Rohan Sivaram: Yeah. Mike, on the second half, we’re obviously, we saw, this was the first quarter of the transformation. And as Jay called out earlier, the early indications have been very positive, which showed up in our cloud performance for the quarter. And more importantly, when we look at our month one and how we are entering Q2, we feel good that some of the momentum had actually carried on to Q2. So that’s good. But in general, like we still have a couple of quarters of execution that we need to focus on. But if you ask me how am I feeling, obviously, what’s evident in our Q1 performance and our Q2 guide, which are strong and which we feel are ahead of our expectations. And what that does is the strength in the first half is also derisking our second half from an overall guidance perspective. So, overall, where we are, we feel really good with the transformation, but at the same time, we still need to execute a couple of more quarters.
Mike Cikos: Great. Thank you for that.
Shane Xie: Thanks, Mike. We’ll take our next question from Derrick Wood with TD Cowen, followed by Bernstein.
Derrick Wood: Great. Thanks guys. Nice to chat with you. Jay, you mentioned having gone through some pricing changes recently. Can you remind us what changes you made and what kind of dividends you’re expecting to see as this gets absorbed in the market?
Jay Kreps: Yeah. Yeah. There’s a set of changes. Some of these are actually product offerings, which effectively allow better TCO and incentivize the use of our multi-tenant offerings, which are more efficient for us. So we announced freight clusters in Kafka Summit Bangalore. We announced enterprise cluster type, which is a high performance multi-tenant offering with private networking. We made adjustments to some of the throughput oriented pricing. So there was a number of changes that came out. All of these were meant to reduce friction in the land-and-expand process. We thought about this consumption transformation. A big part of it was changes on the field team, changes in our systems, changes in compensation. But I think going along with that, we felt it was very important that there not be a ton of product or pricing friction in that land process, right?
So if we’re trying to tell the team to go sell in a way that gets customers up and going, it can’t be the case that to get to a reasonable price, there’s a six-month negotiation at the very front door of the process and so those changes have lined up with that. Why do that? It’s ultimately because there’s a ton of open-source Kafka and we want to go soak that up with our cloud offering. We feel that’s very important. So kind of growing the breadth of that customer base, that sets us up for all the growth in those customers over time. And we do feel like these kind of changes and new offerings unlock workloads that would have been harder to access and that comes out of the TCO of the offering, right? We’ve talked in the past about how Quora is able to really offer a better TCO for customers.
And it’s important that we make sure we have offerings that cut across all the different workloads they have, so that it’s a bit of a no-brainer across everything they do, not just a certain workload type or a certain use case. So that was our goal.
Derrick Wood: Yeah. That’s helpful color. And I don’t know for Jay or Rohan, you guys talked about rebound in digital native consumption trends. I wanted to ask about financial services vertical, which is obviously important for you. Just curious what you’re seeing there around demand conditions and deal sizes and whether you’re seeing much composition change in Platform versus Cloud?
Jay Kreps: Yeah. Yeah. That’s continued to be a strong segment for us. And over the last few years, we have seen a pretty significant ramp up in Confluent Cloud adoption. And I would say that that happened first in the smaller banks, and then over time, that’s spread to some of the largest financial institutions. And they tend to be a little bit slower to start with a new cloud offering. There’s actually very substantial security, reliability, scrutiny that goes into the adoption of any part of their stack. But increasingly, we’re really a great fit for their use cases and actually allow them to meet the requirements that they have faster than if they were trying to build this up themselves. And so we’ve started to see great adoption of cloud in financial services and I think that’s a very promising thing as these very large institutions open up something that is very low friction to consume across their very broad set of use cases.
So we’re really excited about kind of getting in the front door in a lot of these very large banks.
Derrick Wood: Great. Thanks. Congrats.
Shane Xie: Thank you. Our next question will come from Peter Weed with Bernstein, followed by Guggenheim.
Peter Weed: Thank you very much. Obviously, great to see the continued momentum on the cloud side and the transition to consumption working out kind of as you planned. But I may have missed it, but I feel like we haven’t talked very much more on the Platform side where I think we saw a sequential step down in revenue. And I wonder how we should think about some of that, a little bit more weakness there and whether or not some of that’s cannibalization of people moving to cloud and so it’s just some underlying share shifting or whether or not we should think about, like, slower growth going forward on that side of the business, given that it’s an important part of the revenue stream.
