MongoDB expands collaboration with Google Cloud

MongoDB at Google Cloud Next ‘24 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).

“MongoDB’s strong partnership with Google Cloud is the result of our close collaboration to meet the unique demands of organisations across industries,” said Alan Chhabra, Executive Vice President of Partners at MongoDB. “Being awarded Google Cloud Partner of the Year – Marketplace is a testament to our commitment to providing the best solutions for customers building modern applications, while operating under the highest levels of security and data privacy requirements. We look forward to continuing to grow our partnership as more customers choose MongoDB and Google Cloud to enhance their businesses with new classes of generative AI applications.”

“Customers continue to tell us they want world-class generative AI support built into the leading tools they already use, such as MongoDB.” said Stephen Orban, Vice President of Migrations, ISVs, and Marketplace at Google Cloud. “MongoDB and Google Cloud have already helped thousands of joint customers successfully build and deploy applications with MongoDB Atlas on Google Cloud. We’re excited to continue to build on this strategic partnership and provide even more ways for developers to quickly and effectively create and enhance applications with generative AI, including access to leading models through Vertex AI and automated data pipelines with BigQuery.”

Partnered since 2018, MongoDB and Google Cloud have helped thousands of joint customers—including Keller Williams, Powerledger, and Ulta—adopt cloud-native data strategies to modernise how they run their organisations 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, optimise costs, and reduce query times by up to 60 percent. For example, a financial services company running a high-traffic application during tax season can use dedicated infrastructure with Atlas Search Nodes to optimise performance independent of their database by isolating and scaling the generative AI portion of the workload. With this capability, the company could scale a knowledge retrieval workload that uses MongoDB Atlas Vector Search for AI-powered agents that autonomously take action on behalf of end users—without having to resize their entire database. 
  
• 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 generative AI models such as 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. This allows developers to more easily augment LLMs with an organisation’s real-time operational data to build applications that provide context-aware and highly personalised end-user experiences that are more accurate, up to date, and trustworthy—all with less complexity. Developers can also use the new extension to input natural language in the Vertex AI console to automatically generate queries for manipulating data and performing database operations (e.g., create, read, update, delete) on data stored in MongoDB Atlas for a more seamless experience.

• 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. With a new integration for Spark stored procedures with BigQuery, customers can better automate, optimise, and reuse data processing workflows between BigQuery and MongoDB Atlas for analytics, BI, and end-user applications. For example, customers can automate pipelines that combine and transform real-time operational data stored in MongoDB Atlas with analytical data in BigQuery and send it to Vertex AI to create new types of end-user application experiences.
  
• Enrich data from the factory floor with real-time application data to optimise manufacturing and supply chain operations: Tens of thousands of organisations rely on MongoDB Atlas to securely store, process, and manage real-time application data of diverse types with high performance and scale. Manufacturers today want to modernise 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. However, many organisations are unable to transform their operations because they still rely on legacy technologies that are difficult to replace and cost-prohibitive to modernise. With a new technical integration between MongoDB Atlas and Google Cloud Manufacturing Data Engine, manufacturers can now more easily combine and transform data from across their organisations to automate processes and optimise operations with modern, real-time applications.
• Easily build and deploy applications that provide modern shopping experiences with composable commerce capabilities: Retail organisations are at the forefront of inventing new customer experiences with personalisation and automation. However, building applications that support these types of experiences at scale can be cumbersome and complex. To address these challenges, MongoDB is joining Google Cloud’s Industry Value Network (IVN) partner program, an initiative that streamlines the development of differentiated end-to-end solutions across industries through collaboration with system integrator partners to accelerate innovation. Beginning with a new solution for retailers, customers can now take advantage of MongoDB Atlas on Google Cloud using the Integrated Commerce Network from Kin + Carta, a digital transformation consultancy, to deploy a modern commerce architecture to meet their unique business needs and provide customers with highly engaging shopping experiences.
  
• Run highly sensitive workloads in a tightly controlled and secured environment: Governments, public sector organisations, and enterprises in regulated industries often struggle to modernise their operations because of the highly sensitive nature of their data. As a result, these organisations face limited choices when running workloads. With MongoDB EA on GDC, organisations can now build, deploy, and scale applications in an air-gapped environment without needing to connect to Google Cloud or the public internet. MongoDB is among the first software providers to offer a validated solution for the new Google Cloud Ready—Distributed Cloud program, a marketplace that provides tailored integrations to support use cases for customers with highly sensitive workloads. GDC enables governments, public sector organisations, and regulated enterprises to address strict data residency and security requirements, and combined with MongoDB EA, these organisations can now modernise all of their operations with the flexibility needed to securely deploy innovative applications and features while protecting sensitive data. 

MongoDB and Google Cloud joint customers and partners welcome expanded partnership 

Founded in 2009, Rent the Runway is disrupting the trillion-dollar fashion industry and changing the way women get dressed through the Closet in the Cloud, the world’s first and largest shared designer closet. “MongoDB Atlas on Google Cloud is fantastic because it provides the whole set of infrastructure, which we don’t have to take care of, so we can focus on our solutions and innovations,” said Mike Liberant, Senior Director of Software Engineering at Rent the Runway. “For example, MongoDB Atlas enables us to process our garments more quickly and accurately without human error, which ultimately means our customers get a better, faster service to rent more and spend less. MongoDB and Google Cloud have been great partners, providing the services and tools where we require them.”

Kin + Carta, a global digital transformation consultancy, helps companies modernise their business to better address the evolving needs of their customers. “Through the Integrated Commerce Network, we’re showcasing the best-in-breed software partners who together make building an end-to-end commerce solution much easier,” said Tara Catalano, National Google Cloud Alliance Lead at Kin + Carta. “MongoDB’s connectors and integrations with both BigQuery and Vertex AI make it a compelling choice for customers who want flexibility and optionality in building the best composable solution for their needs, as well as positioning them well to seize the opportunity with generative AI.”