MongoDB Announces General Availability of MongoDB Atlas Vector Search Integration with Amazon Bedrock

Rhea-AI Summary

MongoDB, Inc. (NASDAQ: MDB) announced the general availability of MongoDB Atlas Vector Search on Knowledge Bases for Amazon Bedrock, facilitating the development of generative AI-powered applications. Customers like Novo Nordisk are benefiting from this integration to create innovative end-user experiences securely. The partnership between MongoDB and AWS allows for the customization of foundation models and the deployment of generative AI applications on AWS, providing accurate and relevant responses to user queries.

Positive

• The integration of MongoDB Atlas Vector Search and Amazon Bedrock enables organizations to build generative AI applications with ease, leveraging real-time operational data for enhanced end-user experiences.

• Customers can benefit from faster query times and up to 60% faster query times, optimizing cost and performance with MongoDB Atlas Search Nodes.

• The partnership between MongoDB and AWS allows for the secure use of generative AI with proprietary data to realize business value quickly and with less operational overhead.

Negative

• Organizations may face challenges in ensuring the accuracy of outputs from AI-powered systems while protecting their proprietary data.

• Despite the benefits, there may be concerns about the complexity and resource requirements involved in deploying generative AI applications with MongoDB Atlas Vector Search and Amazon Bedrock.

New integration of MongoDB Atlas Vector Search Knowledge Bases for Amazon Bedrock accelerates development of highly engaging generative AI-powered applications

Novo Nordisk among customers building AI-powered applications with MongoDB Atlas Vector Search and Amazon Bedrock

NEW YORK, May 2, 2024 /PRNewswire/ -- MongoDB, Inc. (NASDAQ: MDB) today at its developer conference MongoDB.local NYC announced the general availability of MongoDB Atlas Vector Search on Knowledge Bases for Amazon Bedrock to enable organizations to build generative AI application features using fully managed foundation models (FMs) more easily. MongoDB Atlas is the world's most widely available developer data platform and provides vector database capabilities that make it seamless for organizations to use their real-time operational data to power generative AI applications. Amazon Bedrock is a fully managed service from Amazon Web Services (AWS) that offers a choice of high-performing FMs from leading AI companies via a single API, along with a broad set of capabilities organizations need to build generative AI applications with security, privacy, and responsible AI. Customers across industries can now use the integration with their proprietary data to more easily create applications that use generative AI to autonomously complete complex tasks and to deliver up-to-date, accurate, and trustworthy responses to end-user requests.

"Customers of all sizes, from startups to large enterprises, are starting to take advantage of generative AI to create compelling new end-user experiences. However, many businesses remain concerned about ensuring the accuracy of the outputs from AI-powered systems while also protecting their proprietary data," said Sahir Azam, Chief Product Officer at MongoDB. "With the integration of MongoDB Atlas Vector Search and Amazon Bedrock now generally available, we're making it easier for joint MongoDB-AWS customers to use a variety of foundation models hosted in their AWS environments to build generative AI applications that can securely use their proprietary data within MongoDB Atlas to improve accuracy and provide enhanced end-user experiences."

The new integration with Amazon Bedrock allows organizations to more quickly and easily deploy generative AI applications on AWS that can act on data processed by MongoDB Atlas Vector Search to deliver more accurate, relevant, and trustworthy responses. Unlike add-on solutions that only store vector data, MongoDB Atlas Vector Search powers generative AI applications by functioning as a highly performant and scalable vector database with the added benefit of being integrated with a globally distributed operational database that can store and process all of an organization's data.

Customers can use the integration between MongoDB Atlas Vector Search and Amazon Bedrock to privately customize FMs like large language models (LLMs)—from AI21 Labs, Amazon, Anthropic, Cohere, Meta, Mistral AI, and Stability AI—with their real-time operational data by converting it into vector embeddings for use with LLMs. Using Agents for Amazon Bedrock for retrieval-augmented generation (RAG), customers can then build applications with LLMs that respond to user queries with relevant, contextualized responses—without needing to manually code. For example, a retail organization can more easily develop a generative AI application that uses autonomous agents for tasks like processing real-time inventory requests or to help personalize customer returns and exchanges by automatically suggesting in-stock merchandise based on customer feedback. Organizations can also isolate and scale their generative AI workloads independent of their core operational database with MongoDB Atlas Search Nodes to optimize cost and performance with up to 60 percent faster query times.

