MongoDB delivers strong Q2, outlook

MongoDB reported strong fiscal second quarter results with revenue growth of 30%.

The company reported second quarter earnings of $40.9 million, or 50 cents a share, on revenue of $771.8 million, up 30% from a year ago. Non-GAAP earnings were $1.90 a share.

Wall Street was expecting MongoDB to report non-GAAP earnings of $1.61 a share on revenue of $734.4 million.

• MongoDB launches Voyage AI, search tools aimed at on-prem, private cloud deployments
• Constellation ShortList™ Next-Gen Databases: RDBMS for On-Premises
• Constellation ShortList™ Hybrid-Cloud and MultiCloud NoSQL Databases

CEO CJ Desai said the company's performance was driven by AI workloads. "This performance reflects the mission-critical role our platform plays for customers, with strength driven by core enterprise workloads and early momentum with AI use cases. That strength spans our run anywhere strategy across both Atlas and Enterprise Advanced, highlighting the power of our data platform," said Desai.

Desai said the company is focusing on simplifying how developers connect to coding agents and connect operational data on MongoDB.

MongoDB said it had more than 70,600 customers in the quarter with more than 69,000 on Atlas. Customers spending more than $100,000 in ARR were 2,999.

The one nit in the second quarter was a surge in capital expenditures. In the second quarter, MongoDB reported capital expenditures of $2.47 billion, up from $537 million a year ago. CapEx has more than doubled for the six months ending July 31.

MongoDB projected third quarter revenue between $756 million to $761 million with non-GAAP earnings of $1.57 a share to $1.61 a share. For fiscal 2027, MongoDB is projecting revenue between $2.99 billion to $3.03 billion with non-GAAP earnings of $6.39 a share to $6.58 a share.

Desai said on the earnings call:

• "For customers that already run a large part of their data estate on MongoDB, building an agent on top of that data is a natural extension because the data an agent actually needs is live operational data not a stale copy sitting in a warehouse. Search, Vector Search and Embeddings are built in, not bolted on, so rather than agents connecting to many separate systems, they connect to one platform. We are seeing this show up across industries in a range of use cases, whether it's retrieval of internal knowledge, customer-facing chatbots and agents, or fraud and identity workflows."
• "We are also seeing strong traction with Voyage, our Embedding and Reranking models, which consistently rank at the top of independent leaderboards. In August, we brought Automated Voyage Embeddings to Atlas for one-click Vector Search setup, launched voyage-code-4, a model purpose-built for code, and shipped an upgraded Reranking API, all keeping Atlas retrieval accuracy for AI ahead of the market. Voyage traction is showing up on both ends of the market. Some of our largest existing Atlas customers are beginning to adopt Voyage for the AI use cases, while a large majority of new Voyage customers are AI native and have no prior relationship to MongoDB."
• "More and more of my customer conversations involve running across multiple clouds and self-managed environments at the same time. For customers on both Enterprise Advanced (EA) and Atlas, it is an "and", not an "or.""
• "I want to be very clear that the growth of EA self-managed MongoDB is not coming at expense of Atlas. Atlas actually continues to grow, and we are meeting customers where they are, whether it's data sovereignty, whether they don't want to move it to public cloud for other reasons, it will run in our self-managed environment."