MongoDB, Inc. Class A Goldman Sachs Communacopia + Technology Conference 2026

Product of, or Chief Product Officer of Core Products.

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Maybe make the mic a little. Wait for the door to close. All right. CJ, let's start with you. You've now had nearly a year in the role, hundreds of customer conversations. What's become clear to you about the role MongoDB can play, and how has that changed your ambition for the company?

Absolutely. First, it's great to be here. Thanks for inviting us. I would say, yes, on an average, the customer meetings I do tends to be around 10 to 12 a week. Really trying to understand how do customers see us today and where do they see us in the future, right? This is across AI natives, digital natives or Fortune 500 or Global 2000. The biggest learning I had is that we are definitely seen as a very modern database that can scale significantly for massive workloads, right? To give an example, one of the Fortune 100 firms in North America told me that after they moved multiple of their workloads on MongoDB, they have now created us as a standard that any new application, unless proven otherwise, should be on MongoDB. Okay? Which is a massive thing in a highly competitive database world, as you know.

So one is that it is the most modern database. It is the right data platform when you think about creating AI workloads that are real-time. Seems like it is a great technology or great foundation. So that's my learning number one. Learning number two is the modernization opportunity that Dave and the team have talked about in the past that is real as people are trying to get AI-ready. I know we are early, and we said that this year, Mike and I shared with you guys that we are focusing on creating the right product and product-focused approach versus services-focused approach. But that opportunity, specifically in Fortune 100 and Global 2000, is very real for MongoDB as customers prepare themselves for AI.

The third thing that I would share is my learning has been as MongoDB grew phenomenally workload by workload over the last many years now, including Atlas, the awareness in the C-suite has been low. So that's the, I would say, the thing that we can improve on is what I realized in having this conversation. The AI decisions, what is the AI architecture for the workloads, where do they fit, say, MongoDB in, those are usually top-down decision made by a chief data officer or chief AI officer or enterprise architect. My realization was when I would tell them, "Hey, we are now vector integrated fully.

We have this great best-in-class embedding model, so you should build your agents on top of MongoDB," they're like, "Gee, we didn't know that." With one very large financial services company, 6 weeks ago, I had the conversation, and now they are doing a POC on top of Vector, Voyage AI, and Mongo together. I found that the awareness was low in the C-suite on MongoDB.

Yeah. I think since day one, you've talked about maybe driving more top-down engagement. I guess, where are we in that journey over the last kind of 12 months? Like how much higher is engagement around winning some of those newer workloads?

Yes. Sales cycles, when you go top-down, tend to be always long. But it is early, but it's working. We are having a lot more strategic conversations with a large telecommunications firm or a large retail firm in Europe, and they are saying, "Okay, we will standardize." One of the Fortune 100 companies in Texas, once we engaged, they said, "CJ, the reason developers love MongoDB, but you have not served us in the past, so you are not one of the standards on our marketplace. Let's fix that so that people can start building on MongoDB." So early, but the signs are very encouraging, and we are getting opportunity for newer workloads, including AI, through those top-down conversations.

Okay, great. Mike, let's bring you into the conversation. Atlas has been sustaining around 29% growth the last few quarters. Seems to be largely led by large customers, while the newer AI cohorts remain a bit earlier. What is different about the workloads you're landing today, and how does that shape your view of the durability of the current trajectory and its potential to even strengthen over time?

Sure. Thanks for having us, Matt. Let's talk about the workload growth as it relates to the durability. In answer to your first question, we've not seen very much. There hasn't been a big difference between the workloads that we signed historically versus today. There's always some nuances by geo or by sector. What's really driving the growth is three things. One is as we've continued to increase our go-to-market focus on the larger enterprises, as those workloads grow, we have two jobs. One is to get more workloads, and the more MongoDB we have in there, it gives us the ability to expand our workloads. That's number one. Number two is the cross-sell, especially related to Vector Search and now embeddings.

While almost half of our large customers have multiple products, the revenue contribution from that is still quite a bit lower, so that's the job to drive it up. What underpins all of that, Matt, is, hey, Jim Scharf and team have done a great job on the reliability and the performance of Atlas, and that has enabled us to limit the churn and contraction in that base. We expect all of that to continue, which is why it gives us confidence in the durable growth. Again, not much difference in the workload trajectory, but that's really what's driving the growth in those large enterprises.

Okay. CJ and Mike, for you guys, AI demand is clearly building across the frontier labs, AI natives, and a little bit on the large enterprise side as well, but each of them are on a different adoption curve. Can you walk us through the pace of new workload acquisition across those separate cohorts, and how you see each of those beginning to influence Atlas consumption?