MongoDB Named a Leader in the 2024 Gartner® Magic Quadrant™ for Cloud Database Management Systems

Scott Sanchez
December 23, 2024

I’m pleased to announce that MongoDB has been named a Leader in the 2024 Gartner® Magic Quadrant™ for Cloud Database Management Systems (DBMSs) for the third consecutive year. In our view, this recognition cements MongoDB’s status as the only pure-play database provider in the cloud database management system category, underscoring MongoDB’s innovation, execution, and customer-centric approach.

According to Gartner, “The cloud DBMS market remains as vibrant as ever and is transforming in important ways, especially in the use of gen AI and how DBMSs interact with other data management components. This Magic Quadrant will help data and analytics leaders make the right cloud DBMS choices in this essential market.” We believe this continued recognition by Gartner is a testament to MongoDB’s commitment to serving developers, as well as the investments we’ve made in our unified platform and integrated services.

Driving innovation for enterprises

MongoDB's mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. 2024 was a year of innovation and accolades at MongoDB, and I’m proud to share some of its highlights:

The Gartner Magic Quadrant for cloud database management systems

“Gartner defines the cloud database management systems (DBMSs) market as solutions designed to store, manipulate, and persist data, primarily delivered as Software-as-a-Service (SaaS). These systems must support transactional, analytical, and hybrid workloads while enabling enterprises to innovate across multi-cloud, hybrid, and intercloud ecosystems.”1

It’s our opinion that this recognition by Gartner is a testament to MongoDB’s strong ability to execute and support customers today, as well as MongoDB’s comprehensive product vision that positions our platform to support tomorrow's operational workloads.

What is the Magic Quadrant, and what is a Leader?

“A Gartner Magic Quadrant is a culmination of research in a specific market, giving you a wide-angle view of the relative positions of the market’s competitors.  By applying a graphical treatment and a uniform set of evaluation criteria, a Magic Quadrant helps you quickly ascertain how well technology providers are executing their stated visions and how well they are performing against Gartner’s market view.”2

According to Gartner, “Leaders execute well against their current vision and are well positioned for tomorrow.” Overall, Magic Quadrants can help you “get quickly educated about a market’s competing technology providers and their ability to deliver on what end-users require now and in the future.”

Powering innovation at scale with MongoDB Atlas

Enterprises choose MongoDB Atlas because it gives them the freedom and agility they need to succeed in a rapidly evolving digital landscape.

MongoDB Atlas’s multi-cloud architecture—including availability across Amazon Web Services, Google Cloud, and Microsoft Azure—ensures customers can design for unmatched scale and resilience. By automating functions like scaling and performance optimization, and giving them the ability to leverage industry-first capabilities like MongoDB Queryable Encryption (which allows customers to encrypt, store, and perform queries directly on data), with MongoDB Atlas customers can spend less time managing infrastructure and more time delivering experiences.

MongoDB Atlas’s integrated capabilities to support multi-modal data types and use cases—like full-text and vector search, stream processing, and data federation—accelerate innovation, helping enterprises quickly respond to market changes, power AI-driven insights, and deliver meaningful digital experiences to their end users—all without the burden of operational complexity.

Modernizing and building for the future

In our opinion, the Gartner Magic Quadrant provides organizations with a clear and accessible evaluation framework to identify solutions that fit their needs, today and tomorrow. The placement of MongoDB in the Leader quadrant for Cloud Database Management Systems—for the third year in a row!—validates the efforts MongoDB has made to help developers and organizations take advantage of their most valuable resource, their data.

I talk to MongoDB customers frequently, and many say the same thing: in today’s digital-first economy, AI-powered applications and scalable data infrastructure aren’t just advantages, they’re absolute necessities. They say that the time to act is now, and they’re looking for solutions that will help them innovate, streamline, and seize the AI-driven future. And when it comes to modernizing their operations, they consistently point to MongoDB as their go-to partner.

Begin your cloud journey with MongoDB Atlas today. Contact our sales team or register for a free account to begin building!

