MongoDB Extends Search and Vector Search Capabilities to Self-Managed Offerings
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MongoDB enables millions of developers to securely build AI applications on any infrastructure, from local machines to on-premises data centers
"According to a 2025 IDC survey, more than 74% of organizations plan to use integrated vector databases to store and query vector embeddings within their agentic AI workflows," said
Customers today expect high-performing, personalized, and real-time modern applications. To meet those demands, developers and enterprises alike require comprehensive AI search and retrieval tools integrated into the database where their data is stored. These native out-of-the-box search and AI-driven capabilities include full-text, semantic retrieval, and hybrid search to deliver highly accurate, intelligent, and context-aware retrieval-augmented generation (RAG) and agentic AI user experiences.
"At MongoDB, we believe in empowering developers everywhere with the tools they need to build next-gen applications," said
Enabling millions of developers to build more powerful applications
Previously, integrating search capabilities into self-managed MongoDB environments required adding on external search engines or vector databases. Managing a fragmented search stack added complexity and risk, and created operational overhead that could lead to fragile extract, transform, and load (ETL) pipelines, synchronization errors, and higher costs. This meant that developers had to use and manage multiple systems from different vendors just to add search features—which proved to be complicated, risky, and expensive.
Now, with search and retrieval capabilities directly integrated into MongoDB Community Edition and MongoDB Enterprise Server, developers and organizations can:
- Test and build AI applications locally: Vector search enables semantic information retrieval based on meaning encoded in vector embeddings. This empowers users to manage and build dynamic AI applications that rely on unstructured data like text documents, images, videos, audio files, chat messages, and more, all within their local or on-premises environments.
- Boost accuracy with hybrid search: Combine keyword and vector search to return unified results from a single query for more accurate results. Crucial for reliable agentic solutions and AI applications, developers can easily take advantage of this powerful capability directly through MongoDB's familiar query framework.
- Power AI agents with long-term memory: Allow data in MongoDB to serve as the long-term memory store for AI agents, enabling precise, context-aware applications ready for real-world situations. With Community Edition, developers can easily prototype RAG systems. Organizations building on Enterprise Server can securely ground AI agents in proprietary data on their own infrastructure
MongoDB is a unified document database that gives developers the tools they need to build modern applications to handle any use case, all in one place. Today, MongoDB furthers this commitment with the integration of powerful search and retrieval capabilities that will help developers build intelligent AI applications to provide relevant context for agentic systems in their environment of choice.
MongoDB partners validate new search capabilities in Community Edition
A number of MongoDB partners—including LangChain, a provider of software development frameworks for building LLM-powered applications; and LLamaIndex, an open-source framework for LLM applications—collaborated closely with MongoDB to test search and vector search capabilities in Community Edition.
"We're thrilled MongoDB search and vector search are now accessible in the already popular MongoDB Community Edition," said
"We're excited about the next interaction of search experiences in MongoDB Community Edition. Our customers want the highest flexibility to be able to run their search and gen AI-enabled applications, and bringing this functionality to Community unlocks a whole new way to build and test anywhere," said
MongoDB Search and MongoDB Vector Search are available in MongoDB Community Edition and Enterprise Server via public preview today. To learn more, check out the MongoDB blog.
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Forward-Looking Statements
This press release includes certain "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933, as amended, or the Securities Act, and Section 21E of the Securities Exchange Act of 1934, as amended, including statements concerning integration of search and vector search capabilities with MongoDB Community Edition and MongoDB Enterprise Server. These forward-looking statements include, but are not limited to, plans, objectives, expectations and intentions and other statements contained in this press release that are not historical facts and statements identified by words such as "anticipate," "believe," "continue," "could," "estimate," "expect," "intend," "may," "plan," "project," "will," "would" or the negative or plural of these words or similar expressions or variations. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Although we believe that our plans, intentions, expectations, strategies and prospects as reflected in or suggested by those forward-looking statements are reasonable, we can give no assurance that the plans, intentions, expectations or strategies will be attained or achieved. Furthermore, actual results may differ materially from those described in the forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and factors that are beyond our control including, without limitation: our customers renewing their subscriptions with us and expanding their usage of software and related services; global political changes; the effects of the ongoing military conflicts between
1Source: IDC IT Quick Poll – Agentic AI and Data Q2 Survey 2025, N=102
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SOURCE MongoDB, Inc.
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