A – Shane Xie: Thanks Rohan. To join the Q&A, please raise your hand. And today our first question will come from Michael Turrin with- Wells Fargo, followed by Morgan Stanley. Michael, go ahead.
Michael Turrin: Hey, thanks. Nice bounce back here. I appreciate you taking the question. Jay, I want to go back to some of the partnerships you mentioned. You caught out a few captivating companies with Anthropic, Pinecone and then the OpenAI customer relationships. So I’m just wondering, is there commonality in terms of their needs for data streaming? Are there reasons they’re landing with Confluent versus open source Kafka? I think this is obviously new ground for all of us, so just anything else you can provide just to help us understand what drove those is useful.
Jay Kreps: Yes, I think increasingly streaming is a critical part of the architecture for these generative AI applications. The need is very much to bring together the kind of proprietary enterprise data you would have with one of these more generic language models that kind of knows about the world but doesn’t know the up to the second view of your business and what’s happening. And so that’s very much the use case where we tend to play in that. So the partnerships with the vector databases like Pinecone, the models, it’s very much around supporting that architecture. And our goal was really to support the integration across the best technologies in that space and I think was very much embraced on the other side by these companies that are trying to do that.
And then OpenAI, this is an incredible technology company that I think has the potential to be the size of Google over time in terms of the scale of their infrastructure. We’re extremely happy to be part of that stack.
Michael Turrin: That’s great. If I can just ask a follow-on for Rohan, it’s encouraging to see the 22% guide hold onto here. You had mentioned a number of impacts for us to consider last quarter. Any commentary you can provide just on how some of those played through in Q4 relative to what you were expecting previously is also useful. Thanks very much.
Jay Kreps: Thanks for the question, Michael. Yes, of course, I mean it all starts with our Q4 execution. We delivered subscription revenue growth of 31% and total revenue growth of 26%. And a couple of milestones, the first quarter of $100 million for both Confluent Platform and Cloud. That coupled with just the green shoots we started seeing in the digital native segment with respect to the consumption was, I’d say one area in general, Q4 execution was solid. I think number two, on the consumption transformation side, Michael, we saw good early traction with respect to where exactly where we want to be. As Jay mentioned, we were at sales kickoff last week, and the feedback was very, very positive. And generally, like one month in, we’re getting just positive signals with respect to our transformation.
So that’s that. And in general, when you think about our guidance philosophy, if you look at our Q1 and full year guidance, we’re not assuming a huge amount of acceleration in the second half of the year. So that, I mean, when you combine all of these, it just gives us, I’d say, more confidence around our 2024 guide.
Michael Turrin: Very clear. Thanks very much. I appreciate it.
Shane Xie: All right, thanks, Michael. We’ll go to Sanjit Singh with Morgan Stanley and then followed by Deutsche. Sanjit?
Sanjit Singh: Yes. Just to pick up on the previous question and some of the themes last quarter, Jay I think one of the themes that you called out last quarter was just that software development projects had slowed down throughout the course of calendar 2023. It doesn’t sound like you’re giving the all clear signs just yet, but there seems to be some encouraging signs with, like new logo acquisition in January. In terms of what you’re seeing from the customer base and sort of them sort of restarting some innovation initiatives, any update there that you can tell us as it relates to potentially driving pipeline for Confluent?
Jay Kreps: Yes, I would characterize it this way. I think ’23 was just a tight year for it budgets kind of everywhere. And then in the digital native space, it was extra, extra tight where there was very significant push on optimization. Where are we now? Yes, I wouldn’t say it’s all back to where 2021 was, but there are some green shoots. Right? We’ve definitely seen more activity in the digital native space where I think some of the optimization has been accomplished. So there’s projects happening there. I think maybe there’s kind of a normalization across both large enterprise and digital native where people are getting a little bit back to normal. It’s early in calling out, but I would say that’s the early part of what we’ve seen.
Sanjit Singh: Great. So a little bit of incremental progress and maybe just one quick follow up. I mean, from Q4 is typically a big renewal quarter for most software companies. As you saw the renewals come up, did you pick up any sort of increased motivation by a cohort of customers to move or downgrade from paid Confluent to open source Kafka, any sort of update there?
Jay Kreps: No. Overall, the kind of gross retention rate has remained very strong, as I think we called out. We track exactly whatever we compete versus open source, whether renewal or kind of new win and those win rates have remained very strong, in fact, actually improved in Q4 over past quarters.
Sanjit Singh: Excellent. Thank you.
Shane Xie: Great. Thanks, Sanjit. We’ll go to Brad Zelnick with Deutsche and next followed by RBC.
