MongoDB Sees AI, Modernization and Self-Managed Demand Fueling Growth

Key Points AI and modernization are driving demand for MongoDB’s database platform, with large enterprises increasingly evaluating it for AI architectures, new applications and data-environment upgrades. Atlas growth is being supported by enterprise expansion, cross-selling products such as Vector Search and embedding capabilities, and early adoption among AI-native companies. MongoDB also added 2,900 net new customers in the second quarter. Self-managed demand is accelerating : Enterprise Advanced grew 36%, prompting MongoDB to raise its full-year growth guidance to 11%. Customers are seeking self-managed deployments because of cloud capacity, cost, data-sovereignty and in-house AI infrastructure considerations. Five stocks to consider instead of MongoDB . MarketBeat Week in Review – 09/07 - 09/11 MongoDB NASDAQ: MDB executives said customer demand for its database platform is being supported by enterprise modernization efforts, AI-related workloads and growing interest in self-managed deployments, while the company works to raise awareness among senior decision-makers. CJ Desai, MongoDB’s president and CEO, said that after nearly a year in the role and roughly 10 to 12 customer meetings per week, he has found that customers view MongoDB as a modern database capable of supporting large-scale workloads. He said one North American Fortune 100 company had made MongoDB the default standard for new applications unless another technology is justified. Get MongoDB alerts: Sign Up Could Snowflake's Big Quarter Be a Sign of More to Come? Desai said the company sees a meaningful modernization opportunity as large enterprises prepare their data environments for AI. However, he said MongoDB’s awareness among C-suite executives has historically been limited, even as developers have adopted the platform. He said senior technology leaders are increasingly making top-down decisions about AI architectures and data platforms. “Sales cycles, when you go top-down, tend to be always long,” Desai said. “But it is early, but it’s working.” He cited discussions with telecommunications, retail and financial-services companies around standardizing on MongoDB and deploying new workloads, including AI applications. Atlas Growth and AI Workloads AI Token Costs Are Changing the Hardware vs. Software Debate Mike Berry, MongoDB’s chief financial officer, said Atlas growth has been driven primarily by the company’s increased focus on large enterprises, expansion within existing customer accounts and cross-selling products such as Vector Search and embedding capabilities. He said nearly half of MongoDB’s large customers use multiple products, though the revenue contribution from those products remains lower than their adoption rate. Berry also attributed the durability of Atlas growth to platform reliability and performance, which he said have helped limit customer churn and contraction. He said the company has not seen a major change in the types of workloads it is winning compared with prior periods. Executives characterized AI demand as early but promising across AI-native companies, frontier labs and large enterprises. Desai cited ElevenLabs as an example of an AI-focused customer that moved to MongoDB after encountering scaling issues with another database service. He said the company benefited from having search and vector search integrated with its operational data layer, reducing the need to move data between systems. Desai said MongoDB added 2,900 net new customers in the second quarter and expects some of those customers to become more meaningful Atlas users over time. He also said Voyage AI, which provides embedding models, is attracting customers through coding agents such as Claude Code and Codex. Developer Discovery a...