MongoDB Extends Search and Vector Search Capabilities to Self-Managed Offerings

Rhea-AI Summary

MongoDB (NASDAQ: MDB) announced the expansion of its search and vector search capabilities to MongoDB Community Edition and Enterprise Server, previously exclusive to MongoDB Atlas cloud platform. This integration enables developers to build AI applications locally and on-premises with comprehensive search features.

According to IDC research, over 74% of organizations plan to use integrated vector databases for AI workflows. The new capabilities include full-text search, semantic retrieval, and hybrid search, allowing developers to create retrieval-augmented generation (RAG) and agentic AI experiences without managing multiple systems.

Partners including LangChain and LlamaIndex have validated these new search capabilities, which are now available in public preview for development and testing purposes.

Positive

• Integration of search capabilities eliminates need for external search engines, reducing complexity and costs
• Enables local and on-premises AI application development with vector search capabilities
• Provides unified solution for handling unstructured data (text, images, videos, audio)
• Partnership with key AI framework providers LangChain and LlamaIndex validates the solution

Negative

• Features currently only available in public preview, not general availability
• Full deployment requires migration to MongoDB Atlas for enterprise-grade features

Insights

Database and AI Technology Expert

MongoDB's expansion of search capabilities to self-managed offerings strengthens its competitive position in the AI database market.

MongoDB's announcement represents a significant strategic expansion of their product capabilities. By extending search and vector search features from their cloud platform (Atlas) to Community Edition and Enterprise Server, MongoDB is addressing a critical market need at the intersection of databases and AI development.

The addition of these capabilities solves a fundamental pain point for developers working with AI applications. Previously, developers using self-managed MongoDB installations needed to integrate external search engines or vector databases, creating fragmented architectures that increased complexity, operational overhead, and potential failure points. The integrated solution eliminates these ETL pipelines and synchronization challenges.

This move aligns perfectly with industry trends, as highlighted by the IDC statistic that 74% of organizations plan to use integrated vector databases for AI workflows. Vector search capability is particularly crucial for modern AI applications as it enables semantic information retrieval based on meaning rather than exact keyword matching. This is essential for applications working with unstructured data like documents, images, and audio.

The hybrid search capabilities (combining keyword and vector search) and ability to use MongoDB as a long-term memory store for AI agents directly supports the development of Retrieval-Augmented Generation (RAG) systems. These systems are increasingly important for grounding AI responses in accurate, relevant data.

The endorsements from LangChain and LlamaIndex – two significant players in the LLM application development space – validate the market demand for this functionality and suggest the potential for increased MongoDB adoption among AI developers.

While this announcement doesn't include financial projections, it represents a clear product strategy to capture market share in the rapidly growing AI database segment by removing barriers between MongoDB's cloud and self-managed offerings.

MongoDB enables millions of developers to securely build AI applications on any infrastructure, from local machines to on-premises data centers

NEW YORK, Sept. 17, 2025 /PRNewswire/ -- MongoDB, Inc. (NASDAQ: MDB) today at its developer conference MongoDB.local NYC announced the integration of search and vector search capabilities with MongoDB Community Edition and MongoDB Enterprise Server. Previously exclusive to the fully managed MongoDB Atlas cloud platform, developers and organizations of all sizes can now access the preview of robust full-text search and vector search capabilities on MongoDB's local, on-premises, and self-managed offerings—all with the world's most popular modern database. Starting today, these capabilities are in public preview for development and testing purposes.

"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 Devin Pratt, Research Director at IDC.¹ "In a fast-moving technological era driven by LLMs and AI applications, developers can't afford to be slowed down by fragmented systems. Embedding search and vector search directly into the database gives them one less complexity to manage, and allows them to stay focused on building intelligent applications."

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 Benjamin Cefalo, Senior Vice President, Head of Core Products at MongoDB. "By expanding our Search and Vector Search capabilities, we're giving developers unparalleled flexibility to build in the environment of their choice, with the ultimate customer guarantee—that the core database and query capabilities they love in MongoDB Atlas are also freely available in Community. And when they're ready to bring their applications to market, they can easily migrate to our fully managed MongoDB Atlas platform for seamless scaling, multi-cloud flexibility, and enterprise-grade security."