With fully managed capabilities, this new integration enables joint AWS and MongoDB customers to securely use generative AI with their proprietary data to its full extent throughout an organization, and to realize business value more quickly—with less operational overhead and manual work. Learn more how to get started building applications with MongoDB Atlas on AWS.

"For more than a decade, AWS and MongoDB have been helping organizations transform their businesses with their data," said Vasi Philomin, Vice President of Generative AI at AWS. "Today, tens of thousands of organizations choose Amazon Bedrock to build generative AI applications that are tailored to their specific needs. Now with MongoDB Atlas Vector Search generally available on Knowledge Bases for Amazon Bedrock, our shared customers can more easily and quickly implement retrieval augmented generation (RAG) to glean greater insights from their data."

Novo Nordisk among customers building generative AI applications with MongoDB Atlas Vector Search and Amazon Bedrock

Founded in 1923 in Denmark, Novo Nordisk is a leading global healthcare company that drives change to defeat serious chronic diseases like diabetes, obesity, rare diseases, and cardiovascular conditions. "We needed to find a solution to reduce the time it takes to prepare Clinical Study Reports so we could get new life-saving treatments and therapeutics to patients faster," said Louise Lind Skov, Head of Content Digitisation at Novo Nordisk. "Using Amazon Bedrock and MongoDB Atlas, we were able to quickly build our solution NovoScribe, and we are the first in the industry to generate complete Clinical Study Reports in minutes rather than weeks. We are doing it at scale, and with just a fraction of the resources we needed in the past. It is a game changer for healthcare around the world."

This announcement and more will be featured in the MongoDB.local NYC keynote delivered by MongoDB President and CEO Dev Ittycheria and Chief Product Officer Sahir Azam, which can be viewed today via live-stream here beginning at 10:00am ET.

About MongoDB Atlas
MongoDB Atlas is the leading multi-cloud developer data platform that accelerates and simplifies building modern applications with a highly flexible, performant, and globally distributed operational database at its core. By providing an integrated set of data and application services in a unified environment, MongoDB Atlas enables development teams to quickly build with the security, performance, and scale modern applications require. Millions of developers and tens of thousands of customers across industries—including Cisco, GE Healthcare, Intuit, Toyota Financial Services, and Verizon—rely on MongoDB Atlas every day to innovate more quickly, efficiently, and cost-effectively for virtually every use case across the enterprise. To get started with MongoDB Atlas, visit mongodb.com/atlas.

About MongoDB
Headquartered in New York, MongoDB's mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. Built by developers, for developers, MongoDB's developer data platform is a database with an integrated set of related services that allow development teams to address the growing requirements for today's wide variety of modern applications, all in a unified and consistent user experience. MongoDB has tens of thousands of customers in over 100 countries. The MongoDB database platform has been downloaded hundreds of millions of times since 2007, and there have been millions of builders trained through MongoDB University courses. 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 MongoDB's expanded collaboration with Google Cloud. 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: 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; our limited operating history; our history of losses; 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 integrate acquired businesses and technologies successfully or achieve the expected benefits of such acquisitions; 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 Annual Report on Form 10-K for the year ended January 31, 2024, filed with the SEC on March 15, 2024, 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.

MongoDB Public Relations
press@mongodb.com

[NOT APPROVED] Acoustic is a customer-obsessed marketing technology company committed to creating powerful tools that are easy to use. "At Acoustic, we empower organizations with insights about how their customers interact with their brand to help them optimize and personalize customer experiences," said John Riewerts, EVP of Engineering at Acoustic. "We've been using MongDB Atlas Stream Processing to power real-time customer insights and have had a seamless experience. Given that success, we're excited to use MongoDB Atlas as a Knowledge Base for Amazon Bedrock to enrich insights with generative AI to power everything from summarizations to agents that can automate tasks."

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SOURCE MongoDB, Inc.

FAQ

What did MongoDB announce regarding Amazon Bedrock?

MongoDB announced the general availability of MongoDB Atlas Vector Search on Knowledge Bases for Amazon Bedrock, enabling the development of generative AI-powered applications.

Who are some customers using MongoDB Atlas Vector Search and Amazon Bedrock?

Novo Nordisk is among the customers leveraging this integration to build generative AI applications for healthcare solutions.

How can organizations benefit from the integration with Amazon Bedrock?

Organizations can deploy generative AI applications on AWS more easily to deliver accurate, relevant, and trustworthy responses using data processed by MongoDB Atlas Vector Search.