{"success":true,"data":{"pressRelease":{"id":"24861","rtpr_id":"nPn8f9CrSa","ticker":"MDB","exchange":"NASDAQ","all_tickers":["MDB"],"title":"MongoDB Makes Enterprise AI Production Ready","author":"PR Newswire","published_at":"2026-05-07T08:00:02.067Z","article_body":"MongoDB Makes Enterprise AI Production Ready\n\nPR Newswire\n\nLONDON, May 7, 2026\n\nUnified Data Platform Delivers Native Embeddings Generation, Persistent Agent\nMemory, and\nReal-Time Operational Data\n\nLONDON, May 7, 2026 /PRNewswire/ -- MongoDB, Inc. (NASDAQ: MDB) today\nannounced new capabilities at MongoDB local London 2026\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1541703372&u=https%3A%2F%2Fwww.mongodb.com%2Fevents%2Fmongodb-local%2Flondon&a=local+London+2026)\n, furthering its vision and strategy of delivering a unified AI data platform\nthat gives enterprises everything they need to run agents in production—a\nreal-time database, full text and vector search, memory, embeddings, and\nreranker models—all in one platform. Until now, enterprises have had to\nstitch together disparate systems and to hope they would work together at\nscale. MongoDB has solved that.\n\n(https://mma.prnewswire.com/media/384058/MongoDB_Logo.html)\n\n\"The hardest part of running agents in production isn't the model. It's the\ndata layer underneath it,\" said CJ Desai, President and Chief Executive\nOfficer of MongoDB. \"To trust an agent at scale, it has to retrieve the right\ncontext, hold memory across sessions, and operate at machine speed, wherever\nthe enterprise needs it. That's why AI-native companies like ElevenLabs build\nvoice agents on MongoDB, and why institutions like Lloyds Banking Group trust\nit for mission-critical workloads.\"\n\nRetrieval accuracy\n\nWith Automated Voyage AI Embeddings in MongoDB Vector Search, now in public\npreview, embeddings are now generated automatically as data is written or\nupdated to give agents accurate, real-time context.\n\nAgents are only as good as what they remember and what they can retrieve.\nEmbedding models convert information into vectors—an array of numbers that\nrepresent meaning mathematically—so an agent can find the right information.\nMongoDB's Voyage AI embedding models rank #1 on the Retrieval Embedding\nBenchmark (RTEB). This means agents built on MongoDB can accurately find the\nright information.\n\nAutomated Voyage AI Embeddings removes the manual infrastructure work that has\nhistorically stood between enterprises and accurate AI search. Enterprises\nthat previously spent weeks building search infrastructure can now ship\nsemantic search in minutes.\n\nHigh accuracy requires strong memory. Agents without memory can't learn,\nimprove, or be trusted. The LangGraph.js Long-Term Memory Store, now generally\navailable, gives JavaScript and TypeScript developers persistent,\ncross-conversation agent memory that Python developers have had—powered by\nMongoDB Atlas, as a single backend, with no additional database required.\n\n\"When AI tools and agents produce a wrong answer, the instinct is to blame the\nmodel,\" said Pablo Stern, Chief Product Officer, AI and Emerging Products at\nMongoDB. \"But the data platform is what enables the agent with the right\ncontext and memory to act correctly. With MongoDB, we've made this easy.\nDevelopers no longer have to build and maintain data infrastructure, wire up\nembeddings, or manage syncing between systems. They can focus on business\noutcomes rather than the plumbing.\"\n\nPerformance under pressure\n\nMongoDB 8.3, available today, delivers up to 45% more reads, 35% more writes,\n15% more ACID transactions, and 30% more complex operations over MongoDB\n8.0—without changing a line of application code.\n\nWhen enterprises like Adobe need to scale to serve Fortune 500 marketing teams\non one of the world's most widely used platforms, the requirements are clear:\nsub-100ms retrieval, sub-second context updates, and zero downtime. MongoDB\nAtlas is built for AI speed.\n\n\"The requirements of enterprises running AI at scale are what we build for.\nMongoDB 8.3 makes agent workloads faster and cheaper to run on infrastructure\ncustomers already have. We've also moved common data transformations into the\ndatabase itself, so teams no longer have to maintain external pipelines just\nto feed their agents. Production AI doesn't wait, and neither do we,\" said Ben\nCefalo, Chief Product Officer, Core Products at MongoDB.\n\nRun anywhere\n\nFor banks, healthcare organizations, and government agencies, deployment\nchoice isn't optional. It's often a data residency requirement set before\narchitecture enters the conversation.