databases have added vector support, so for most teams this is a feature to switch on rather than a system to procure.
In practice
For most applications the pragmatic starting point is vector support in the database you already run. A dedicated vector database earns its place at large scale or with demanding latency requirements — not by default.
Where this shows up
Tools and models in our catalog.
PineconeThe leading managed vector database for AI applications. Serverless pricing, 99.99% SLA, and billions of vectors at millisecond query speeds. Widely used in production RAG systems.
Supabase VectorPostgreSQL-based vector storage using the pgvector extension. Seamlessly combines traditional relational data with vector search in a single database.
Elastic AI SearchESRE (Elasticsearch Relevance Engine) for retrieval-augmented generation. Hybrid vector + keyword search combining traditional full-text with semantic vector search in a single platform.
NeonServerless Postgres. Scale-to-zero, database branching, PostgreSQL 18, Neon Auth. 100 free projects. Rust Data API for performance.