Simplify the AI lifecycle through operational, analytical, and AI data services that leverage a single data model and single query API on top of a highly scalable and secure multi-cloud platform.
Innovate and experiment with new parameters and data of any type by landing, storing, and indexing data without lengthy schema design or ongoing modifications.
Achieve high throughput and low latency for inference stores and by combining data tiering and federation with row and column indexing in a horizontally scalable, operational database.
Enhance productivity for developers and ML/AI teams with a single expressive query API that simplifies data preparation, model training, inferencing, and knowledge retrieval.
Augment applications with generative AI through natively integrated vector and document datastores without the extra infrastructure to provision, secure or manage.
A multi-cloud database service built for resilience, scalability, data privacy, and security.
Unified with Atlas Database and with support integrations into LLMs, Atlas Vector Search is a fast and easy way to build semantic search and AI-powered apps.
Combining keyword search by Atlas Search with semantic search powered by Atlas Vector Search to improve the relevance and accuracy of prompts for LLMs.
Automatically run code in response to database changes, user events, or on preset intervals. Easily interact with ML models deployed as REST endpoints.
Build and run data-intensive analytical applications by combining the flexibility of the document model with time series collections.
Seamlessly query, transform, and aggregate data across Atlas databases and cloud object storage.
Transform building ML/AI apps requiring skew detection, feature stores, and enrichment pipelines. Unify working with data in motion and at rest.
Build new classes of sophisticated AI apps combining MongoDB data and high volume, high-velocity data in Apache Spark and Databricks.
To compete and win in the digital economy you need to make your applications smarter. Smarter apps use data, AI, and analytics to engage users with natural language, generate insights and autonomously take action.
To build this new generation of apps, we need to do things differently. We can no longer rely on just copying our data out of operational systems into centralized analytics systems. Instead, we have to bring a new class of AI and analytics processing directly to the source of the data – to the applications themselves. We call this application-driven intelligence.
MongoDB Atlas puts powerful AI and analytics capabilities directly into the hands of developers in ways that fit their workflows, frameworks, and languages.
Learn more about the requirements to successfully deliver application-driven intelligence and how you can get started.
Using MongoDB and MindsDB to enhance predictive capabilities for data science and data engineering teams.
Learn how to make a call to an OpenAI API and perform a vector search query in MongoDB Atlas.