\n\nMongoDB runs across Amazon Web Services (AWS), Google Cloud, Microsoft Azure,\non-premises, and in hybrid environments. Customers get one database, one API,\nand one set of skills that work consistently wherever they deploy.\n\nCross-region connectivity for AWS PrivateLink, now generally available,\nensures that database traffic between MongoDB Atlas clusters in different AWS\nregions stays on the AWS private network, with no exposure to the public\ninternet. That helps security teams approve cross-region architectures faster,\nwith fewer exceptions, and without forcing a tradeoff between compliance and\nglobal reach.\n\nWith these announcements, MongoDB continues to deliver what enterprises need\nto run AI agents in production—all in one platform.\n\nWhat's new at MongoDB.local London 2026:\n\n * Automated Voyage AI Embeddings in MongoDB Vector Search for Atlas\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=515199626&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Fnow-in-public-preview-automated-embedding-in-mongodb-vector-search-on-atlas%2F&a=Automated+Voyage+AI+Embeddings+in+MongoDB+Vector+Search+for+Atlas)\n\n— Public Preview\n * MongoDB 8.3\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1812296230&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Fmongodb-8-3%2F&a=MongoDB+8.3)\n\n— Generally Available\n * LangGraph.js Long-Term Memory Store Integration\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1799734139&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Fmongodb-support-for-langgraph-js-long-term-memory%2F&a=LangGraph.js+Long-Term+Memory+Store+Integration)\n\n— Generally Available\n * Cross-Region Connectivity Support for AWS PrivateLink\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1505958625&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Fcross-region-connectivity-support-for-aws-privatelink%2F&a=Cross-Region+Connectivity+Support+for+AWS+PrivateLink)\n\n— Generally Available\n * Feast Feature Store Integration with MongoDB\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1063356600&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Fmongodb-support-in-feast-framework%2F&a=Feast+Feature+Store+Integration+with+MongoDB+)\n\n— Generally Available\n * New Query Expressions for Data Transformation\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1782691366&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Fnew-query-expressions-for-data-transformation%2F&a=New+Query+Expressions+for+Data+Transformation)\n\n— Generally Available\n * MongoDB AI Skill Badges\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1888832731&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Feducation-new-ai-focused-skill-badges-for-developers%2F&a=MongoDB+AI+Skill+Badges)\n\n— Generally Available\nAbout MongoDB\nHeadquartered in New York, MongoDB's mission is to empower innovators to\ncreate, transform, and disrupt industries with software. MongoDB's unified\ndatabase platform was built to power the next generation of applications, and\nMongoDB is the most widely available, globally distributed database on the\nmarket. With integrated capabilities for operational data, search, real-time\nanalytics, and AI-powered data retrieval, MongoDB helps organizations\neverywhere move faster, innovate more efficiently, and simplify complex\narchitectures. Millions of developers and more than 65,200+ customers across\nindustries – including ~75% of the Fortune 100 – rely on MongoDB for their\nmost important applications. To learn more, visit mongodb.com\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=3414744342&u=https%3A%2F%2Fedge.prnewswire.com%2Fc%2Flink%2F%3Ft%3D0%26l%3Den%26o%3D4670468-1%26h%3D1280962182%26u%3Dhttp%253A%252F%252Fmongodb.com%252F%26a%3Dmongodb.com&a=mongodb.com)\n.\n\nForward-Looking Statements\nThis press release includes certain \"forward-looking statements\" within the\nmeaning of Section 27A of the Securities Act of 1933, as amended, or the\nSecurities Act, and Section 21E of the Securities Exchange Act of 1934, as\namended, including new capabilities announced at MongoDB .local London 2026.