Enjoy the freedom and flexibility of using MongoDB Atlas as a vector database to store and search vectors alongside your operational data.
MongoDB Atlas. A vector database, and much more
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Native vector database features
Vector embeddings are numeric representations of data and related context. MongoDB Atlas unifies vector embeddings with live application data in a single, fully managed, multi-cloud database that handles transactional, search and retrieval, in-app analytics, geospatial, and streaming workload needs.
Make LLMs smarter with RAG
Retrieval augmented generation (RAG) gives large language models (LLMs) access to live, up-to-date data, filling the gaps in knowledge that LLMs aren't trained on. RAG enables you to build hyper-personalized experiences uniquely tailored to business needs using your own enterprise data.
Reduce complexity, increase productivity
Niche technologies lead to fragmented and inefficient developer experiences. Instead of bolting on a standalone vector database, Atlas gives you all the features you need to build gen AI-powered applications while reducing sprawl, complexity, and overhead for developers, all in a single platform.
Workload isolation for scalability and availability
Set up dedicated infrastructure for MongoDB Vector Search on Atlas workloads. Optimize compute resources to scale search and database independently, delivering better performance at scale with higher availability.
Cloud flexibility and AI ecosystem integration
Some AI-enabled applications require specialized ML infrastructure from a particular cloud or model provider. MongoDB Atlas uniquely offers global, multi-cloud database clusters on all the major cloud providers, and supports embeddings generated by the vast majority of model providers.
Level Up Your MongoDB Skills
MongoDB Skill Badges are focused credentials that help you quickly learn how to use MongoDB in 60 to 90 minutes.
GEN AI CASE STUDY
“As the world’s most widely used natural language ingestion and preprocessing platform, partnering with MongoDB was a natural choice for us. This collaboration allows for even faster development of intelligent applications. Together, we're paving the way businesses harness their data.”
Brian Raymond
Founder/CEO, Unstructured.io
Founder/CEO, Unstructured.io
GEN AI CASE STUDY
“As the world’s most widely used natural language ingestion and preprocessing platform, partnering with MongoDB was a natural choice for us. This collaboration allows for even faster development of intelligent applications. Together, we're paving the way businesses harness their data.”
Brian Raymond
Founder/CEO, Unstructured.io
Founder/CEO, Unstructured.io
GEN AI CASE STUDY
“We use the sentences stored in MongoDB to train our models and support real-time inference. The flexibility of its document data model made MongoDB an ideal fit to store the diversity of structured and unstructured content and features our ML models translate.”
Himanshu Sharma
Co-Founder/CEO, Devnagri
Co-Founder/CEO, Devnagri
GEN AI CASE STUDY
“In the ever-changing AI tech market, MongoDB is our stable anchor … my developers are free to create with AI while being able to sleep at night.”
Orr Mendelson
Head of R&D, WINN.AI
Head of R&D, WINN.AI
GEN AI CASE STUDY
“We are still sticking with MongoDB because it is evolving with the technologies. It’s providing all the solutions we need.”
Pratham Rasal
Senior Software Engineer, Mercor
Senior Software Engineer, Mercor
GEN AI CASE STUDY
“For any infrastructure the best outcome is that the engineering team doesn't have to worry about it because it just runs. You focus on the innovation with the models because you don't have to worry about the data layer—and the data layer is MongoDB.”
Alex Holt
Field CTO, ElevenLabs
Field CTO, ElevenLabs
Ready to get started?
Start leveraging RAG, LLMs, and your own private data to build transformative AI-powered applications using native vector database features in MongoDB Atlas.
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