Dev Ittycheria: Yeah. So Rich, thanks for your question. I would say, obviously, the general operational workload of what you call the OLTP workload is still our bread and butter. Workloads that people come to us and relational migrator would just be more of that because of the migrating operational workloads of relational databases to MongoDB. And then the other use case really, other products really function of the use cases that customers are really interested in. For example, Atlas Device Sync, which is really focused on the enterprise mobility play, for example, point-of-sale devices for the retail industry in automotive, connected car in manufacturing, instrumenting the factory floor. So it really depends on the use cases.
In application search, we’re really seeing an acceleration of large workloads in that — for that product. So we’re really excited about the size of some of the business that we’re seeing there. Obviously, Vector is still in public preview. So we hope to have a GA sometime next year, but we’re really excited about the early and high interest from enterprises. And obviously, some customers are already deploying it in production, even though it’s a public preview product. Streaming is something that we’re super excited about. This is more for event-driven real-time applications. It’s just very suitable for MongoDB due to the — most of the data is in JSON and the flexibility of [document] (ph) model makes it a very compelling play. And so I would say that, it really depends on the customer’s use case.
What it really does is it just enables us to go after more workloads more quickly, and it really positions us as a truly strategic supplier to large enterprises and obviously, a critical supplier for early-stage companies, and that’s our strategy, is to get customers to use MongoDB for a variety of use cases across a variety of deployment models.
Michael Gordon: The other thing that I’d add, which is probably implicit and comes across that, but I think it’s important enough to make explicit is one thing that, that sort of, Rich, in your question, sort of the slice-by-slice view misses is the aggregate benefit of delivering the whole platform, right, in delivering a common integrated unified experience to developers so they don’t have to use a bunch of point solutions. And I think that’s really a key part of the strategy.
Unidentified Analyst: Got it. That makes perfect sense. Thank you.
Operator: Thank you. One moment for the next question. Our next question will be coming from Brent Bracelin of Piper Sandler. Your line is open.
Brent Bracelin: Good afternoon. This is Brent. I believe I’m — was next there. Dev, I wanted to talk a little bit about AI. And Mongo has been at the leading powering new apps for the better part of the decade. We’re all trying to figure out what this AI [first world] (ph) looks like, given your purview as a new app enabler, what’s your sense in the next three to four years, how many of these new apps are going to layer in large language models? And what is the net result on the database? Thanks.
Dev Ittycheria: Yeah. So Brent, thanks for your question. I firmly believe that we, as an industry, tend to overestimate the impact of a new technology in the short term and underestimate the impact in the long term. So as you may know, there’s a lot of hype in the market right now, in the industry right around AI and in some of the early stage companies in the space have — the valuations are to the roof. In some cases, almost — it’s hard to see how people can make money because the risk reward doesn’t seem to be sized appropriately. So there’s a lot of hype in the space. But I do think that AI will be a big impact for the industry and for us long term. I believe that almost every application, both new and existing, will have some AI functionality embedded into the application over the next — in your horizon three to five years.
And let me just remind people why we see — where we see AI impacting our business. One, developers will become far more productive with the use of code generation and code assist tools. What that will mean is like that will lead to more applications, which means they need more databases and more data platforms. Second, developers will use things like generative AI to just build smarter and more intelligent applications. One, they don’t want to use point solutions because it’s clear developers want one platform to process and analyze data and metadata and vectorized data and MongoDB is that platform. So which is why in so much interest. And Vector Search, I believe, is really ultimately a feature, not a product. And essentially, it’s basically enabling people to marry private data with public data to really offer a compelling experience and there’s so much interest in our public preview right now.
And as we — as I mentioned earlier, we’ve continued to add many more AI customers this quarter and we think the impact will be big. But in the short term, these are still smaller workloads, but they’re going to grow over time. Some of the use cases are really interesting, but the fact is that we’re really well positioned because what generative AI does is really instantiate AI in front of — in software, which means developers play a bigger role rather than data scientists, and that’s where you’ll really see the business impact. And I think that impact will be large over the next three to five years.
Brent Bracelin: Super helpful color there. And a quick follow-up for Mike. Three-year annual growth rate for EA is over 20%. It slipped below 10% in Q1, spiked now about 30% here in Q2, excluding the licensing multiyear deals. If you continue to see enterprise workload migrations happen, why can’t you continue to see strength in EA?
Michael Gordon: Yeah. So a couple of things. I think as Dev mentioned, some of the Relational Migrations, with the destination of those, whether it’s Enterprise Advanced or Atlas will depend on the customers cloud strategy and their overall approach from like an IT strategy standpoint. Certainly, some of that could benefit EA, and we’ve continued to see robust adoption and adoption of new workloads within that EA customer base. I think the key thing, at least thinking about the back half of this year, and as you start thinking about next year, is just we have had very strong results from EA. And so we think about EA on a compare basis, I just think it’s really important to keep in mind.
Unidentified Analyst: Okay. Thank you.
Operator: Thank you. One moment for the next question. Our next question will be coming from Jason Ader of William Blair. Your line is open.
Jason Ader: Yeah. Thank you. Good afternoon. Just wanted to get a sense on EA. You talked about doing a good job of existing customers adding incremental workloads. What’s the main driver there? Is there something you’re doing differently? Or do you think it’s just maturity and customers getting more comfortable with you for more workloads?
Dev Ittycheria: Yeah. I think it’s really a function of people really recognizing that MongoDB is truly a standard. It’s a platform they can bet on to run the most mission-critical use cases and the flexibility of the deployment models means that they can start on-prem, but they can always migrate to the cloud. And so that optional — that built-in optionality also makes going to EA that much more comforting because it’s not like they’ll be locked into an on-prem solution or locked into some proprietary cloud solutions. So that that’s why I believe, just given our maturity given how we’re really becoming a standard in so many organizations, people are much more comfortable doubling down on EA.
