MongoDB Provides New Capabilities to Move Beyond AI Experimentation to Deploy Production-Ready Applications

MongoDB, Inc. announced an industry-first expansion of its AI capabilities to bring together its core database with Voyage AI's embedding and reranking models to deliver a unified data intelligence layer for production AI.

According to the company, by integrating these models directly into MongoDB's platform infrastructure, developers can now build and operate sophisticated applications at scale with reduced risk of hallucinations, without the need to move or duplicate data.

To support developers moving AI applications into production, MongoDB introduced a set of new AI capabilities designed to simplify how intelligent applications are built and operated.

The company unveiled five embedding models from Voyage AI, MongoDB's embedding and retrieval model suite, Automated Embedding for MongoDB Community Vector Search, embedding and reranking AI model APIs in Atlas, and an AI-powered data operations assistant for MongoDB Compass and Atlas Data Explorer.

Voyage AI models are available through MongoDB Atlas via API, integrated with MongoDB Community through managed Automated Embedding, and remain fully available as a standalone platform independent of MongoDB.

"The biggest challenge customers face with AI isn't experimentation, it's operating reliably at scale," said Fred Roma, senior vice president of product and engineering at MongoDB. "Developers want fewer moving parts and clearer paths from prototype to production. With today's launches, MongoDB is raising the bar, helping teams reduce complexity and focus on building AI applications that perform in real-world, mission-critical environments."

MongoDB addresses this by unifying the core capabilities needed to build and run AI New capabilities include:

• State-of-the-art accuracy with models from Voyage AI: The general availability of the new Voyage 4 series continues giving developers high performing embedding models—which outperform Gemini and Cohere on the public RTEB leaderboard—for more accurate retrieval at lower cost.
• Facilitated context extraction from video, images, and text: The general availability of the new voyage-multimodal-3.5 model expands support for interleaved text and images to now include video.
• Automated Embedding for MongoDB Vector Search: Automatically generate and store high-fidelity embeddings using Voyage AI whenever data is inserted, updated, or queried. By handling embedding generation natively within the database, MongoDB removes the need for separate embedding pipelines or external model services. Embeddings stay fresh as data changes, helping retrieval to remain accurate and AI applications to maintain reliable context.

For the first time, developers can build and run AI applications with operational data, semantic understanding, and retrieval in a single system, the vendor said.

MongoDB's Atlas Embedding and Reranking API exposes Voyage AI models natively within Atlas, allowing teams to ship AI features with enterprise-grade security, performance, and reliability infrastructure.

An intelligent assistant for MongoDB Compass and Atlas Data Explorer is now generally available, delivering natural-language, AI-powered assistance for everyday data operations, such as query optimization.

MongoDB also introduced a new AI skills certification to help teams scale data strategies, accelerate time to market, and reduce costs–the first in a broader set of AI skill offerings planned this year, the company said.

For more information about this news, visit www.mongodb.com.