MongoDB introduces new capabilities

MongoDB has introduced MongoDB Atlas Vector Search and MongoDB Atlas Search Nodes to make it faster and easier for organizations to securely build, deploy, and scale next-generation applications at less cost. MongoDB Atlas Vector Search simplifies bringing generative AI and semantic search capabilities into real-time applications for highly engaging and customized end-user experiences using an organization’s operational data. MongoDB Atlas Search Nodes provide dedicated infrastructure for applications that use generative AI and relevance-based search to scale workloads independent of the database and manage high-throughput use cases with greater flexibility, performance, and efficiency. Together, these capabilities on MongoDB Atlas provide organizations with the required foundation to seamlessly build, deploy, and scale applications that take advantage of generative AI and robust search capabilities with greater operational efficiency and ease of use. To get started with MongoDB Atlas, visit mongodb.com/atlas.

“Customers of all sizes from startups to large enterprises around the world tell us they want to take advantage of generative AI and relevance-based search to build next-generation applications that reimagine how businesses find ways to deeply personalize engagement with their customers, drive increased efficiency through automation, and propel new product development. But these customers know that complexity is the enemy of speed, and the choice of a database is fundamental to ensuring not just the success of an application but also how fast it can be built, deployed, and continually updated with the flexibility and scale needed to meet shifting end-user demands,” said Sahir Azam, Chief Product Officer at MongoDB. “With the general availability of MongoDB Atlas Vector Search and MongoDB Atlas Search Nodes, we’re making it even easier for customers to use a unified, fully managed developer data platform to seamlessly build, deploy, and scale modern applications and provide end users with the types of personalized, AI-powered experiences that save them time and keep them engaged.”

As the use of MongoDB Atlas as an integrated developer data platform has rapidly grown, and more customers want to take advantage of generative AI, they have asked for more even integrated capabilities to meet the shifting demands of their businesses and end users—and MongoDB is meeting that demand:

• Integrate AI-powered capabilities into applications with MongoDB Atlas Vector Search: Unlike an add-on solution that only stores vector data, MongoDB Atlas Vector Search powers generative AI applications by functioning as a highly performant and scalable vector database with the added benefits of being integrated with a globally distributed operational database that can store and process all of an organization’s data. This allows developers to use a single API to more easily build generative AI applications for virtually any type of workload across major cloud providers without the complexity of unnecessary data duplication and synchronization that bolt-on vector databases require. MongoDB Atlas Vector Search allows customers to easily and securely use retrieval-augmented generation (RAG) with pre-trained foundation models (FMs) to leverage their own up-to-date data for intelligent applications. As a result, applications built with MongoDB Atlas Vector Search can provide more accurate and relevant responses for specific domains and AI-powered use cases without the complex and tedious work of training and fine-tuning FMs or tacking on a separate database to store and process vector data.

With the general availability of MongoDB Atlas Vector Search, customers can quickly build, deploy, and scale AI-powered features from semantic search to image comparison to highly personalized recommendations using a single, familiar, unified platform with minimal developer friction. Because MongoDB Atlas uses a flexible and scalable document-based data model that supports virtually any type of data, customers can easily combine a breadth of queries for vector data, analytical aggregations, text-based search, geospatial data, and time series data unavailable with other solutions to augment RAG and further refine responses to end-user requests. For example, an end-user could request, “Find real estate listings with houses that look like this image, were built in the last five years, and are in an area within seven miles north of downtown Seattle with top-rated schools and walking distance to parks,” and an application running on MongoDB Atlas can quickly and seamlessly provide an FM the right data to produce accurate results—without the complexity of processing separate queries across multiple data stores that can increase response times and degrade end-user experiences. To get started, visit mongodb.com/products/platform/atlas-vector-search.

• Isolate and scale generative AI and search workloads on MongoDB Atlas: Now generally available, MongoDB Atlas Search Nodes provide dedicated infrastructure for customers to manage generative AI and relevance-based search workloads that use MongoDB Atlas Vector Search and MongoDB Atlas Search independent of the core operational nodes of their database—enabling workload isolation, cost optimization, and better performance at scale. For example, a retailer running promotions leading up to a holiday shopping event may want to isolate and scale workloads for AI-powered chatbots and relevance-based search in specific geographical regions independent of their global operational database to optimize performance.

With MongoDB Atlas Search Nodes, customers can improve performance by reducing query times by up to 60 percent to provide end users optimized AI-powered and relevance-based search experiences using their operational data with a single API, all with less complexity.