And to learn how MongoDB can help accelerate your AI journey, visit the MongoDB AI Applications Program page.

Footnotes

Gartner, Magic Quadrant for Cloud Database Management Systems,  Henry Cook, Ramke Ramakrishnan, et al., 18 December 2024
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

1 Gartner Peer Insights, Cloud Database Management Systems, December 2024 https://www.gartner.com/reviews/market/cloud-database-management-systems

2 Gartner Research Methodologies, Gartner Magic Quadrant, 20 December 2024 https://www.gartner.com/en/research/methodologies/magic-quadrants-research

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MongoDB’s 2024 Year in Review

It’s hard to believe that another year is almost over! 2024 was a transformative year for MongoDB, and it was marked by both innovation and releases that further our commitment to empowering customers, developers, and partners worldwide. So without further ado, let’s dive into MongoDB’s 2024 highlights. We’ll also share our executive team’s predictions of what 2025 might have in store. A look back at 2024 MongoDB 8.0: The most performant version of MongoDB ever In October we released MongoDB 8.0 , the fastest, most resilient, secure, and reliable version of MongoDB yet. Architectural optimizations in MongoDB 8.0 have significantly improved the database’s performance, with 36% faster reads and 59% higher throughput for updates. Our new architecture also makes horizontal scaling cheaper and faster. Finally, working with encrypted data is easier than ever, thanks to the addition of range queries in Queryable Encryption (which allows customers to encrypt, store, and perform queries directly on data). Whether you’re a startup building your first app, or you’re a global enterprise managing mission-critical workloads, MongoDB 8.0 offers unmatched power and flexibility, solidifying MongoDB’s place as the world’s most popular document database. Learn more about what makes 8.0 the best version of MongoDB ever on the MongoDB 8.0 page . Delivering customer value with the MongoDB AI Applications Program AI applications have become a cornerstone of modern software, and MongoDB is committed to equipping customers with the technology, tools, and support they need to succeed on their AI journey. That’s why we launched the MongoDB AI Applications Program (MAAP) in 2024, a comprehensive program designed to accelerate the development of AI applications. By offering customers resources like access to AI specialists, an ecosystem of leading AI and tech companies, and AI architectural best practices supported by integrated services, MAAP helps solve customers’ most pressing business challenges, unlocks competitive advantages, and accelerates time to value for AI investments. Overall, MAAP’s aim is to set customers on the path to AI success. Visit the MongoDB AI Applications Program page or watch our session from AWS re:Invent to learn more! Advancing AI with MongoDB Atlas Vector Search In 2024, MongoDB further cemented its role in the AI space with enhancements to MongoDB Atlas Vector Search . Recognized in 2024 (for the second consecutive year!) as one of the most loved vector databases , MongoDB continues to provide a scalable, unified, and secure platform for building cutting-edge AI use cases. Recent advancements like vector quantization in Atlas Vector Search help deliver even more value to our customers, enabling them to scale applications to billions of vectors at a lower cost. Head over to our Atlas Vector Search quick start guide to get started with Atlas Vector Search today, or visit our AI resources hub to learn more about how MongoDB can power AI applications. Search Nodes: Performance at scale Search functionality is indispensable in modern applications, and with Atlas Search Nodes, organizations can now optimize their search workloads like never before. By providing dedicated infrastructure for Atlas Search and Vector Search workloads, Search Nodes ensure high performance (e.g., a 40–60% decrease in query times), scalability, and reliability, even for the most demanding use cases. As of this year , Search Nodes are generally available across AWS, Google Cloud, and Microsoft Azure. This milestone underscores MongoDB’s commitment to delivering powerful solutions that scale alongside our customers’ needs. To learn more about Search Nodes, check out our documentation or watch our tutorial . Looking ahead: MongoDB’s 2025 predictions After the excitement of the past few years, 2025 will be defined by ensuring that technology investments deliver tangible value. Organizations remain excited about the potential AI and emerging technologies hold to solve real business challenges, but are increasingly focused on maintaining a return on investment. “Enterprises need to innovate faster than ever, but speed is no longer the only measure of success. Increasingly, organizations are laser-focused on ensuring that their technology investments directly address critical business challenges and provide clear ROI and competitive advantage—whether it’s optimizing supply chains, delivering hyper-personalized customer experiences, or scaling operations efficiently,” said Sahir Azam, Chief Product Officer at MongoDB. “In 2025, I expect to see organizations make significant strides in driving this innovation and efficiency by applying AI to more production use cases and by maturing the way they leverage their data to build compelling and differentiated customer experiences.” Indeed, we expect to see organizations make more strategic investments in emerging technologies like gen AI—innovating with a sharp focus on solving business challenges. “In 2025, we can expect the focus to shift from ‘what AI can do’ to ‘what AI should do,’ moving beyond the hype to a clearer understanding of where AI can provide real value and where human judgment is still irreplaceable,” said Tara Hernandez, VP of Developer Productivity at MongoDB. “As we advance, I think we’ll see organizations begin to adopt more selective, careful applications of AI, particularly in areas where stakes are high, such as healthcare, finance, and public safety. A refined approach to AI development will be essential—not only for producing quality results but also to build trust, ensuring these tools genuinely support human goals rather than undermining them.” With more capable, accessible application development tools and customer-focused programs like MAAP at developers’ fingertips, 2025 is an opportunity to make a data-driven impact faster than ever before. "Right now, organizations have an opportunity to leverage their data to reimagine how they do business, to more effectively adapt to a changing world, and to revolutionize our quality of life,” said Andrew Davidson, SVP of Products at MongoDB. “By harnessing our latest technologies, developers can build a foundation for a transformative future." Head over to our updates page to learn more about the new releases and updates from MongoDB in 2024. Keep an eye on our events page to learn what's to come from MongoDB in 2025!