\nThese forward-looking statements include, but are not limited to, plans,\nobjectives, expectations and intentions and other statements contained in this\npress release that are not historical facts and statements identified by words\nsuch as \"anticipate,\" \"believe,\" \"continue,\" \"could,\" \"estimate,\" \"expect,\"\n\"intend,\" \"may,\" \"plan,\" \"project,\" \"will,\" \"would\" or the negative or plural\nof these words or similar expressions or variations. These forward-looking\nstatements reflect our current views about our plans, intentions,\nexpectations, strategies and prospects, which are based on the information\ncurrently available to us and on assumptions we have made. Although we believe\nthat our plans, intentions, expectations, strategies and prospects as\nreflected in or suggested by those forward-looking statements are reasonable,\nwe can give no assurance that the plans, intentions, expectations or\nstrategies will be attained or achieved. Furthermore, actual results may\ndiffer materially from those described in the forward-looking statements and\nare subject to a variety of assumptions, uncertainties, risks and factors that\nare beyond our control including, without limitation: our customers renewing\ntheir subscriptions with us and expanding their usage of software and related\nservices; global political changes; the effects of the ongoing military\nconflicts between Russia and Ukraine and Israel and Hamas and recent events in\nVenezuela on our business and future operating results; economic downturns\nand/or the effects of rising interest rates, inflation and volatility in the\nglobal economy and financial markets on our business and future operating\nresults; our potential failure to meet publicly announced guidance or other\nexpectations about our business and future operating results; reputational\nharm or other adverse consequences resulting from use of AI and ML in our\nproduct offerings and internal operations if they don't produce the desired\nbenefits; our limited operating history; our history of losses; our potential\nfailure to repurchase shares of our common stock at favorable prices, if at\nall; failure of our platform to satisfy customer demands; the effects of\nincreased competition; our investments in new products and our ability to\nintroduce new features, services or enhancements, including AI and ML; social,\nethical and security issues relating to the use of new and evolving\ntechnologies, such as artificial intelligence, in our offerings or\npartnerships; our ability to effectively expand our sales and marketing\norganization; our ability to continue to build and maintain credibility with\nthe developer community; our ability to add new customers or increase sales to\nour existing customers; our ability to maintain, protect, enforce and enhance\nour intellectual property; our ability to continue to increase revenue from\nour Atlas platform; the effects of social, ethical and regulatory issues\nrelating to the use of new and evolving technologies, such as AI and ML, in\nour offerings or partnerships; the growth and expansion of the market for\ndatabase products and our ability to penetrate that market; our ability to\nmaintain the security of our software and adequately address privacy concerns;\nour ability to manage our growth effectively and successfully recruit and\nretain additional highly-qualified personnel; our ability to integrate\nacquisitions and work with our strategic partners effectively; and the price\nvolatility of our common stock. These and other risks and uncertainties are\nmore fully described in our filings with the Securities and Exchange\nCommission (\"SEC\"), including under the caption \"Risk Factors\" in our Annual\nReport on Form 10-K for the fiscal year ended January 31, 2026, filed with the\nSEC on March 11, 2026. Additional information will be made available in other\nfilings and reports that we may file from time to time with the SEC. Except as\nrequired by law, we undertake no duty or obligation to update any\nforward-looking statements contained in this release as a result of new\ninformation, future events, changes in expectations or otherwise.\n\nContacts\nInvestors\nir@mongodb.com (mailto:ir@mongodb.com)\n\nMedia\npress@mongodb.com (mailto:press@mongodb.com)\n\nView original content to download\nmultimedia:https://www.prnewswire.com/news-releases/mongodb-makes-enterprise-ai-production-ready-302764870.html\n(https://www.prnewswire.com/news-releases/mongodb-makes-enterprise-ai-production-ready-302764870.html)\n\nSOURCE MongoDB, Inc.