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MongoDB

Document database with Atlas Vector Search for AI applications.

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📋About MongoDB

Updated September 16, 2026

MongoDB is a leading database platform company founded in 2007, known for its eponymous NoSQL document database. The publicly traded company serves over 49,000 customers in more than 100 countries, providing a developer-friendly database used by organizations ranging from startups to the Fortune 500.

MongoDB Atlas, the company's fully managed cloud database, includes Atlas Vector Search — a vector database capability that enables semantic search and RAG (retrieval-augmented generation) applications without requiring a separate specialized vector database. This allows developers to store operational data and vector embeddings in the same database, simplifying the architecture of AI applications. MongoDB's flexible document model is particularly well-suited for storing the diverse, unstructured data types common in AI applications.

MongoDB's strategy of adding AI capabilities to its existing, widely-adopted database platform gives it a significant distribution advantage over standalone vector databases like Pinecone or Weaviate. Enterprises already running MongoDB can add vector search with minimal additional complexity, and the company's familiar query language and tooling reduce the learning curve for teams building their first AI applications.

🛠️Products & Tools (1)

MongoDB Atlas Vector SearchVector Databases & RAG

Vector search built into MongoDB Atlas. Store, index, and query vector embeddings alongside your existing MongoDB data without a separate vector database.

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