December 19, 2024
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Using Agentic RAG to Transform Retail With MongoDB

In the competitive world of retail and ecommerce, it’s more important than ever for brands to connect with customers in meaningful, personalized ways. Shoppers today expect relevant recommendations, instant support, and unique experiences that feel tailored just for them. Enter retrieval-augmented generation (RAG) : a powerful approach that leverages generative AI and advanced search capabilities to deliver precise insights on demand. For IT decision-makers, the key challenge lies in integrating operational data with unstructured information—which can span object stores (like Amazon S3 and SharePoint), internal wikis, PDFs, Microsoft Word documents, and more. Enterprises must unlock value from curated, reliable internal data sources that often hold critical yet hard-to-access information. By combining RAG’s capabilities with these data assets, retailers can find contextually accurate information. For example, they can seamlessly surface needed information like return policies, refund processes, shipment details, and product recalls, driving operational efficiency and enhancing customer experiences. To provide the most relevant context to a large language model (LLM) , traditional RAG (which has typically relied on vector search) needs to be combined with real-time data in an operational database, the last conversation captured in a customer relationship management API call to a REST endpoint, or both. RAG has evolved to become agentic—that is, it’s capable of understanding a user inquiry and translating it to determine which path to use and which repositories to access to answer the question. MongoDB Atlas and Dataworkz provide an agentic RAG as a service solution that enables retailers to combine operational data with relevant unstructured data to create transformational experiences for their customers. MongoDB Atlas stores and unifies diverse data formats—such as customer purchases, inventory levels, and product descriptions—making them easily accessible. Dataworkz then transforms this data into vector embeddings, enabling a multistep agentic RAG pipeline to retrieve and create personalized, context-aware responses in real time. This is especially powerful in the context of customer support, product recommendations, and inventory management. When customers interact with retailers, Dataworkz dynamically retrieves real-time data from MongoDB Atlas, and, where needed, combines it with unstructured information to generate personalized AI responses, enhancing the customer experience. This architecture improves engagement, optimizes inventory, and provides scalable, adaptable AI capabilities, ultimately driving a more efficient and competitive retail operation. Reasons for using MongoDB Atlas and Dataworkz MongoDB Atlas and Dataworkz work together to deliver agentic RAG as a service for a smarter, more responsive customer experience. Here’s a quick breakdown of how: Vector embeddings and smart search: The Dataworkz RAG builder enables anyone to build sophisticated retrieval mechanisms that turn words, phrases, or even customer behaviors into vector embeddings—essentially, numbers that capture their meaning in a way that’s easy for AI to understand—and store them in MongoDB Atlas. This makes it possible to search for content based on meaning rather than exact wording, so search results are more accurate and relevant. Scalable, reliable performance: MongoDB Atlas’s cloud-based, distributed setup is built to handle high-traffic retail environments, minimizing disruptions during peak shopping times. Deep context with Dataworkz’s agentic RAG as a service: Retailers can build agentic workflows powered by RAG pipelines that combine lexical and semantic search with knowledge graphs to fetch the most relevant data