Ittai Kidron: Got it, helpful. And then, Dev, on Vector Search, I know this is kind of fresh out of the oven here, but maybe you can talk about the opportunity here on a per-customer basis. How do we think about the dollar potential here and is there one common vendor out there that you expect to see more in competition for those types of use cases?
Dev Ittycheria: Yes, so let me start with the second question first. I would say that I think six to nine months ago, there was a lot of interest in vector databases and there were some point solutions that got a lot of name recognition and a lot of people wondering is there a risk that could be disrupted by them. And at that point in time, we’ve made it clear that we believed vectors were really another form of an index and that every database platform would ultimately incorporate vectors into their architecture and the winner really would be the technology that made the vector functionality very integrated and cohesive as part of the development workflow. I would I would argue that that’s really played out. As I’ve said in the prepared remarks, there was a recent analysis done by a consultancy firm called Retool that really spoke to lots of customers and we came out on top in terms of NPS and by the way, our product is a preview product, it wasn’t even the GA product.
We’ve seen a lot of demand from customers and we feel like this is a big, big opportunity. Again, it’s early days, it’s going to take time to materialize, but this is again one of the other big growth opportunities for our business. That being said, in terms of the revenue opportunity, it is really hard to quantify now because the use cases that customers are starting with are still kind of, I would say, early in development, so because people are still playing around with the technology. But we are seeing you know as I mentioned, UKG, is using it to essentially provide a AI-powered assistance for its people, you know one energy — European energy company is using — has terabytes of geospatial data and is using vectors to basically get better insights in terms of the images that they’re getting from the work they’re doing in terms of drilling for oil.
So, it’s still very, very, early days. So, hard to give you like an exact number, even today even in our general non-AI workloads, the workload variety can vary a lot depending on the customer, the number of users, the amount of data. So, I think it’s going to be similar to our core business, which is that just really depends on the use case.
Ittai Kidron: Very good. Appreciate it. Thank you.
Operator: Thank you. One moment, please. Our next question comes from the line of Brad Sills of Bank of America. Your line is open.
Brad Sills: Great. Thanks so much. Wanted to ask a question around the customer count, greater than 100K, it looks like a real nice result this quarter. Is there any change going on there in terms of the trajectory or the path for customers to get to that level. In other words, are they starting bigger, are they landing bigger or are they just getting to that point faster and what would be driving those two things?
Dev Ittycheria: Yes, I’m glad you called that out, Brad. Yeah, we’re — I think we added 117 100K customers this quarter, which is the largest add. I think in the company’s history. What I think it really speaks to is that customers are increasingly viewing MongoDB as a mission-critical platform. They’re going to run more and more workloads on MongoDB. So, by definition, it’s rare that one workload on its own will drive that kind of revenue. So, the multiple workloads and really dealing us as a standard part of their infrastructure stack is what’s really driving that number. And we’re obviously happy to see the results of that and we think that that’s just indication, as I said earlier, where people are consolidating onto a few vendors. They recognize that we offer support for a broad set of use cases, we’re truly a general-purpose mission-critical platform and that their developers really love using MongoDB.
Brad Sills: Wonderful to hear. And then one more if I may please. On the commentary around customers viewing Mongo as that platform with some of these newer workloads besides Search like relational migrator, Atlas streaming, do you — are you finding that receptivity for customers who want to run Search within you know one single solution, is that also the case for streaming and relational, just trying to get a sense for those cycles and how those might ramp on that platform capability. Thank you.
Dev Ittycheria: Yes, so. Actually, yeah, one of the reasons we actually built Search is because we got feedback from our customers in many Instances lot of our customers were dual-homing data to MongoDB and to some sort of search database. So consequently, now that they had to manage two databases, keep that data in sync, but also manage the plumbing, the connected those two database platforms and customers told us [indiscernible] like, we don’t understand why you’re not offering a solution, because we much rather have it all in one platform with one API and that ultimately drove our desire to build-out our search functionality, which is really becoming more-and-more popular. So, the point for customers is that, if you can remove friction in terms of how they can use the platform, leverage the platform, have one set of kind of semantics In terms of — to address a broad set of use cases, it really simplifies the data architecture and the more you simplify data architecture, the more nimble, you can be and the more cost-effective you can be, and I think that’s what’s really resonating with customers.
Brad Sills: Thanks so much, Dev.
Operator: Thank you. One moment, please. Our next question comes from the line of Rishi Jaluria of RBC. Your line is open.
Rishi Jaluria: Wonderful, thanks so much for taking my question. Maybe I want to start by diving a little bit into relational migrator, Dev, I know you said it definitely early days, but where you are seeing usage of it, maybe can you give us a little bit of color, what sort of workloads are these customers are utilizing the tool for, what is kind of that timeline look like; any color you can give there in terms of early adoption would be really helpful and then I’ve got a quick follow-up.
Dev Ittycheria: Yes, so — again, as you can imagine, given the lot of these legacy platforms have been around four between 30 to 40 years. Lot of people have large repository of legacy apps and migrating off a legacy platform to another platform does require some work, it requires essentially three things; one, you have to map the scheme of the old platform onto the new platform, you then have to map move the data and then you have to rewrite the application code. And those three things took some time. So, we heard feedback. When we took the company public, you might remember that 30% of our net new business at the time we went public was actually relational migration. So customers were undertaking that heavy lifting because they were in such pain and wanted to move to a more modern platform.
But clearly, that pain can basically be a bit of a tax on switching costs. And so essentially rebuilt tooling based on feedback from customers to start automating the schema mapping and the data movement. Now, with the availability of Gen AI. You can also now start automating the code generation associated with rebuilding or rebuilding an application and essentially what rather than thing, but just moving the app in one last step, we can actually break down a monolithic relational app into microservices and start cleaning off different parts of the services first. So it can be a much more efficient and also more ROI, quicker ROI and some of the investments we’re making. So, there is a big, big opportunity here for us to do that, but again I want to be very clear, we view this as a long-term growth opportunity.
