MongoDB Atlas Vector Search
Free tierBuild intelligent applications powered by semantic search and generative AI with a native, full-featured vector database.
Free tier available·All audiences·Powered by Voyage AI (Automated Embeddings); any LLM via RAG·API available
Key strengths
Unified operational and vector data in a single platform — no sync overheadHybrid search combining vector, lexical, geospatial, and metadata filteringAutomated Embeddings powered by Voyage AI — no ML expertise requiredIndependent scaling of vector search via dedicated Search NodesEnterprise-grade security, high availability, and multi-cloud support
Free tier + paid plans
New York, USA
Founded 2007
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- RAG pipelines: Implement Retrieval-Augmented Generation by indexing document embeddings in Atlas and retrieving top-k candidates via
$vectorSearchbefore passing context to an LLM. - Hybrid search systems: Combine ANN vector search with BM25 lexical search and metadata filters in a single aggregation pipeline for high-precision retrieval.
- Multitenant SaaS search: Use Flat Indexes with tenant-level filters to serve isolated vector search namespaces within a shared Atlas cluster.
- Agentic memory & context retrieval: Store and query vector embeddings representing agent state, tool outputs, or conversation history for long-context AI agents.
- Real-time recommendation engines: Execute vector similarity queries fused with aggregation pipeline logic (joins, scoring, geospatial filters) for low-latency personalization.
- Automated embedding pipelines: Use Voyage AI Automated Embeddings to eliminate custom ETL/embedding sync infrastructure, keeping embeddings consistent with source data automatically.