\n\n\n\nPhoto: \nhttps://mma.prnewswire.com/media/384058/MongoDB_Logo.jpg\n\nCopyright (c) 2026 PR Newswire Association,LLC. All Rights Reserved.","article_body_html":"","raw_payload":{"data":{"id":"nPn8f9CrSa","title":"MongoDB Makes Enterprise AI Production Ready","author":"PR Newswire","ticker":"MDB","created":"2026-05-07T08:00:02.067Z","tickers":["MDB"],"exchange":"NASDAQ","article_body":"MongoDB Makes Enterprise AI Production Ready\n\nPR Newswire\n\nLONDON, May 7, 2026\n\nUnified Data Platform Delivers Native Embeddings Generation, Persistent Agent\nMemory, and\nReal-Time Operational Data\n\nLONDON, May 7, 2026 /PRNewswire/ -- MongoDB, Inc. (NASDAQ: MDB) today\nannounced new capabilities at MongoDB local London 2026\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1541703372&u=https%3A%2F%2Fwww.mongodb.com%2Fevents%2Fmongodb-local%2Flondon&a=local+London+2026)\n, furthering its vision and strategy of delivering a unified AI data platform\nthat gives enterprises everything they need to run agents in production—a\nreal-time database, full text and vector search, memory, embeddings, and\nreranker models—all in one platform. Until now, enterprises have had to\nstitch together disparate systems and to hope they would work together at\nscale. MongoDB has solved that.\n\n(https://mma.prnewswire.com/media/384058/MongoDB_Logo.html)\n\n\"The hardest part of running agents in production isn't the model. It's the\ndata layer underneath it,\" said CJ Desai, President and Chief Executive\nOfficer of MongoDB. \"To trust an agent at scale, it has to retrieve the right\ncontext, hold memory across sessions, and operate at machine speed, wherever\nthe enterprise needs it. That's why AI-native companies like ElevenLabs build\nvoice agents on MongoDB, and why institutions like Lloyds Banking Group trust\nit for mission-critical workloads.\"\n\nRetrieval accuracy\n\nWith Automated Voyage AI Embeddings in MongoDB Vector Search, now in public\npreview, embeddings are now generated automatically as data is written or\nupdated to give agents accurate, real-time context.\n\nAgents are only as good as what they remember and what they can retrieve.\nEmbedding models convert information into vectors—an array of numbers that\nrepresent meaning mathematically—so an agent can find the right information.\nMongoDB's Voyage AI embedding models rank #1 on the Retrieval Embedding\nBenchmark (RTEB). This means agents built on MongoDB can accurately find the\nright information.\n\nAutomated Voyage AI Embeddings removes the manual infrastructure work that has\nhistorically stood between enterprises and accurate AI search. Enterprises\nthat previously spent weeks building search infrastructure can now ship\nsemantic search in minutes.\n\nHigh accuracy requires strong memory. Agents without memory can't learn,\nimprove, or be trusted. The LangGraph.js Long-Term Memory Store, now generally\navailable, gives JavaScript and TypeScript developers persistent,\ncross-conversation agent memory that Python developers have had—powered by\nMongoDB Atlas, as a single backend, with no additional database required.\n\n\"When AI tools and agents produce a wrong answer, the instinct is to blame the\nmodel,\" said Pablo Stern, Chief Product Officer, AI and Emerging Products at\nMongoDB. \"But the data platform is what enables the agent with the right\ncontext and memory to act correctly. With MongoDB, we've made this easy.\nDevelopers no longer have to build and maintain data infrastructure, wire up\nembeddings, or manage syncing between systems. They can focus on business\noutcomes rather than the plumbing.\"\n\nPerformance under pressure\n\nMongoDB 8.3, available today, delivers up to 45% more reads, 35% more writes,\n15% more ACID transactions, and 30% more complex operations over MongoDB\n8.0—without changing a line of application code.\n\nWhen enterprises like Adobe need to scale to serve Fortune 500 marketing teams\non one of the world's most widely used platforms, the requirements are clear:\nsub-100ms retrieval, sub-second context updates, and zero downtime. MongoDB\nAtlas is built for AI speed.\n\n\"The requirements of enterprises running AI at scale are what we build for.\nMongoDB 8.3 makes agent workloads faster and cheaper to run on infrastructure\ncustomers already have. We've also moved common data transformations into the\ndatabase itself, so teams no longer have to maintain external pipelines just\nto feed their agents. Production AI doesn't wait, and neither do we,\" said Ben\nCefalo, Chief Product Officer, Core Products at MongoDB.\n\nRun anywhere\n\nFor banks, healthcare organizations, and government agencies, deployment\nchoice isn't optional. It's often a data residency requirement set before\narchitecture enters the conversation.\n\nMongoDB runs across Amazon Web Services (AWS), Google Cloud, Microsoft Azure,\non-premises, and in hybrid environments. Customers get one database, one API,\nand one set of skills that work consistently wherever they deploy.