Jason Ader: Got you. And then just to follow up on that on EA question. It seems like — I mean, I don’t want to put words in your mouth, but it seems like maybe you’ve been a little bit surprised at the strength of EA relative to Atlas over last year. I mean Atlas has been really strong, too, but EA has, I think, surprised you more to the upside. What does that say, I guess, about kind of on-prem versus cloud or self-managed versus fully managed. Any comments on that, Dev?
Dev Ittycheria: Yeah. What I would say is it really is a reinforcement that customers still will run workloads on-prem, that they still will run workload they want to manage themselves versus use a managed service like Atlas. And so — and I think customers value choice. And customers value the ability to have different deployment models, but also value the fact that if they want to migrate from one deployment model to another, it’s easiest to do so using MongoDB. So I think what we’re really seeing is customers valuing choice and to run anywhere strategy is really resonating with customers.
Michael Gordon: And I would just add, Jason, that the premise of your question is correct. We have been very surprised, obviously, pleasantly so, with the performance of EA. It’s been terrific to see, but it’s definitely been surprising to the upside.
Jason Ader: Great. Thank you.
Operator: Thank you. One moment for the next question, please. And the next question will be coming from Tyler Radke of Citi. Your line is open.
Tyler Radke: Yes. Thanks for taking the question. So Atlas revenue grew by almost $30 million quarter-over-quarter, which is the highest you’ve ever seen it. Certainly, that performance is better than any of the other consumption models you’re seeing. It seems like the commentary at least on Atlas, consumption was pretty consistent with your expectations and still a bit below where it had been pre some of the macro challenges. So could you just kind of unpack what what’s driving that strength and revision back to kind of record high levels of sequential dollar adds? Is it better pricing just given some of the sales changes you made or perhaps maybe it’s the new AI use cases that you talked about. If you could just help us understand that a bit better. Thank you.
Michael Gordon: Yeah. Thanks for the question, Tyler. A few things. If you’re looking at an absolute dollars basis, obviously, the business is much larger. So let’s start with that. Secondly, if we’re looking at the sequential from Q1 to Q2, remember, Q1 has fewer days. And so that’s obviously part of the dynamic, and you’ll see that historically as well. And then third, we had talked about the Q1 consumption being better than planned, and therefore, the starting ARR in Q2 being better. We saw the consumption itself kind of once we had that — once you kind of adjust for that higher starting base, broadly in line with our expectations. So slightly better, but not big upside there. And when we look at it in the back half of the year, that’s what we’re continuing to assume is that same trend line, obviously, seasonally adjusted based on those emerging seasonal patterns. But that’s really how to kind of tie and square all the numbers.
Tyler Radke: Okay. That’s helpful. And then a follow-up question, just in terms of the excitement, obviously, out in the industry around generative AI. But I guess I’m curious specifically how internally you’re using generative AI in products like relational migrator to automate a lot of the re-architecture process? And secondly, are you seeing a greater appetite from customers to modernize kind of legacy transactional applications. And is that starting to pick up just given the excitement around Gen AI? Thank you.
Dev Ittycheria: Yeah. So with regards to Gen AI, I mean, we do see opportunities, essentially, the reason when you migrate off using Relational Migrator, there’s really three things you have to focus on. One is mapping the schema from the old relational database to the MongoDB platform. Moving the data appropriately and then also rewriting some, if not all, of the application code. Historically, that last component has been the most manually intensive part of the migration, obviously, with the advance of code generation tools, there’s opportunities to automate the rewriting of the application code. I think we’re still in the very early days. You’ll see us continue to add new functionality to Relational Migrator to help again reduce the switching costs of doing so. And that’s obviously an area that we’re going to focus. So that’s in some ways, a big opportunity for us. And Tyler, there was the second part to your question, which I…
Tyler Radke: Yeah. Yeah, it was just around the customer appetite, like is that the frenzy around Gen AI, is that causing an acceleration in the pace in which customers want to take on these modernization projects?
Dev Ittycheria: Yeah. So I would say that the recent MongoDB is well suited for these new modernization projects is, one, obviously, the data that’s trapped in these legacy platforms is incredibly important if you want to leverage that proprietary data for a competitive advantage. Two, is that the performance requirements of these new modern applications require a new modern platform. And three, because it’s such an iterative area where people are — is changing so quickly, you all see the platform that’s inherently flexible. So that’s driving people to move to MongoDB and to more modern platforms more quickly. So unlike the old lift and shift where people are just trying to say, avoid paying the Oracle tax, now people are being much more thoughtful about not just lifting and shifting, but modernizing and going off relational to MongoDB. And that’s definitely a trend that’s increasing.
Tyler Radke: Thank you.
Dev Ittycheria: Thanks, Tyler.
Operator: Thank you. One moment for the next question. And our next question will be coming from Patrick Walravens of JMP Securities. Your line is open.
Unidentified Analyst: Hi. This is [Owen] (ph) for Pat. Thanks for taking the question and congrats on the strong quarter. Just a quick one for me. What will the pricing structure for some of the new features like Vector Search and Stream Processing be?
Dev Ittycheria: So the pricing will be a function of the — obviously, the consumption of the back-end infrastructure that supports those new capabilities. So they’ll show up as essentially more consumption of Atlas clusters, or increases of clusters depending on the load of the application, and will show up on the Atlas revenue line.
Patrick Walravens: Great. Thank you.
Operator: Thank you. One moment for the next question. And our next question will be coming from Michael Turits of KeyBanc. Your line is open.
Michael Turits: Hello?
Dev Ittycheria: Hey, Michael.
Michael Turits: My name got [indiscernible], I wasn’t sure it was me. Thanks. Quick one for you, Mike, and then one for Dev. Very quick. Mike, are you able to comment on the linearity in the quarter relative to those consumption growth trends and how we exited? And then, Dev, for you, you just said that you think that Vector Search is a feature, not a product. There are as you know, two databases deliberately out there, explicitly out there in the market. And then, you, as well as others, who don’t have vector databases, including Google, with [Allo] (ph) the other day, are talking about the applicability of their databases for vector embedding. So can you talk about how that’s playing out as far as you can tell with customers in terms of their receptivity of looking for something besides a dedicated vector database for this? So linear question and then that one.
