MongoDB(MDB)

Nearly a decade ago, I joined MongoDB as a Senior Product Manager to help build the company’s new cloud product, MongoDB Atlas. Our customers had been telling us they wanted to bring MongoDB’s familiar developer experience to the cloud, with the reliability and confidence teams needed to run in production. Atlas was our answer. Today, we’re celebrating 10 years of MongoDB Atlas, the generational data platform for AI applications, and the customers who pushed us to build it. Atlas was shaped in close conversation with those customers and scaled alongside them every step of the way. Today, more than 250,000 builders get started on Atlas every month. Atlas serves more than three trillion queries a day (a roughly threefold increase just since 2023!), and represents 75% of MongoDB’s revenue. Those numbers reflect something more important than growth: the trust builders and customers have placed in us to scale their businesses. That trust was earned by listening closely. Every major capabili

The enterprise adoption of artificial intelligence has reached an inflection point. Organizations are rapidly moving into the era of agentic AI, autonomous systems capable of executing complex reasoning and making operational decisions independently. Yet as executives attempt to transition agents from sandbox environments into mission-critical production channels, they inevitably collide with an AI trust gap. Unlike traditional applications, agentic solutions interpret intent and take autonomous action on behalf of your business. Traditional IT tools are not designed to manage dynamic solutions. To scale securely, organizations must deploy a proactive control plane that evaluates an agent's logic and employs strict governance. In this article, we outline a four-step approach for building safety and optimization into agentic solutions. The approach outlines a broad framework that can be tailored to an organization’s specific needs. The 4-step framework for agentic trust To close the AI

There’s a common theme to the conversations I’ve been having with AI teams lately: change. Constant, head-spinning change. Teams across industries are evaluating and re-evaluating model providers, agent frameworks, and harnesses on a continuous basis. At MongoDB, we believe that your choice of technology partner—specifically, your data platform—should simplify how you build with AI. It should deliver performance at scale, enable you to build and run anywhere, and it should allow you to choose your own providers and frameworks. This is exactly what MongoDB offers, and it’s why more than 67,000 customers rely on us for their most important applications. The organizations seeing the most AI success are the ones whose technology stacks are set up for the current pace of change. For example, DevRev’s AgentOS platform is powered by MongoDB Atlas. AgentOS handles billions of requests each month, for everything from AI-assisted insights and analytics to internal communications and development.

Retail supply chains are not a back-office logistics function; they are a high-stakes, board-level concern. Imagine learning suddenly that shipment rerouting surcharges have doubled due to new regional escalations; the impact on competitive differentiation and consumer trust is immediate. As a result, a long-standing focus on linear efficiency and lean inventory is being disrupted by a mandate for resilience and AI-driven responsiveness. To survive, retailers must move beyond the rigidity of legacy systems and embrace an AI-ready data platform that can pivot as fast as headlines change. Indeed, a 2026 study by KPMG reported that businesses are establishing new performance metrics, centered around post-disruption recovery time, supplier diversification, sourcing agility, revenue growth from improved experiences, cost savings, and employee engagement. Now, retailers are modernizing their supplier management capabilities. An effective supplier management application that boosts visibility

Quarterly Report filed 2026-05-29

Material Event filed 2026-05-28