\n\nCross-region connectivity for AWS PrivateLink, now generally available,\nensures that database traffic between MongoDB Atlas clusters in different AWS\nregions stays on the AWS private network, with no exposure to the public\ninternet. That helps security teams approve cross-region architectures faster,\nwith fewer exceptions, and without forcing a tradeoff between compliance and\nglobal reach.\n\nWith these announcements, MongoDB continues to deliver what enterprises need\nto run AI agents in production—all in one platform.\n\nWhat's new at MongoDB.local London 2026:\n\n * Automated Voyage AI Embeddings in MongoDB Vector Search for Atlas\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=515199626&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Fnow-in-public-preview-automated-embedding-in-mongodb-vector-search-on-atlas%2F&a=Automated+Voyage+AI+Embeddings+in+MongoDB+Vector+Search+for+Atlas)\n\n— Public Preview\n * MongoDB 8.3\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1812296230&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Fmongodb-8-3%2F&a=MongoDB+8.3)\n\n— Generally Available\n * LangGraph.js Long-Term Memory Store Integration\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1799734139&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Fmongodb-support-for-langgraph-js-long-term-memory%2F&a=LangGraph.js+Long-Term+Memory+Store+Integration)\n\n— Generally Available\n * Cross-Region Connectivity Support for AWS PrivateLink\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1505958625&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Fcross-region-connectivity-support-for-aws-privatelink%2F&a=Cross-Region+Connectivity+Support+for+AWS+PrivateLink)\n\n— Generally Available\n * Feast Feature Store Integration with MongoDB\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1063356600&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Fmongodb-support-in-feast-framework%2F&a=Feast+Feature+Store+Integration+with+MongoDB+)\n\n— Generally Available\n * New Query Expressions for Data Transformation\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1782691366&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Fnew-query-expressions-for-data-transformation%2F&a=New+Query+Expressions+for+Data+Transformation)\n\n— Generally Available\n * MongoDB AI Skill Badges\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=1888832731&u=https%3A%2F%2Fwww.mongodb.com%2Fproducts%2Fupdates%2Feducation-new-ai-focused-skill-badges-for-developers%2F&a=MongoDB+AI+Skill+Badges)\n\n— Generally Available\nAbout MongoDB\nHeadquartered in New York, MongoDB's mission is to empower innovators to\ncreate, transform, and disrupt industries with software. MongoDB's unified\ndatabase platform was built to power the next generation of applications, and\nMongoDB is the most widely available, globally distributed database on the\nmarket. With integrated capabilities for operational data, search, real-time\nanalytics, and AI-powered data retrieval, MongoDB helps organizations\neverywhere move faster, innovate more efficiently, and simplify complex\narchitectures. Millions of developers and more than 65,200+ customers across\nindustries – including ~75% of the Fortune 100 – rely on MongoDB for their\nmost important applications. To learn more, visit mongodb.com\n(https://edge.prnewswire.com/c/link/?t=0&l=en&o=4682996-1&h=3414744342&u=https%3A%2F%2Fedge.prnewswire.com%2Fc%2Flink%2F%3Ft%3D0%26l%3Den%26o%3D4670468-1%26h%3D1280962182%26u%3Dhttp%253A%252F%252Fmongodb.com%252F%26a%3Dmongodb.com&a=mongodb.com)\n.\n\nForward-Looking Statements\nThis press release includes certain \"forward-looking statements\" within the\nmeaning of Section 27A of the Securities Act of 1933, as amended, or the\nSecurities Act, and Section 21E of the Securities Exchange Act of 1934, as\namended, including new capabilities announced at MongoDB .local London 2026.\nThese forward-looking statements include, but are not limited to, plans,\nobjectives, expectations and intentions and other statements contained in this\npress release that are not historical facts and statements identified by words\nsuch as \"anticipate,\" \"believe,\" \"continue,\" \"could,\" \"estimate,\" \"expect,\"\n\"intend,\" \"may,\" \"plan,\" \"project,\" \"will,\" \"would\" or the negative or plural\nof these words or similar expressions or variations. These forward-looking\nstatements reflect our current views about our plans, intentions,\nexpectations, strategies and prospects, which are based on the information\ncurrently available to us and on assumptions we have made. Although we believe\nthat our plans, intentions, expectations, strategies