MongoDB, Inc. announced an expanded collaboration with Google Cloud to make it even easier and more cost-effective to build, scale, and deploy generative AI applications using MongoDB Atlas Vector Search and Vertex AI from Google Cloud, along with additional support for data processing with BigQuery. The companies are also collaborating on new industry solutions for retail and manufacturing, with deeper product integrations and solutions to provide a seamless development environment for creating engaging shopping experiences and data-driven applications for smart factories. For customers looking to run workloads that use highly sensitive data, MongoDB Enterprise Advanced (EA) is now available on Google Distributed Cloud (GDC).

Partnered since 2018, MongoDB and Google Cloud have helped thousands of joint customers?including Keller Williams, Powerledger, Rent the Runway, and Ulta?adopt cloud-native data strategies to modernize how they run their organizations and serve end users. The expanded collaboration between MongoDB and Google Cloud now allows customers to: Seamlessly isolate and scale generative AI applications for high performance and efficiency: MongoDB Atlas Search Nodes?now generally available on Google Cloud?provide dedicated infrastructure for generative AI and relevance-based search workloads that use MongoDB Atlas Vector Search and MongoDB Atlas Search. MongoDB Atlas Search Nodes are independent of core operational database nodes and allow customers to isolate workloads, optimize costs, and reduce query times by up to 60%.

Streamline building generative AI applications with leading foundation models: MongoDB Atlas Vector Search has provided an integration with Vertex AI since last year to give developers more choice of managed foundation models to build generative AI applications. Now, with a deepened integration, developers can use a dedicated Vertex AI extension to make it even easier to work with large language models (LLMs)?from Anthropic, Google Cloud, Meta, Mistral, and more?without having to transform data or manage data pipelines between MongoDB Atlas and Google Cloud. Enhance analytical workloads with automated pipelines for operational data: BigQuery is a serverless, scalable, and cost-effective enterprise data warehouse that works across clouds for analytics, business intelligence (BI), and machine learning workloads.

Customers currently use bi-directional sync between BigQuery and MongoDB Atlas to enhance their analytical workloads with real-time operational data or to easily provide end-user applications access to historical enterprise data. Enrich data from the factory floor with real-time application data to optimize manufacturing and supply chain operations: Tens of thousands of organizations rely on MongoDB Atlas to securely store, process, and manage real-time application data of diverse types with high performance and scale. Manufacturers want to modernize their operations by combining data from many sources like factory equipment sensors, end-user applications, and enterprise resource planning systems to automate decision-making and run more efficiently.

Easily build and deploy applications that provide modern shopping experiences with composable commerce capabilities: Retail organizations are at the forefront of inventing new customer experiences with personalization and automation. However, building applications that support these types of experiences at scale can be cumbersome and complex. Run highly sensitive workloads in a tightly controlled and secured environment: Governments, public sector organizations, and enterprises in regulated industries often struggle to modernize their operations because of their data's high sensitivity.