from unstructured operational and analytical data sources before generating AI responses. This combination gives ecommerce brands the power to personalize experiences at a vastly larger scale. Figure 1: Reference architecture for customer support chatbots with Dataworkz and MongoDB Atlas Retail e-commerce use cases So how does this all work in practice? Here are some real-world examples of how MongoDB Atlas and Dataworkz are helping ecommerce brands create standout experiences. Building smarter customer-support chatbots Today’s shoppers want quick, accurate answers, and RAG makes this possible. When a customer asks a chatbot, “Where’s my order?” RAG enables the bot to pull the latest order and shipping details stored in MongoDB Atlas. Even if the question is phrased differently—say, “I need my order status”—the RAG-powered vector search can interpret the intent and fetch the correct response. As a result, the customer gets the help they need without waiting on hold or navigating complex menus. Personalizing product recommendations Imagine a customer who’s shown interest in eco-friendly products. With MongoDB Atlas’s vector embeddings, a RAG-powered system can identify this preference and adjust recommendations accordingly. So when the customer returns, they see suggestions that match their style—like organic cotton clothing or sustainably sourced kitchenware. This kind of recommendation feels relevant and thoughtful, making the shopping experience more enjoyable and increasing the chances of a purchase. Creating dynamic marketing content Marketing thrives on fresh, relevant content. With MongoDB Atlas managing product data and Dataworkz generating personalized messages, brands can send out dynamic promotions that truly resonate. For example, a customer who browsed outdoor gear might receive a curated email with top-rated hiking boots or seasonal discounts on camping equipment. This kind of targeted messaging feels personal, not pushy, building stronger customer loyalty. Enhancing site search experiences Traditional e-commerce searches often rely on exact keyword matches, which can lead to frustrating dead ends. But with MongoDB Atlas Vector Search and Dataworkz’s agentic RAG, search can be much smarter. For example, if a customer searches for “lightweight travel shoes,” the system understands that they’re looking for comfortable, portable footwear for travel, even if none of the product listings contain those exact words. This makes shopping smoother and more intuitive and less of a guessing game. Understanding trends in customer sentiment For e-commerce brands, understanding how customers feel can drive meaningful improvements. With RAG, brands can analyze reviews, social media comments, and support interactions to capture sentiment trends in MongoDB Atlas. Imagine a brand noticing a spike in mentions of “too small” in product reviews for a new shoe release—this insight lets them quickly adjust sizing info on the product page or update their stock. It’s a proactive approach that shows customers they’re being heard. Interactions that meet customers where they are In essence, MongoDB Atlas and Dataworkz’s RAG models enable retailers to make e-commerce personalization and responsiveness smarter, more efficient, and easier to scale. Together, they help retailers deliver exactly what customers are looking for—whether it’s a personalized recommendation, a quick answer from a chatbot, or just a better search experience. In the end, it’s about meeting customers where they are, with the information and recommendations they need. With MongoDB and Dataworkz, e-commerce brands can create that kind of connection—making shopping easier, more enjoyable, and ultimately more memorable. Learn more about Dataworkz on MongoDB by visiting dataworkz.com . The Dataworkz free tier is powered by MongoDB Atlas Vector Search .

December 23, 2024