MongoDB (NASDAQ: MDB) expanded its AI capabilities on Jan 15, 2026 by integrating Voyage AI embedding and reranking models into MongoDB Atlas and Community deployments to enable production-ready retrieval tasks without moving data.
Key launches include five Voyage 4 embedding models (voyage-4, voyage-4-large, voyage-4-lite, voyage-4-nano, and multimodal-3.5), Automated Embedding for MongoDB Vector Search (public preview), Atlas embedding and reranking APIs, and an AI assistant for Compass and Atlas Data Explorer. MongoDB said these features aim to reduce latency, simplify architectures, and improve retrieval accuracy for customers (60,000+ users).
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Positive
- Integration of Voyage 4 models into MongoDB Atlas
- Five embedding models released including voyage-4-large
- Automated Embedding keeps embeddings fresh on data change
- AI assistant for Compass and Atlas Data Explorer is GA
- Positioned as AI-ready platform for 60,000+ customers
Negative
- Automated Embedding is currently in public preview
- Some Atlas features described as “coming soon” (not yet GA)
Publication history
120 years
Trusted publication and citation data in Web of Science Core Collection
Beta start
December 2023
Web of Science Research Assistant entered beta testing
Launch year
2024
Clarivate announcement dated Sept. 4, 2024
Guided tasks
3 named tasks
‘Understand a topic’, ‘Literature review’, ‘Find a journal’
$399.76
Last Close
Volume
Volume 1,617,843 is 19% above the 20-day average of 1,358,030, indicating elevated trading activity before this AI-related headline.
normal
Technical
Price at $387.19, trading above the 200-day MA $275.54 while still 12.94% below the 52-week high.
MDB fell 5.91% with elevated volume, while key software peers were mixed: AFRM −3.83% but NTAP +0.52%, IOT +0.39%, TOST +2.68%, and VRSN +1.2%, pointing to a stock-specific move rather than a sector-wide AI reaction.
| Date | Event | Sentiment | Move | Catalyst |
|---|---|---|---|---|
| Dec 01 |
Earnings release |
Positive | -1.1% |
Strong Q3 FY2026 growth and raised guidance accompanied by mild price decline. |
| Nov 28 |
AI conferences |
Neutral | -1.1% |
Announcement of multiple December 2025 investor and AI conference presentations. |
| Nov 06 |
Funding news |
Positive | +1.6% |
Appetronix seed funding tied to MDB-ticker entity, prompting modest positive reaction. |
| Nov 04 |
Earnings date |
Neutral | -1.6% |
Scheduling of Q3 FY2026 earnings call and webcast details for Dec 1, 2025. |
| Nov 03 |
Leadership change |
Positive | +2.6% |
CEO transition with prelim Q3 results expected above high end of guidance. |
Recent news has mostly seen price moves aligned with sentiment, with one notable divergence on strong earnings.
Over the last few months, MDB events included leadership transition, earnings, and AI-focused exposure. On Nov 3, 2025, MongoDB announced a CEO change alongside preliminary Q3 FY2026 results expected to exceed the high end of guidance, and shares rose 2.57%. Final Q3 FY2026 results on Dec 1, 2025 showed $628.3M revenue, up 19% year-over-year, yet the stock slipped 1.05%. Conference announcements and an earnings date notice saw modest negative reactions, highlighting occasional selloffs around otherwise constructive updates.
This announcement highlights continued commercialization of generative AI for research workflows, with Clarivate’s Web of Science Research Assistant drawing on 120 years of publication data and entering beta in December 2023. For MDB, tagged as AI in this context, investors may compare this to its own AI initiatives and prior AI news, which historically moved the stock by an average of 1.9%. Monitoring future AI product launches, customer traction, and earnings commentary remains important for assessing AI’s strategic impact.
generative ai
technical
“The new generative AI-powered tool helps researchers find key papers faster”
Generative AI is a type of computer technology that can create new content, like text, images, or music, on its own. It’s important because it can produce realistic and useful material quickly, which could change how we create art, write stories, or even develop new products. Think of it as a smart robot that can invent and produce things almost like a human.
knowledge graph
technical
“The chat interface combined with the Web of Science knowledge graph allows researchers”
A knowledge graph is a structured map that links facts about people, companies, products, and events so a computer can understand how they relate to one another. For investors, it turns scattered data—news, filings, research—into clear connections (like a labeled map) that reveal risks, hidden relationships, and opportunities faster, improving due diligence and decision-making without relying on a single document.
co-citation networks
technical
“topic maps and co-citation networks that show different angles on a topic”
Co-citation networks map which papers, reports, news sources, companies, or other items are mentioned together by later documents; items linked together are frequently cited alongside one another, revealing groups and relationships. For investors, these maps act like a heat‑map of attention and influence—showing clusters, emerging themes, and which sources or ideas carry weight—helping to spot trends, assess credibility, and identify areas of concentrated market or research focus.
beta testing
technical
“and entered beta testing in December 2023.”
