MongoDB, Inc. used the Ai4 stage to showcase a series of product enhancements and strategic AI partner integrations designed to help enterprises build accurate, trustworthy, and scalable AI applications faster. By combining industry-leading embedding models with a fully integrated, AI-ready data platform and by strengthening its global AI partner network MongoDB is equipping organizations with the tools to deliver high-performing, cost-effective AI solutions.
The announcement comes at a time when AI adoption is both a business priority and a challenge. According to the 2025 Gartner Generative and Agentic AI in Enterprise Applications Survey, 68% of IT leaders say they struggle to keep pace with the rapid rollout of generative AI tools, while 37% indicate that their enterprise application vendors largely shape their gen AI strategies. Many enterprises find themselves in the “messy middle” of AI adoption realizing partial benefits but not enough to justify widespread deployment.
A key barrier, MongoDB notes, is the complexity of the AI technology stack, coupled with the challenge of ensuring accuracy for mission-critical applications and managing performance costs at scale. Addressing these issues, the company continues to simplify AI integration by offering higher-performing, more cost-efficient models and deeper interoperability with leading AI frameworks. Recent developments include the integration of Voyage AI’s latest embedding and reranking models into MongoDB databases, the launch of the MongoDB MCP Server for agent access to data and tools, and an expanded AI partner network to give developers more flexibility.
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These advancements have fueled strong adoption. Over the past 18 months, enterprises such as Vonage, LGU+, and The Financial Times, along with nearly 8,000 startups including Laurel and Mercor, have chosen MongoDB for AI application development. Each month, more than 200,000 new developers register for MongoDB Atlas.
“Databases are more central than ever to the technology stack in the age of AI. Modern AI applications require a database that combines advanced capabilities like integrated vector search and best-in-class AI models to unlock meaningful insights from all forms of data (structure, unstructured), all while streamlining the stack,” said Andrew Davidson, SVP of Products at MongoDB. “These systems also demand scalability, security, and flexibility to support production applications as they evolve and as usage grows. By consolidating the AI data stack and by building a cutting-edge AI ecosystem, we’re giving developers the tools they need to build and deploy trustworthy, innovative AI solutions faster than ever before.”
Accelerating AI Innovation with Advanced Product Capabilities
Voyage AI by MongoDB has introduced next-generation embedding models aimed at delivering higher accuracy at lower costs:
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Context-aware embeddings for improved retrieval – The voyage-context-3 model captures complete document context without relying on metadata hacks, summaries, or complex pipelines, resulting in more relevant search results and reduced chunk-size sensitivity.
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Top-tier performance models – voyage-3.5 and voyage-3.5-lite offer industry-leading retrieval accuracy with exceptional price-performance ratios.
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Instruction-based reranking – rerank-2.5 and rerank-2.5-lite allow developers to guide reranking with specific instructions, significantly boosting retrieval precision.
MongoDB has also rolled out the Model Context Protocol (MCP) Server in public preview, enabling direct connections from MongoDB deployments to tools such as GitHub Copilot, Anthropic’s Claude, Cursor, and Windsurf. This integration allows developers to interact with data in natural language, streamline AI application development, and reduce time to market. The MCP Server has already seen strong adoption among enterprise customers building agentic AI applications.
“Many organizations struggle to scale AI because the models themselves aren’t up to the task. They lack the accuracy needed to delight customers, are often complex to fine-tune and integrate, and become too expensive at scale,” said Fred Roma, SVP of Engineering at MongoDB. “The quality of your embedding and reranking models is often the difference between a promising prototype and an AI application that delivers meaningful results in production. That’s why we’ve focused on building models that perform better, cost less, and are easier to use so developers can bring their AI applications into the real world and scale adoption.”
“As more enterprises deploy and scale AI applications and agents, the demand for accurate outputs and reduced latency keeps increasing,” said Jason Andersen, Vice President and Principal Analyst at Moor Insights and Strategy. “By thoughtfully unifying the AI data stack with integrated advanced vector search and embedding capabilities in their core database platform, MongoDB is taking on these challenges while also reducing complexity for developers.”
Expanding the AI Partner Ecosystem
MongoDB’s growing AI partner network aims to accelerate the development and deployment of robust AI applications:
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Enhanced AI reliability and monitoring – Galileo joins the MongoDB ecosystem, offering real-time evaluations and monitoring to ensure reliable AI application deployment.
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Durable AI orchestration – Temporal, an open-source Durable Execution platform, enables developers to build resilient, horizontally scalable AI applications on MongoDB without additional plumbing code for fault tolerance or scale.
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Streamlined AI workflows – LangChain’s collaboration with MongoDB introduces features like GraphRAG for transparent retrieval processes and natural language querying for agentic applications, allowing more trustworthy and explainable AI results.
“As organizations bring AI applications and agents into production, accuracy and reliability are of paramount importance,” said Vikram Chatterji, CEO and co-founder at Galileo. “By formally joining MongoDB’s AI ecosystem, MongoDB and Galileo will now be able to better enable customers to deploy trustworthy AI applications that transform their businesses with less friction.”
“Building production-ready agentic AI means enabling systems to survive real-world reliability and scale challenges, consistently and without fail,” said Maxim Fateev, CTO at Temporal. “Through our partnership with MongoDB, Temporal empowers developers to orchestrate durable, horizontally scalable AI systems with confidence, ensuring engineering teams build applications their customers can count on.”
“As AI agents take on increasingly complex tasks, access to diverse, relevant data becomes essential,” said Harrison Chase, CEO & Co-founder at LangChain. “Our integrations with MongoDB, including capabilities like GraphRAG and natural language querying, equip developers with the tools they need to build and deploy complex, future-proofed agentic AI applications grounded in relevant, trustworthy data.”
