MongoDB Aims For Production-Ready AI Apps With New Model Capabilities

MongoDB is more tightly integrating the embedding and reranking model technology it obtained last year through its Voyage AI acquisition with its database development platform.

MongoDB is expanding the AI capabilities of its database and application development platform with newly integrated embedding and reranking models that the company says will improve the accuracy of AI applications as they move from development into production.

The new functionality, based on technology MongoDB acquired when it bought Voyage AI in February 2025, delivers a unified data intelligence layer for production AI and helps developers build and operate sophisticated AI applications at scale with minimized risk of hallucinations and without the need to move or duplicate data, according to the company.

MongoDB, which is holding its MongoDB.local San Francisco event today, also announced an expansion of MongoDB for Startups, a program that provides startup companies with technical expertise, financial credits and other resources to build AI software using a technology stack with MongoDB Atlas as its foundation.

“Over the past few months, we've spent time with countless customers, founders, executives at large companies, platform teams, developers—not to pitch but to understand where things break as AI moves from prototype to production,” said Ben Cefalo, senior vice president, MongoDB head of core products and Atlas Foundational Services, in a press briefing prior to today’s MongoDB.local San Francisco event.

“Those conversations rarely start with AI models. They start with really practical questions like ‘How do we get our data ready? How do we keep things performant as we scale? How do we ensure accuracy of results? How do we avoid gluing together five different systems or extensions just to ship something? What's going to be the ROI?’” Cefalo said.

While MongoDB is generally seen as among the leading next-generation database systems, the company in recent years has positioned its software—especially the MongoDB Atlas cloud-native database—as a foundation for the technology stack needed to develop and run AI applications.

In 2024 the company launched the MongoDB AI Applications Program (MAAP) through which the company partners with the cloud hyperscalers, large language model developers, AI development tool providers, and system integrators and consulting partners to provide a technology stack and reference architectures for building AI systems.

Cefalo pointed to capabilities within the MongoDB platform that support production AI systems, at scale, including support for structured, semi-structured and unstructured data; real-time operational data; data accuracy controls; vector search and hybrid search functionality; and automated embedding.

“At the end of the day, all this work comes back to one thing: Helping builders build,” Cefalo said. “The database, the platform, the industry-leading AI capabilities—it's all in the service of turning ideas into systems that actually run.”

Integrating Voyage AI’s embedding and reranking models with the MongoDB core database provides a unified data intelligence layer for production AI, according to the company. With the addition of these models into the MongoDB platform infrastructure developers can build and operate complex AI applications at scale, with reduced risk of hallucinations, and without the need to move or duplicate data.

New Voyage Models

MongoDB announced the general availability of a new Voyage 4 model series including the general-purpose Voyage-4 embedding model, the flagship Voyage-4 large language model for retrieval accuracy, Voyage-4-lite, and the open-weights Voyage-4-nano for local development and testing and for on-device applications.

Also now generally available is the new Voyage-Multimodal-3.5 model with expanded support for interleaved text and images.

The company also unveiled a public preview of Automated Embedding for MongoDB Vector Search in the MongoDB Community edition with availability soon in MongoDB Atlas. Also now generally available is the Atlas Embedding and Reranking API that exposes Voyage AI models natively within Atlas.

“This announcement is all about taking one of the most painful parts of building AI applications, that is, managing retrieval and managing embedding models, and pushing it down into the database,” said Frank Liu, a Voyage AI product manager, during the press conference.

“For builders, this means that there is one place to manage all of your data, your embeddings and your retrieval. There's less friction from getting [an] idea to a working AI feature and also a clean path from prototype to production using the same models and platform,” Liu said. “All this is designed to pair you with the rest of MongoDB’s capabilities like auto embedding, vector search, and a more unified developer experience, so that when you choose MongoDB for your AI workloads, you're not just getting a database, you're getting a retrieval stack that can keep up with your ambitions as a developer.”

MongoDB For Startups Expansion

MongoDB said the companies that participate in its MongoDB for Startups program now represent more than $200 billion in combined valuations. The overall goal of the program is to provide startup companies with a complete infrastructure stack, so startups can avoid having to devote time to infrastructure decisions.

Under the expansion the program’s ecosystem of supporting IT vendors, including AI workflow platform developer Temporal and generative AI platform provider Fireworks AI, will provide startups with match credits, enabling content, joint events and other benefits across complementary technologies.

“The goal is to turn MongoDB for Startups from a one-way perks marketplace to a two-way ecosystem, where partners and startups both benefit from being in the program,” said Suraj Patel, vice president of MongoDB Ventures & Corporate Development, during the press conference.

“This reciprocal partnership with MongoDB allows us to reach a community of developers who value a strong data foundation,” said Temporal CEO Samar Abbas, in a statement. “We look forward to creating a collaborative ecosystem that simplifies complexity for founders as they push the boundaries of distributed systems and workflow orchestration.”

“By joining this program, we are ensuring [startup] founders who choose MongoDB can easily access our high-performance inference engine, creating a seamless path to scale their AI ambitions together,” said Lin Qiao, Fireworks co-founder and CEO, also in a statement.

MongoDB is also putting increased emphasis on recruiting more startups from San Francisco and the Bay Area, with plans to host more than 50 local events in the next year, with a particular emphasis on AI startups.