MongoDB Atlas

Unlock the AI Advantage

MongoDB Atlas securely unifies operational and vector data to streamline building AI-enriched applications.
Get StartedVector Search Quick Start guide
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TRUSTED BY
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Okta
Cisco
Oneai
Financial Times
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Context is the key to unlocking generative AI

Large language models (LLMs) are rich in general knowledge but lack the context to deliver a competitive advantage. Retrieval-augmented generation (RAG) bridges this gap. With vector search built into MongoDB Atlas, you can store and search operational data alongside vectors, enabling RAG to deliver accurate, relevant results grounded in your own data.Build a RAG chatbot demo
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Unified interface for retrieval and memory

MongoDB Atlas combines a flexible document model with text, vector, and hybrid search, going beyond RAG to deliver short- and long-term memory for AI agents.Learn more about AI Agents
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Enterprise-grade infrastructure for modern AI apps

With vector and operational data managed together in Atlas, you get a unified, secure foundation for AI applications, backed by the same enterprise-grade performance, availability, scalability, and security MongoDB is known for. Leverage our documentation, code samples, and case studies to streamline your AI development process.Read the case studies
MONGODB ATLAS IN THE WILD
“Atlas Vector Search was the answer to our problems. It simplifies a lot of the work that goes into making Okta Inbox super user-friendly for customers.”
Suchit Agarwal
Director of Engineering, Okta
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Ready to
start building?

Accelerate AI development and unlock new opportunities with MongoDB.
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Vector Search Quick Start guide

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