LAUNCHMongoDB 8.3 is built for the sub-100ms retrieval & zero downtime AI demands. Read blog >

AI DATAStop fighting your data layer. Get the memory & retrieval agents need to scale. Read blog >

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Build trustworthy AI applications faster with an AI-ready data platform that unifies your operational data and retrieval capabilities.

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One platform for any AI use case

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AI Retrieval

Vector Database

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AI retrieval use cases

Build faster with a coherent architecture that unifies your database query language, text search, and vector search. This integrated reliability ensures your mission-critical apps aren't broken by external API rate limits or third-party downtime.

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Vector search use cases

Store operational data and vector embeddings together in the same document. This native integration allows you to perform semantic similarity searches that are always grounded in your most current, governed enterprise data.

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Text search use cases

Incorporate precision matching with MongoDB Search for autocomplete, facets, and fuzzy matching. Because your data stays unified, you eliminate the risk of data drift that commonly occurs in fragmented "bolt-on" search systems.

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Multi-cloud use cases

Deploy a multi-cloud database with unmatched data distribution across AWS, Microsoft Azure, and Google Cloud. Ensure your AI strategy remains flexible and de-risked through consistent operations across major cloud providers.

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Governed AI use cases

Secure your AI applications within the same platform trusted for your operational workloads. Unified governance helps you bridge the trust gap, ensuring your enterprise AI is grounded in accurate, reliable, and secure data.

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Why companies choose MongoDB

30%reduction in operating costs vs. a self-hosted solution

18k+global customers with mission-critical identities

AI/ML

By migrating its Customer Identity Cloud to MongoDB Atlas, Okta eliminated manual database maintenance, accelerating innovation. Leveraging Atlas Vector Search, Okta now powers natural language queries for its chatbot with seamless scalability.

30%reduction in operating costs vs. a self-hosted solution

18k+global customers with mission-critical identities

AI/ML

50–60xfaster code migration

20xfaster small app migrations

GENERATIVE AI

Lombard Odier moved from a monolithic architecture to a microservices-based "GX" platform using MongoDB. This shift allows them to handle over 200 million documents and 10,000 requests per second, enabling faster delivery of new digital banking services.

10xfaster feature deployment

30k+AI agents created by users on the platform to date

LOGISTICS OPTIMIZATION

Relevance AI shifted from a rigid PostgreSQL setup to MongoDB Atlas to handle the unpredictable schemas of AI-driven data. This move empowered their "low-code" platform to support complex agentic workflows with high performance and infinite scale.

300xincrease in daily requests

350kpatients served daily

HEALTHCARE COMPLIANCE

McKesson scaled operations 300x overnight with MongoDB Atlas to meet the DSCSA compliance deadline. The flexible platform ensures pharmaceutical supply chain integrity and reliably serves 350,000+ patients daily.

10minutes to generate reports instead of 12 weeks

50%of the world's insulin production

GENERATIVE AI

Novo Nordisk built NovoScribe, an AI-powered platform with MongoDB Atlas Vector Search, to reduce Clinical Study Report creation from 12 weeks to 10 minutes. This groundbreaking efficiency accelerates time-to-market for new treatments.

Resources for AI innovation

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