India—27th June 2023 MongoDB, Inc. (NASDAQ: MDB) today at its developer conference MongoDB.local NYC announced five new products and features for its industry-leading developer data platform, MongoDB Atlas, that make it significantly faster and easier for customers to build modern applications, for any workload or use case. The new products and features include: generative AI capabilities with MongoDB Atlas Vector Search for highly relevant information retrieval and personalization, MongoDB Atlas Search Nodes for dedicated resources with search workloads at enterprise scale, MongoDB Atlas Stream Processing for high-velocity streams of complex data, significant scaling and efficiency improvements for MongoDB Time Series collections, and new capabilities using MongoDB Atlas Data Federation for querying data and isolating workloads on Microsoft Azure. Together, these new features for MongoDB Atlas enable businesses to dramatically improve operational efficiency and speed up their pace of innovation by standardizing many types of workloads on a single developer data platform across the enterprise. To learn more about MongoDB Atlas, visit

Organizations today face an inflection point with the explosion of new technology like generative AI and the exponential growth of different types of data being generated in real-time. Organizations of all shapes and sizes want to be able to take advantage of new technology like large language models (LLMs) and process streams of real-time data to provide highly engaging end-user experiences and take autonomous action on that data more quickly to build new classes of applications. The choice of a database is fundamental to ensuring not only the success of an application but also how fast it can be built, deployed, and continually updated. Organizations want a unified, fully managed platform for their developer teams that makes it easy to build, deploy, and scale modern applications seamlessly.

MongoDB Atlas is the leading multi-cloud developer data platform that accelerates and simplifies building with data. MongoDB Atlas provides an integrated set of data and application services in a unified environment to enable developer teams to quickly build with the capabilities, performance, and scale modern applications require. Tens of thousands of customers and millions of developers rely on MongoDB Atlas every day so they can innovate more quickly, efficiently, and cost-effectively with modern applications for virtually every use case across an organization. As the use of MongoDB Atlas has rapidly grown, customers have asked for even more integrated capabilities to meet the growing demands of their businesses and end-users, and MongoDB is meeting that demand:

  • Integrate AI-powered search and personalization into applications on MongoDB Atlas: MongoDB Atlas Vector Search enables organizations to more quickly and easily build next-generation applications that use generative AI to dramatically enhance end-user experiences and improve productivity across teams. Generative AI is creating a once-in-a-generation shift in how end-users interact with applications. Organizations want to be able to use technology based on generative AI—like LLMs—but find it difficult to integrate into applications because most existing technology stacks lack the flexibility to store and process different types of data. For example, LLMs require data in the form of vectors, which are geometric representations of data (e.g., text, images, and audio). These types of AI models measure the similarity between vectors to probabilistically construct sentences from prompts, generate images from captions, or return search results that are more accurate and contain greater context than traditional search engines. To store vectors so LLMs can use them, some organizations have begun using specialized databases. However, single-purpose databases for use cases like vector stores or time series applications are often bolted on to existing technology stacks, resulting in more administrative complexity, an educational burden on developers, and longer time to value. With MongoDB Atlas Vector Search, customers can power a range of new workloads from semantic search with text to image search and comparison to highly personalized product recommendations using a single, familiar, unified platform across an entire organization—all with minimal developer friction. MongoDB Atlas Vector Search also allows customers to augment the capabilities of pre-trained generative AI models easily and securely with their own data to provide memory that creates more accurate and relevant results for specific domains or use cases. Because MongoDB Atlas uses a highly flexible and scalable document-data model that supports data of virtually any type, customers can also easily manage the outputs of LLMs using MongoDB Atlas for use cases like caching common search requests for faster results at less cost. MongoDB Atlas Vector Search is integrated with the open source LangChain and LlamaIndex frameworks with tools for accessing and managing LLMs for a wide variety of applications. Customers can use these frameworks to access LLMs from MongoDB Partners (e.g., AWS, Databricks, Google Cloud, Microsoft Azure, MindsDB) and model providers (e.g., Anthropic, Hugging Face, and OpenAI) to generate vector embeddings and build AI-powered applications on MongoDB Atlas. To learn more, visit
  • Isolate and scale search workloads on MongoDB Atlas: MongoDB Atlas Search Nodes provide dedicated infrastructure for customers to scale search workloads independent of their database, enabling workload isolation, resource optimization, and better performance at scale. Today, customers use MongoDB Atlas Search to quickly and easily build relevance-based search capabilities directly into applications for a variety of use cases (e.g., personalized recommendations, product catalog and content search, multimedia management, and geospatial applications) using a seamlessly integrated developer experience. However, customers that have scaled their search workloads with MongoDB Atlas Search have asked for the ability to access and control dedicated resources to run search workloads independent of the database. With MongoDB Atlas Search Nodes, customers can now use dedicated infrastructure to seamlessly scale their MongoDB Atlas Vector Search and MongoDB Atlas Search workloads with greater flexibility and control to provide end-users the best relevance-based and AI-powered search experiences. To learn more, visit
  • Process high-velocity streams of complex data with MongoDB Atlas: MongoDB Atlas Stream Processing transforms the way organizations can process streaming data to engage end-users and speed up operations. Real-time streaming data (e.g., data coming from IoT devices, end-user browsing behaviors, inventory feeds) is critical to modern applications because it gives organizations the ability to engage end-users with real-time experiences as behaviors change and optimize business operations as conditions change. Streaming data is rich, heterogeneous, and constantly changing—requiring a flexible and scalable data model that can quickly evolve as conditions change. For this reason, rigid and inflexible relational data schemas are less ideal for working with real-time data that can keep up with ground truth. To incorporate streaming data into applications today, many developer teams must use specialized programming languages, libraries, application programming interfaces (APIs), and drivers bolted onto existing technology stacks. This creates a complex and fragmented development experience with teams having to learn how to use different tools for ever-changing use cases, leading to longer development cycles and increased costs. As a result, developers working with streaming data often face a level of complexity that leads to a slower pace of innovation and a risk to the business of falling behind the competition. With MongoDB Atlas Stream Processing, customers now have a single interface to easily extract insights from high-velocity and high-volume streaming data. MongoDB Atlas Stream Processing works with any type of data, and with its flexible data model, enables customers to build highly engaging applications that can analyze data in real time to adjust application behavior and inform business operations (e.g., highly personalized promotional offers, real-time inventory management, fraud prevention). MongoDB’s flexible data model can also be easily changed over time as needs evolve to ensure applications are consistently providing an optimized experience for end-users and making business operations more efficient. With MongoDB Atlas Stream Processing, organizations can now do significantly more with their data in less time and with no heavy lifting. To learn more, visit
  • Scale with greater flexibility using MongoDB Time Series collections: Workload scalability and data flexibility for MongoDB Time Series collections now make it easier to handle enterprise-scale time series workloads and provide the option to modify data that has already been ingested. Time series workloads can grow quickly in use cases where, for example, millions of devices are sending data to a database for processing. Once data is ingested, time series databases typically do not allow that data to be modified. If there was an error in the data before it was ingested into the database, that means future analyses would be flawed. Further, because time series data evolves as real-world conditions change, a flexible data model is required to ensure it can effectively be put to use with the ability to quickly map new relationships between data, generate forecasts, and update the business logic of applications or make operations more efficient. Now, MongoDB Time Series collections provide scaling enhancements and the ability to modify time series data—giving customers more control over their data at scale. These new capabilities result in better storage efficiency and improved query speeds for the most demanding time series workloads while helping customers meet strict data governance requirements. Together, these new enhancements to MongoDB Time Series collections give customers the scalability and flexibility required for mission-critical time series workloads. To get started, visit
  • Tier and query data on Microsoft Azure with MongoDB Atlas Online Archive and Atlas Data Federation: New multi-cloud options bring Microsoft Azure support to MongoDB Atlas Online Archive and Atlas Data Federation in addition to Amazon Web Services (AWS). Customers today use MongoDB Atlas Online Archive to automatically tier Atlas databases to the most cost-effective cloud object storage option while retaining the ability to query. By adding support for Microsoft Azure, customers can now more easily keep their entire workloads in the same cloud. Atlas Data Federation provides a seamless way to read and write data from Atlas databases and cloud object stores. This dramatically simplifies how customers can generate datasets from Atlas to feed downstream applications and systems that leverage cloud storage. Now, by adding support for Microsoft Azure Blob Storage, customers can work with Azure data in addition to AWS. To get started, visit

