AI Vector Databases & RAG Infrastructure
Power semantic search and retrieval-augmented generation (RAG) apps with a database built for AI embeddings. Store and query millions of vectors fast — the infrastructure layer behind AI applications that need to search documents, memories, or knowledge bases by meaning, not just keywords.
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AI Vector Databases & RAG Infrastructure
When you want an AI to answer questions based on your own documents — your product documentation, internal knowledge base, past support tickets — the standard approach is called retrieval-augmented generation (RAG). The documents get converted into numerical representations called embeddings and stored in a vector database, which can then find the most relevant chunks when a user asks a question.
What vector databases do that regular databases can't
Traditional databases search by exact match — a document either contains the word "refund" or it doesn't. Vector databases search by meaning — they can find documents about returns, cancellations, and money-back guarantees when someone asks about "getting my money back," even if the exact phrase never appears.
Choosing the right one for your project
- Pinecone — fully managed, easiest to get started with.
- Weaviate and Qdrant — self-hostable for teams that want data control.
- Chroma — lightweight, popular for local development and prototyping.
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