MongoDB: The World’s Leading Modern Database | MongoDB
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MongoDB: The World’s Leading Modern Database | MongoDB

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Get your ideas to market faster with a flexible, AI-ready database. MongoDB makes working with data easy.

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Industry EventBuild your AI future on MongoDB. Join us at re:Invent, Dec 1 - 5 in Las Vegas. Find out more >Build your AI future on MongoDB. Join us at re:Invent, Dec 1 - 5 in Las Vegas. >>\
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AnnouncementLearn why MongoDB was named a Leader in the 2025 Gartner® Magic Quadrant™ Learn more >Learn why MongoDB was named a Leader in the 2025 Gartner® Magic Quadrant™ >>\
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MongoDB EventJoin us at MongoDB.local San Francisco on Jan 15 to see how you can ship your AI vision faster. Use WEB50 to save 50% >Join us at MongoDB.local San Francisco on Jan 15 to see how you can ship your AI vision faster. Use >>

MONGODB ATLAS

The database for dynamic, demanding software

AI depends on fluid, instantly accessible data, not fragmented silos. Consolidate structured and unstructured data—text, video, audio, time series, and more—into a single, flexible system.

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Atlas Learning Hub

Discover tutorials, guides, and videos to master MongoDB Atlas and its features.

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MongoDB Atlas

The modern database for any use case

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Vector Search

Stream Processing

Operational

Transactional

Text Search

Analytical

Graph

Geospatial

Vector Search Use Cases

MongoDB Atlas integrates operational and vector databases in a single, unified platform. Use vector representations of your data to perform semantic search, build recommendation engines, design Q&A systems, detect anomalies, or provide context for gen AI Apps.

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Documentation

Stream Processing Use Cases

Build scalable event-driven applications that react and respond in near real-time. Atlas Stream Processing unifies the developer experience, enabling you to work with high-velocity data streams from sources like Apache Kafka using the same familiar MongoDB Aggregation Pipeline stages you use for your database.

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Tutorial

Operational Use Cases

Optimize write performance with a document data model that maps to your application’s access patterns. Meet a wide range of query requirements via a single query API that supports everything from simple lookups to complex processing pipelines for data analytics and transformations.

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Documentation

Transactional Use Cases

Guarantee millisecond response times at scale with a flexible document data model and rich query capabilities—including secondary indexing, joins, multi-document ACID transactions and more.

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Documentation

Search Use Cases

Combine three systems—database, search engine, and sync mechanisms—into one and deliver 30-50% faster. Build catalog and content search, in-app search, and single view into your application with MongoDB Atlas Search.

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Documentation

Analytical Use Cases

Unify the core capabilities needed for application-driven analytics with MongoDB Atlas. Perform powerful aggregations and transformations in-place and in real time. Leverage optimized indexes, storage, data formats and an extensive ecosystem of native and integrated analytics services to build smarter applications and achieve real-time business visibility.

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Documentation

Graph Use Cases

Elevate your applications with MongoDB’s native graph data support. Traverse relationships across your collections to uncover patterns, enable intelligent graph-based reasoning, and build powerful solutions like recommendation engines, fraud detection, network management, and knowledge graphs.

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Documentation

Geospatial Use Cases

Easily build applications that leverage geospatial data with MongoDB's native support for GeoJSON and simple coordinate pairs. Harness specialized indexes for blazing-fast queries. A one-stop solution for logistics, location-based services, and spatial analysis.

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Documentation

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200databases migrated to Atlas in 4 months

240%improvement in API performance

Retail

“MongoDB and everything that comes with it was great. On MongoDB, we could automate our deployments and scalability monitoring, and we had advanced features like search charts and an online vector store that didn’t exist in the CouchDB ecosystem.”

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