MongoDB’s document data model is built to match how developers think and work, and seamlessly handles vectors and any type of data at the scale AI-enriched applications require.
MongoDB’s modern architecture enables you to isolate and scale AI workloads independent of your core operational database for optimized performance and lower cost.
When developers have flexible tools to move fast, you can innovate more quickly. MongoDB empowers teams to ship AI-enriched applications and deploy new features at a faster pace—without the confines of rigid data models that slow teams down.
MongoDB allows you to run anywhere—on your laptop, in your data center, across clouds, or in hybrid environments—to easily meet low-latency performance and data sovereignty requirements with no vendor lock-in.
MongoDB simplifies your tech stack by delivering the capabilities needed for AI in a single database—including semantic search for intelligent applications, vector search for generative AI, and more—to reduce complexity and operational costs.
Security and data protection are at the core of MongoDB. Industry-first queryable encryption protects data at rest where it’s stored, in motion over networks, and even while in use while being processed to meet the most stringent regulatory and compliance requirements.
Vector search connects the dots between your data to find relevant results even when the user doesn’t know what they’re looking for.
Retrieval-augmented-generation (RAG) gives LLMs—the foundation of AI—contextual, up-to-date data that is more useful for your application.
MongoDB lets you store and search vector embeddings generated by OpenAI and other LLMs alongside the rest of your operational data.
Hybrid search combines text search with the advanced capabilities of vector search to deliver more accurate and relevant search results.
Store vectorized data alongside proprietary business data, giving AI-powered applications the information they need to transform user experiences.
Simplify and streamline the development and deployment of generative AI applications backed by enterprise data with deep integrations between MongoDB and Microsoft Azure OpenAI.
With the click of a button, Amazon Bedrock integrates MongoDB Atlas as a vector database into its fully managed, end-to-end retrieval-augmented generation (RAG) workflow.
Confidently build generative AI experiences by integrating MongoDB with Google Cloud Vertex AI and BigQuery to accelerate deployment and stay at the forefront of AI innovation.
Combining MongoDB Atlas with LangChain simplifies and streamlines the development and deployment of generative AI applications.
With Anthropic’s Claude models and MongoDB, you can deploy AI applications that leverage internal data sources to produce more accurate and relevant results.
Enhance search functions and data retrieval, script effective recommendation systems, perform in-depth document analysis, create sophisticated chatbots, and more.