MongoDB Sees AI, Atlas and Enterprise Modernization Fueling Growth

MongoDB Sees AI, Atlas and Enterprise Modernization Fueling Growth

MongoDB reported revenue growth above 30% in fiscal Q2 and is targeting durable growth alongside improving operating margins and cash flow. The company sees major opportunities in enterprise modernization and AI-native customers, particularly as businesses migrate from legacy relational databases and adopt MongoDB for scalable, real-time AI workloads. Atlas remains a key growth driver: MongoDB added approximately 2,900 customers during the quarter, with about 99% choosing Atlas, while new embedding and managed Model Context Protocol tools are increasing developer engagement.

MongoDB NASDAQ: MDB CEO CJ Desai outlined the company’s focus on enterprise modernization, AI-native customers and product enhancements during Citi’s TMT Conference, following what he described as a strong fiscal second-quarter report. Desai said MongoDB reported total revenue growth above 30% in the quarter, marking what he called the company’s first time reaching that growth level “in a long time.” He also said the company is pursuing durable growth alongside increasing profitability, citing operating-margin and cash-flow results released the prior week.

Enterprise modernization and global demand Desai said MongoDB is increasingly becoming a standard platform for modern database deployments at large enterprises, particularly in financial services and insurance, where companies are managing unstructured data and moving toward multicloud environments.

He also cited early public-sector demand and international opportunities. In Europe, he said sovereign-cloud requirements have supported demand for MongoDB’s ability to be self-managed and deployed across environments. In India, Desai pointed to digital-native companies, including Zomato, that are building on MongoDB. The company is also putting greater emphasis on executive-level relationships with chief information officers, chief technology officers, chief data officers and enterprise architects.

Desai said some large customers spending seven- or eight-figure annual recurring revenue amounts had not fully recognized the extent of their MongoDB usage across mission-critical workloads. Those conversations are also opening opportunities around AI readiness and modernization, according to Desai. He said MongoDB is discussing ways to help enterprises migrate certain workloads from aging relational databases, with the goal of reducing migration projects from years to months or from months to weeks.

AI-native customers and frontier labs Desai said many AI-native companies initially used PostgreSQL or hyperscaler-provided database services before encountering scaling constraints and moving to MongoDB. He said companies such as Mercor, ElevenLabs and Harvey have become examples of businesses using MongoDB as a foundational operational data layer. He identified three product attributes that he believes are driving adoption for AI workloads: MongoDB’s JSON-native document model, which Desai said aligns with the structure and speed of AI development; Its ability to scale out for large workloads; and Its ability to run across cloud environments, including configurations spanning multiple hyperscalers.

For AI labs, use cases vary, Desai said. One example involves using MongoDB as a conversational memory layer for agent interactions. At ElevenLabs, he said Mon...