Brad Zelnick: Thanks very much, guys, and great to see the strong finish to the year. I want to follow up on Michael Turrin’s question around these partnerships Jay and AI use cases, which you called out in your press release, I think, where you referenced real time generative AI use cases as really being at the forefront right now. If we kind of go back to where we were at your analyst event in New York, I don’t know if that was six or eight months ago, it feels like with these partnerships, this is really more coming into focus and into fruition. Can you give us any prospective view in terms of like the types of use cases and the extent to which this is really going to materialize into demand, which I think, again, reflecting back six to eight months ago was a little bit unclear as things were shifting in the world exactly, you kind of knew Confluent was participating, but I think you left the door open to exactly how, if you could articulate that, that would be great.
Jay Kreps: Yes. So I would say that, kind of our place in that stack has played out exactly as we called. Right? We’re in that kind of data supply chain for use cases around large language models. I would say the predominant use case, it’s a lot of language and chat stuff, as you would see, very much that kind of apply this language model using the data about my business. That’s the broad version of it that could be around augmenting internal employees and making them effective. That could be something customer facing. That could be kind of a back end data processing task. We see that across a variety of disciplines, whether it’s, I’d call out some of them, but everything from kind of retail to tech companies to financial services.
So I think that’s happening. Where are we at in that cycle? I’d say it’s still early. There’s more experiments than production applications and obviously we’re kind of a production data analyst, so that’s where we come into play. But I think it’s definitely promising and kind of adds to the set of use cases that we have that drive adoption of this new architecture around data streaming.
Brad Zelnick: Great. And maybe just a quick follow up for Rohan. Thanks, Jay. Great job on the quarter, better Q1 guide than we were modeling, but would I be wrong to assume, and just what I think I’m hearing from you is that you’re feeling better about the environment and growth opportunity versus a quarter ago given you’ve nudged up your 2024 guidance. But you’re still keeping the margin guide for flat, making me wonder, are you hiring more into the opportunity you see ahead or is there maybe dilution from Notable? Not to nitpick, but what should we be thinking about here?
Rohan Sivaram: No, and thanks, Brad. Thanks for the question. On the top line side, like I mentioned, three things, strong Q4 performance, our consumption transformation off to the start, exactly how we expected it to be, and just some green shoots on digital native, that’s driving our slight increase in dollar terms and increased confidence in our 2024 guide. On the margin side, we have a guide of 7 percentage point improvement year-over-year. That’s holding to what we said a year back. So I wouldn’t call out anything specific. I called out that Notable does not have any material impact on our financials and it’s included in the guidance. But in general, as we head into 2024, we will hire in critical areas of the business and to overall make sure that we are driving durable growth and doing that efficiently in a thoughtful manner.
Brad Zelnick: Great. Thanks very much. Nice job, guys.
Shane Xie: All right, thanks, Brad. We’ll take questions from Matt Hedberg with RBC followed by Needham. Matt?
Matt Hedberg: Great. Thanks, guys. I’ll offer my congrats as well. Following up on Brad’s question a bit and focusing on the TAM for data streaming, do you have a sense for the percentage of workload, Jay that, or workload or apps that customers typically see as needing real-time data versus where maybe batches find?
Jay Kreps: Yes, it’s a great question. I mean the key observation is, I think that everybody wants data to be up to date and they want things in sync with the business. The question is, how critical is that, right? Is that something that you must have at all costs? Is that something you would like to have? And I would say there’s two changes there. First, increasingly, use cases do need that. As systems become more part of the operational stack of companies, as more of the use of data is driving action, not just reporting, I think that does require things to be much more in sync with the current state of the world and so I think there’s a trend overall in that direction. And then secondly, the cost of real-time, the cost of streaming and the difficulty of it is very much coming in line with batch computing.
There’s no reason this should be harder. It’s just newer. Right? And that takes time to mature. So, yes, we felt like, hey, without making a lot of really big assumptions, if you look at kind of what’s the portion of workloads that the average enterprise would have on this streaming platform, I would say about a third, maybe a third live in this kind of operational database world where you’re doing the quick lookups and serving the interactive web apps. Maybe a third are in the kind of analytics world, kind of back end batch stuff that just really doesn’t need to move out of that kind of offline processing, and maybe a third are in that stream processing space. I think that’s the end state that we’re aiming for. If you look at companies that are a little more technologically advanced and have been at this for a while, that’s where they are.
If you look at companies who are just starting in this space, they just have a few things right that they’ve done. So the assumption is that those newcomers will be able to progress. What enables that is making this technology easy and approachable, which is, of course, the direction of all our investments.