Beta testing is the stage when a nearly finished product is given to a group of real users outside the company so they can try it in everyday conditions and report problems or suggestions. For investors, a beta test is a practical check on whether a product works, whether users like it, and how quickly it can scale — like a dress rehearsal that reveals whether the show is ready for a paying audience and helps estimate future sales and risks.
ai principles
technical
“Clarivate AI tools have been responsibly developed in line with the Clarivate AI Principles.”
AI principles are a company’s public set of rules and commitments about how it designs, tests, and uses artificial intelligence, like a safety manual for new tools. For investors they signal how a business manages risk to its reputation, legal exposure, and product reliability—similar to checking a company’s safety record before buying stock—which can affect costs, regulatory scrutiny, and long‑term value.
AI-generated analysis. Not financial advice.
Tavily and TinyFish among customers using MongoDB to build and scale AI-powered features and workloads.
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. These capabilities strengthen MongoDB’s position as the leading AI-ready data platform, trusted by more than 60,000 customers running mission-critical workloads. 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.”
Transforming data into AI intelligence
As projects move into production, many organizations are discovering that their existing data stacks were never designed to support context-aware, retrieval-intensive workloads at scale. Developers are left managing fragmented combinations of operational databases, vector stores, and model APIs, which introduces complexity, latency, and operational risk at the exact moment speed and reliability matter most. This fragmentation has become a primary barrier to AI innovations, translating into real customer-facing impact.
MongoDB addresses this by unifying the core capabilities needed to build and run AI applications in production in a single data platform. Instead of stitching together an operational database, a vector store, and multiple pipelines, teams can keep operational data and retrieval capabilities together, reducing latency and synchronization overhead. The result is a simpler architecture, faster iteration, and AI applications that are built to run reliably in production, not just in demos. 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. The Voyage 4 series includes the general-purpose voyage-4 embedding model, which strikes a balance between retrieval accuracy, cost, and latency, the flagship voyage-4-large model for the highest retrieval accuracy, voyage-4-lite for optimized latency and cost, and an open-weights voyage-4-nano for local development and testing, or on-device applications.
- 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. Voyage AI’s voyage-multimodal-3 was the first production-grade embedding model to handle interleaved text and images, voyage-multimodal-3.5 advances this unified processing approach, more effectively vectorizing multimodal data together to best capture key semantic meaning from tables, graphics, figures, slides, PDFs, and more. This helps developers eliminate the significant effort required for complex document parsing, which can reduce retrieval accuracy and lead to less trustworthy applications.
- 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. The result is a simpler architecture with fewer moving parts, making it easier for teams to build and run AI-enabled applications in production. Automated Embedding is available in public preview with support in our drivers (e.g. Javascript, Python, Java, etc) and AI Frameworks like LangChain and LangGraph (Python). Available today for MongoDB Community, and coming soon on MongoDB Atlas.
“We were looking for extremely accurate embedding models, and Voyage AI provided accuracy at scale,” says Sudheesh Nair, Cofounder and CEO of TinyFish. “The Python APIs that Voyage comes out of the box with are also extremely lightweight and very fast.”
“Today, companies need to move extremely fast, and at very lean startups, you need to only focus on what you are building,” said Rotem Weiss, CEO of Tavily. “MongoDB allows us to focus on what matters most, our customers and our business.”
For the first time, developers can build and run AI applications with operational data, semantic understanding, and retrieval in a single system. 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.
To learn more about these new capabilities and to get started, please find the wrap blog here.
About MongoDB
Headquartered in
Press Contact:
press@mongodb.com
FAQ
What did MongoDB (MDB) announce on January 15, 2026?
MongoDB announced integration of Voyage AI embedding and reranking models, five Voyage 4 embedding models, Automated Embedding (public preview), Atlas AI model APIs, and an AI assistant for Compass and Atlas Data Explorer.
Which Voyage embedding models are available through MongoDB Atlas for MDB?
MongoDB offers the Voyage 4 series: voyage-4, voyage-4-large, voyage-4-lite, voyage-4-nano, plus the voyage-multimodal-3.5 model via Atlas APIs.
How does Automated Embedding for MongoDB Vector Search affect MDB customers?
Automated Embedding generates and stores embeddings natively when data is inserted, updated, or queried to keep vectors fresh and reduce external pipelines; it is in public preview now.
Are Voyage AI models available outside MongoDB for MDB users?
Yes; Voyage AI models remain available as a standalone platform independent of MongoDB in addition to Atlas integrations.
What retrieval accuracy claims did MongoDB make about Voyage 4 for MDB?
MongoDB said Voyage 4 embedding models outperform Gemini and Cohere on the public RTEB leaderboard for retrieval accuracy.
