MongoDB combines database and embedding models for simplified AI development – News

MongoDB Inc. is making its play for the hearts and minds of artificial intelligence developers and entrepreneurs with today’s announcement of a series of new capabilities designed to help developers move applications from prototype to production more quickly.

They include the general availability of the Voyage 4 family of embedding models and a planned expansion of the MongoDB for Startups program. The new features tighten the integration between MongoDB’s core database platform and the retrieval and embedding technologies it acquired with the purchase of Voyage AI Inc. last year.

Embeddings are numerical representations of data that capture semantic meaning as vectors. They allow systems to compare and retrieve information based on meaning rather than exact keywords, which is essential for many AI tasks.

“Customers increasingly do not think of MongoDB as just a database; they reframe the database as a foundation for their AI stack,” said Benjamin Flast, director of product management.

Four embedding models

The Voyage 4 series of embedding models are now available through application programming interfaces in the MongoDB Atlas managed service and can also be used in the on-premises community edition of MongoDB.

The lineup includes multiple models designed to balance retrieval accuracy, latency and cost. They include voyage-4 for general-purpose use, voyage-4-large for maximum retrieval accuracy, voyage-4-lite for lower latency and cost and voyage-4-nano, an open-weights model intended for local development and testing.

MongoDB said the models are designed to improve retrieval accuracy for production artificial intelligence workloads by reducing the need to move or duplicate data across separate systems.

The company also announced the general availability of voyage-multimodal-3.5, which expands support for interleaved text and images to include video. The model is intended to simplify context extraction from complex documents and multimedia sources.

“This unlocks unified retrieval across multiple content types,” said Franklin Sun, staff product manager at MongoDB. “You have one embedding model instead of three to handle different data types. You also have better end-user experiences where the system can understand the relationship between what someone wrote, what they saw and what they recorded.”

MongoDB said tighter integration between its operational database and retrieval models allows developers to avoid managing separate vector databases, pipelines and synchronization processes, which can introduce latency and operational risk.

Automated embedding and developer tools

The company also introduced automated embedding capabilities for MongoDB Community Vector Search, now available in public preview. The feature automatically generates and stores embeddings whenever data is inserted, updated or queried, eliminating the need for separate embedding pipelines or external services.

Automated embedding is available today for MongoDB Community Edition and is expected to be available soon on the Atlas service. MongoDB said the feature integrates with its drivers and artificial intelligence frameworks such as LangChain and LangGraph.

For Atlas users, MongoDB also introduced embedding and reranking APIs that expose Voyage AI models directly within the platform.

In addition, an artificial intelligence-powered assistant for MongoDB Compass and Atlas Data Explorer is now generally available. The assistant provides natural-language guidance for common data operations such as query optimization and troubleshooting.

MongoDB for Startups expansion

MongoDB for Startups, a program that helps early-stage companies scale applications from initial development to global deployment, is getting a boost with an expanded partner ecosystem. The company said startups participating in the program now represent more than $200 billion in combined valuation, based on Pitchbook data from last month.

Through the program, eligible startups can access matched credits, coordinated onboarding and joint enablement resources across participating technologies.

Initial launch partners include Fireworks AI Inc. and Temporal Technologies Inc.

Samar Abbas, chief executive officer of Temporal, said the partnership is intended to simplify distributed application development. “This allows us to reach a community of developers who value a strong data foundation,” he said in a statement.

MongoDB said additional partners and offerings are expected to be added to the startup program over time.

Photo: Robert Hof/ News