and prospects as\nreflected in or suggested by those forward-looking statements are reasonable,\nwe can give no assurance that the plans, intentions, expectations or\nstrategies will be attained or achieved. Furthermore, actual results may\ndiffer materially from those described in the forward-looking statements and\nare subject to a variety of assumptions, uncertainties, risks and factors that\nare beyond our control including, without limitation: our customers renewing\ntheir subscriptions with us and expanding their usage of software and related\nservices; global political changes; the effects of the ongoing military\nconflicts between Russia and Ukraine and Israel and Hamas and recent events in\nVenezuela on our business and future operating results; economic downturns\nand/or the effects of rising interest rates, inflation and volatility in the\nglobal economy and financial markets on our business and future operating\nresults; our potential failure to meet publicly announced guidance or other\nexpectations about our business and future operating results; reputational\nharm or other adverse consequences resulting from use of AI and ML in our\nproduct offerings and internal operations if they don't produce the desired\nbenefits; our limited operating history; our history of losses; our potential\nfailure to repurchase shares of our common stock at favorable prices, if at\nall; failure of our platform to satisfy customer demands; the effects of\nincreased competition; our investments in new products and our ability to\nintroduce new features, services or enhancements, including AI and ML; social,\nethical and security issues relating to the use of new and evolving\ntechnologies, such as artificial intelligence, in our offerings or\npartnerships; our ability to effectively expand our sales and marketing\norganization; our ability to continue to build and maintain credibility with\nthe developer community; our ability to add new customers or increase sales to\nour existing customers; our ability to maintain, protect, enforce and enhance\nour intellectual property; our ability to continue to increase revenue from\nour Atlas platform; the effects of social, ethical and regulatory issues\nrelating to the use of new and evolving technologies, such as AI and ML, in\nour offerings or partnerships; the growth and expansion of the market for\ndatabase products and our ability to penetrate that market; our ability to\nmaintain the security of our software and adequately address privacy concerns;\nour ability to manage our growth effectively and successfully recruit and\nretain additional highly-qualified personnel; our ability to integrate\nacquisitions and work with our strategic partners effectively; and the price\nvolatility of our common stock. These and other risks and uncertainties are\nmore fully described in our filings with the Securities and Exchange\nCommission (\"SEC\"), including under the caption \"Risk Factors\" in our Annual\nReport on Form 10-K for the fiscal year ended January 31, 2026, filed with the\nSEC on March 11, 2026. Additional information will be made available in other\nfilings and reports that we may file from time to time with the SEC. Except as\nrequired by law, we undertake no duty or obligation to update any\nforward-looking statements contained in this release as a result of new\ninformation, future events, changes in expectations or otherwise.\n\nContacts\nInvestors\nir@mongodb.com (mailto:ir@mongodb.com)\n\nMedia\npress@mongodb.com (mailto:press@mongodb.com)\n\nView original content to download\nmultimedia:https://www.prnewswire.com/news-releases/mongodb-makes-enterprise-ai-production-ready-302764870.html\n(https://www.prnewswire.com/news-releases/mongodb-makes-enterprise-ai-production-ready-302764870.html)\n\nSOURCE MongoDB, Inc.\n\n\n\nPhoto: \nhttps://mma.prnewswire.com/media/384058/MongoDB_Logo.jpg\n\nCopyright (c) 2026 PR Newswire Association,LLC. All Rights Reserved."},"type":"article","timestamp":"2026-05-07T08:00:02.126892087Z","server_sent_at_ms":1778140802126},"received_at":"2026-05-07T08:00:02.195Z","source_url":"https://www.prnewswire.com/news-releases/mongodb-makes-enterprise-ai-production-ready-302764870.html"},"analysis":{"id":"19451","press_release_id":"24861","analysis_json":{"industry":{"label":"Software","sector":"Information Technology"},"redFlags":[],"eventType":"product_launch","narrative":"MongoDB announced MongoDB 8.3 and new AI capabilities, including Automated Voyage AI Embeddings and the LangGraph.js Long-Term Memory Store, to streamline enterprise AI production.