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 Harrison Chase, CEO, LangChain. "Now our customers can leverage MongoDB and LangChain in either deployment mode and in their preferred environment to build cutting edge LLM applications."

"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 Jerry Liu, CEO, LlamaIndex.

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.

About MongoDB
Headquartered in New York, MongoDB's mission is to empower innovators to create, transform, and disrupt industries with software. MongoDB's unified database platform was built to power the next generation of applications, and MongoDB is the most widely available, globally distributed database on the market. With integrated capabilities for operational data, search, real-time analytics, and AI-powered data retrieval, MongoDB helps organizations everywhere move faster, innovate more efficiently, and simplify complex architectures. Millions of developers and more than 50,000 customers across industries—including over 75% of the Fortune 100—rely on MongoDB for their most important applications. To learn more, visit mongodb.com.

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 Russia and Ukraine and Israel and Hamas on our business and future operating results; economic downturns and/or the effects of rising interest rates, inflation and volatility in the global economy and financial markets on our business and future operating results; our potential failure to meet publicly announced guidance or other expectations about our business and future operating results; liabilities, reputational harm or other adverse consequences resulting from use of AI in our product offerings and internal operations if they don't produce the desired benefits; our limited operating history; our history of losses; our potential failure to repurchase shares of our common stock at favorable prices, if at all; failure of our platform to satisfy customer demands; the effects of increased competition; our investments in new products and our ability to introduce new features, services or enhancements; social, ethical and security issues relating to the use of new and evolving technologies, such as artificial intelligence, in our offerings or partnerships; our ability to effectively expand our sales and marketing organization; our ability to continue to build and maintain credibility with the developer community; our ability to add new customers or increase sales to our existing customers; our ability to maintain, protect, enforce and enhance our intellectual property; the effects of social, ethical and regulatory issues relating to the use of new and evolving technologies, such as artificial intelligence, in our offerings or partnerships; the growth and expansion of the market for database products and our ability to penetrate that market; our ability to maintain the security of our software and adequately address privacy concerns; our ability to manage our growth effectively and successfully recruit and retain additional highly-qualified personnel; and the price volatility of our common stock. These and other risks and uncertainties are more fully described in our filings with the Securities and Exchange Commission ("SEC"), including under the caption "Risk Factors" in our Quarterly Report on Form 10-Q for the quarter ended July 31, 2025, filed with the SEC on August 27, 2025. Additional information will be made available in our Quarterly Report on Form 10-Q for the quarter ended October 31, 2025, and other filings and reports that we may file from time to time with the SEC. Except as required by law, we undertake no duty or obligation to update any forward-looking statements contained in this release as a result of new information, future events, changes in expectations or otherwise.

¹Source: IDC IT Quick Poll – Agentic AI and Data Q2 Survey 2025, N=102

Investor Relations
Brian Denyeau
ICR for MongoDB
646-277-1251
ir@mongodb.com 

Media Relations
MongoDB 
press@mongodb.com

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SOURCE MongoDB, Inc.

FAQ

What new features did MongoDB (MDB) announce for Community Edition in September 2025?

MongoDB announced the integration of search and vector search capabilities with MongoDB Community Edition and Enterprise Server, including full-text search, semantic retrieval, and hybrid search features.

How will MongoDB's vector search capabilities benefit developers?

Developers can now build AI applications locally with vector search for semantic information retrieval, manage unstructured data, and implement RAG systems without managing multiple external systems.

What percentage of organizations plan to use integrated vector databases according to IDC?

According to IDC's 2025 survey, more than 74% of organizations plan to use integrated vector databases for vector embeddings in their agentic AI workflows.

Which partners have validated MongoDB's new search capabilities?

LangChain and LlamaIndex, both providers of LLM application development frameworks, have validated MongoDB's search and vector search capabilities in Community Edition.

When will MongoDB's new search features be available?

The search and vector search capabilities are available in public preview for development and testing purposes starting September 17, 2025.