Jay Kreps: Yeah. You want to speak to that, Rohan?
Rohan Sivaram: Yeah. I’ll be happy to take it. Thanks for your question, Peter. Well, when we look at our Confluent Platform performance, we’re very pleased, actually. We grew 15% year-over-year. And when you generally think about the Platform business, more of as a reminder, what happens is about 20% of total contract value is recognized as licensed revenue upfront. So what that can do, that can add a little bit of lumpiness in the revenue, purely based on the timing of large deals or the timing of renewals for large deals, those have an impact. But when I take a step back and I look at, say, the last 12 months for this business, we’ve been very pleased with the overall momentum. And Jay also called out with respect to product innovation, we launched Flink on-prem, which is obviously going to help this part of the business as well.
So, yeah, I mean, listen, we’ve said that Confluent needs to be wherever our data and applications reside. If it’s on-prem, we need to be on-prem. If it’s in the cloud, we need to be cloud. Just keeping that in mind, we do feel that this is going to be an important part of the business as we look ahead.
Peter Weed: Thank you.
Shane Xie: All right. We’ll take our next question from Howard Ma with Guggenheim, followed by JPMorgan.
Howard Ma: Great. Thanks for taking the question. Jay, can you talk about some of the alternative options that you’re aware of for the transport layer in RAG architectures? I don’t believe they’re the standard yet. And do you have — on that point, do you have plans to establish a more formalized reference architecture program for RAG implementations, and perhaps, broader inference use cases too, and really aimed at making Confluent the standard for transport and transformation as well?
Jay Kreps: Yeah. Yeah. It has been a focus for us kind of evangelizing this architecture, because as you say, it is something that’s just coming into kind of formation now. The reality is, I don’t think that there are great alternatives for real-time data movement, right, outside of Confluent. I would say we have a kind of strong status as a de facto for real-time movement of data across the enterprise. There is opportunities for customers to just try and build it in batch. There’s plenty of batch ETL products. The reality is that for a lot of these use cases, they’re answering questions about the business and that’s really just not good enough. For a lot of these use cases, it’s something that’s customer support related, or in other words, driving some aspect of the business where kind of answering without data information is very likely to be wrong relative to what the customer was just doing and so we are seeing a real push towards real-time.
And yeah, it’s on us to make sure that as that stack solidifies, we have a permanent position in that.
Howard Ma: That’s great. And maybe I can slip in one more just on the topic of open source Kafka conversions. Can you talk about any progress that you’re seeing with the Confluent Migration Accelerator tool, I believe it’s called? And is that increasing…
Jay Kreps: Yeah.
Howard Ma: … your wallets here among Fortune 500 and to what extent are partners using that tool?
Jay Kreps: Yeah. We’re just ramping that up. So somewhat surprisingly, we haven’t had really a focused effort on these migrations. It’s been somewhat more one-off customer-by-customer. And so both in terms of tooling and with our partners, creating a focused effort to move customers over. As you can imagine, in any of these situations where there’s kind of a better TCO alternative, but some effort that’s required to make the switch, you want to reduce as much as possible that effort and make it really easy for customers to get from point A to point B. So I think it’s just coming into being now. We believe that’ll contribute over the next years.
Howard Ma: Great. Thanks so much.
Shane Xie: Thank you. We’ll take our next question from Pinjalim Bora with JPMorgan. Pinjalim?
Pinjalim Bora: Hey. Thanks for squeezing me in. Congrats everyone for the quarter. One clarification. How — help me understand how broad-based was the cloud consumption and ramp. I heard it was driven by a select set of customers. So I wanted to clarify. And if you would understand if some of the new AI vendors that you recently added materially contributed in the quarter?
Jay Kreps: Yeah. You want to take it, Rohan?
Rohan Sivaram: Yeah. Happy to. Hey, Pinjalim. Thanks for your question. So when you look at a cloud performance, I’d put it in maybe two categories, the performance, if I had to call out for Q1. The first one is, when you look at our broad base of customers, we did see stabilization in consumption and the net new use cases and the digital native segment is inclusive in there. So that’s good. That’s a broad base of our customer. And the second call out was some of our newer customers. We’ve seen the ramp up of these newer customers. I’d say something that we are very pleased on. And the GenAI customer that you spoke about is probably in that board of customers. It’s a few of them who kind of we’ve ramped and where the ramp schedule looks in line and we’re pretty happy with that, and that’s for Q1.