\n\nThe company claims 8.3 delivers up to 45% better read performance and 35% better write performance without code changes, alongside new cross-region connectivity for AWS PrivateLink.\n\nLeaders highlighted that the platform unifies real-time databases, vector search, and agent memory to solve data-layer complexity for clients like Adobe and Lloyds Banking Group.","sentiment":"bullish","agentHooks":{"shouldPost":true,"suggestedAngle":"MongoDB 8.3 targets enterprise AI workloads with significant performance boosts and new native embedding capabilities."},"keyFigures":{"customDimensions":{"customer_count":"65,200+","fortune_100_penetration":"~75%","performance_increase_reads":"45%","performance_increase_writes":"35%","performance_increase_complex_ops":"30%","performance_increase_transactions":"15%"}},"quotedText":"The hardest part of running agents in production isn't the model. It's the data layer underneath it.","namedEntities":{"people":[{"name":"CJ Desai","role":"President and Chief Executive Officer"},{"name":"Pablo Stern","role":"Chief Product Officer, AI and Emerging Products"},{"name":"Ben Cefalo","role":"Chief Product Officer, Core Products"}],"products":["MongoDB 8.3","MongoDB Atlas","MongoDB Vector Search","Automated Voyage AI Embeddings","LangGraph.js Long-Term Memory Store","AWS PrivateLink"],"companies":[{"name":"MongoDB, Inc.","ticker":"MDB"},{"name":"ElevenLabs","relationship":"customer"},{"name":"Lloyds Banking Group","relationship":"customer"},{"name":"Adobe","relationship":"customer"},{"name":"Amazon Web Services","relationship":"partner/platform"},{"name":"Google Cloud","relationship":"partner/platform"},{"name":"Microsoft Azure","relationship":"partner/platform"},{"name":"Voyage AI","relationship":"partner"}],"dollarAmounts":[]},"materialImpact":{"score":3,"reasoning":"Major product release (MongoDB 8.3) and significant AI feature announcements (Automated Voyage AI Embeddings, LangGraph.js integration) that directly address enterprise AI production needs. While positive, it is an incremental product update rather than a binary event or earnings surprise."},"tickerRelevance":{"others":[],"primary":"MDB"},"globalImportance":45,"audienceRelevance":70,"eventTypeSecondary":[],"importanceComponents":{"tickerTier":"large-cap/growth","eventGravity":"major product release","sectorWeight":"Software / AI Infrastructure","householdBrandBoost":"high developer mindshare"}},"event_type":"product_launch","event_type_secondary":null,"sentiment":"bullish","material_impact_score":3,"narrative":"MongoDB announced MongoDB 8.3 and new AI capabilities, including Automated Voyage AI Embeddings and the LangGraph.js Long-Term Memory Store, to streamline enterprise AI production.\n\nThe company claims 8.3 delivers up to 45% better read performance and 35% better write performance without code changes, alongside new cross-region connectivity for AWS PrivateLink.\n\nLeaders highlighted that the platform unifies real-time databases, vector search, and agent memory to solve data-layer complexity for clients like Adobe and Lloyds Banking Group.","key_figures":{"customDimensions":{"customer_count":"65,200+","fortune_100_penetration":"~75%","performance_increase_reads":"45%","performance_increase_writes":"35%","performance_increase_complex_ops":"30%","performance_increase_transactions":"15%"}},"named_entities":{"people":[{"name":"CJ Desai","role":"President and Chief Executive Officer"},{"name":"Pablo Stern","role":"Chief Product Officer, AI and Emerging Products"},{"name":"Ben Cefalo","role":"Chief Product Officer, Core Products"}],"products":["MongoDB 8.3","MongoDB Atlas","MongoDB Vector Search","Automated Voyage AI Embeddings","LangGraph.js Long-Term Memory Store","AWS PrivateLink"],"companies":[{"name":"MongoDB, Inc.","ticker":"MDB"},{"name":"ElevenLabs","relationship":"customer"},{"name":"Lloyds Banking Group","relationship":"customer"},{"name":"Adobe","relationship":"customer"},{"name":"Amazon Web Services","relationship":"partner/platform"},{"name":"Google Cloud","relationship":"partner/platform"},{"name":"Microsoft Azure","relationship":"partner/platform"},{"name":"Voyage AI","relationship":"partner"}],"dollarAmounts":[]},"model_name":"glm-4.7","prompt_hash":"sha256:f66160e0fe5301b2","schema_hash":"sha256:12363694422af5d9","created_at":"2026-05-07T18:37:30.173Z","global_importance":45,"audience_relevance":70,"importance_components":{"tickerTier":"large-cap/growth","eventGravity":"major product release","sectorWeight":"Software / AI Infrastructure","householdBrandBoost":"high developer mindshare"}},"durationMs":null,"modelName":"glm-4.7"}}