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Vector DBFreemium
MONGODB
General-purpose document database with native vector search
SSPL-1.0
ABOUT
Teams building AI applications with RAG or semantic search often need a vector database alongside their primary data store, adding operational overhead from syncing data between systems. MongoDB Atlas integrates native vector search directly into the document database, letting developers store embeddings alongside business data and run vector similarity queries with the same API, indexes, transactions, and operational tooling they already use.
INSTALL
brew tap mongodb/brew
brew install mongodb-community
INTEGRATION GUIDE
1. Build RAG pipelines that run vector similarity search alongside document queries within a single database API
2. Implement semantic product search and recommendation systems using embeddings stored with existing product data
3. Power AI agents with hybrid search across structured data and vector embeddings using MongoDB Atlas's unified query API
TAGS
databasenosqldocument-storevector-searchsemantic-searchembeddingsatlascloud-database