“The new MongoDB Atlas capabilities announced today are an answer to the feedback we get from customers every day—they love that their teams are able to quickly build and innovate with MongoDB Atlas and want to be able to do even more with it across the enterprise,” said Dev Ittycheria, President and CEO at MongoDB. “With the new features we’re launching today, we’re further supporting customers running the largest, most demanding, mission-critical workloads that require continually increasing scalability and flexibility, so they can unleash the power of software and data with next-generation applications that will drive the future of their businesses using a single developer data platform with MongoDB Atlas.”

Beamable is a technology company that provides a full-stack, live operations platform that allows game developers to both build and operate live games with 32 games currently live and dozens more in active development. “We built our platform on MongoDB Atlas due to its workload versatility, and ability to easily scale vertically and horizontally,” said Ali El Rhermoul, CTO at Beamable. “We’ve been evaluating MongoDB Atlas Vector Search capabilities in conjunction with OpenAI Embeddings for use in generative AI applications, and were impressed by how trivial it was to set up and use. This means we and our game developer community can build novel AI-powered experiences on Beamable, with familiar technology and without expanding the technology stack.”

Pureinsights is an independent search technology and services company that partners with MongoDB to help customers deploy search-based applications on MongoDB Atlas. “We’ve been working with Atlas Vector Search while in private preview and are excited to be partnering with MongoDB to help enable this new capability for our customers,” said Kamran Khan, CEO at Pureinsights. “Being able to store and use vectors within the MongoDB Atlas platform powers new workloads and exciting AI-powered experiences that users want, like semantic search and generative answers.”

Anywhere is the parent company of some of the world’s leading real estate brokerage brands and service businesses. “Our development teams were spending too much time doing undifferentiated work managing our previous search solution, and we are currently rolling out our new solution powered by MongoDB Atlas and Atlas Search to our brand portfolio, which includes Better Homes and Gardens Real Estate, CENTURY 21, Coldwell Banker, Corcoran, ERA, and Sotheby’s International Realty,” said Damian Ng, Senior Vice President of Technology at Anywhere Real Estate. “Atlas Search allows us to ingest data from hundreds of Multiple Listing Services sources, aggregate the data, and provide customers with a search solution that efficiently delivers accurate and up-to-date information. Since implementing Atlas Search, we’ve observed a 60 percent improvement in response time for search results, and we’re excited that MongoDB is decoupling its architecture to have dedicated nodes for Atlas Search so we can have even greater flexibility and control with our search workloads.”

Hootsuite is a global leader in social media management that powers social media for brands and organizations around the world, from the smallest businesses to the largest enterprises. “Using Time Series collections with MongoDB Atlas, we were able to build a new feature that processes and stores a high volume of streaming data without ballooning our storage costs,” said Chris Martin, Senior Software Developer at Hootsuite. “It also saved us from provisioning and maintaining a separate database built specifically for the purpose.”