All right, good morning everyone. I'm Sanjit Singh. I do infrastructure software on the software research team at Morgan Stanley. We're going to be talking real-time streaming. Super thrilled to have the Confluent management team, CEO Jay Kreps and CFO Rohan Sivaram. Thank you both for joining us again at another TMT conference. Really appreciate having you here. Thanks for having us. Awesome. So let me get through the disclosures. For important disclosures, please see the Morgan Stanley Research Disclosures website at www.morganstanley.com/researchdisclosures. If any questions, please reach out to your Morgan Stanley sales representative. So with that, to sort of level set, Confluent had another great year last year. Grew revenue 32%. Your cloud business grew 65%. I think even almost as impressive. Operating margins improved by 2,300 basis points in a single year. Super impressive. The spending environment has not been without its challenges. So we've been, if we look at the past several quarters, just broadly in software, through a sort of tech downturn. Jay, from your perspective, going through the last several quarters, where do you think the company has shined, and what were some of the things that the downturn sort of surfaced that said, "Okay, we need to get better at these couple of things"? Yeah. Yeah, I think, you know, I think you touched on one of the things I think we did well, which was, you know, sustaining high growth through a tighter environment and then driving efficiency. We came out, as you know, as we came public, you know, we were running relatively hot. And that was intentional. You know, if I think about our growth trajectory, the company grew very quickly. We were able to, you know, I think really just outpace some of our earlier competitors, which turned into kind of non-factors. And I think that was a positive thing that we were really, you know, the only company deeply focused on streaming at large scale. You know, I think that's been a tailwind. So I think that high investment was valuable. But coming into 2023, we definitely wanted to really focus on efficiency. So what did we do well? You know, I think first of all, we achieved that. Second of all, and maybe more importantly, you know, we did it while sustaining our strategic investments in the product and the space. And so from the outside, this is, you know, always less apparent from just looking at the financials. But there's different ways to cut. You know, you can chop off large initiatives. That could be good if those initiatives aren't going to pan out. But in our space, we felt this next wave of functionality for us, going beyond our core Kafka service, which is just the kind of raw data stream, to the connectors that actually get that data and attach to the rest of the organization, the governance functionality to manage this at large scale, and Flink the stream processing capabilities to really act and build applications around that. We felt like that was the next waves of functionality that really take us beyond that kind of core data stream to a complete data streaming platform. And so we wanted to make sure each of those stayed on a good trajectory. And that means, you know, heading into this year and beyond, we kind of have these waves where the Connectors are on a very nice growth trajectory and can turn into a large business in their own right. The governance functionality has grown very quickly in its early days. And, you know, in Q1 of this year, our Flink service will go GA. And so, you know, that was obviously a hard thing to balance, where you're both investing in one area while also really trimming and optimizing and driving efficiency in a lot of other areas of the business and trying to manage all that in ultimately a tighter environment. You know, I think we did that well. And that's something I'm definitely proud of. You know, in terms of what did we you know, what did we get wrong? You know, what could we do better? You know, I do think one thing we were a little behind the ball on was, you know, what we ended up doing this year, which was this consumption transformation. We took the first steps on that change, you know, maybe to the tune of about 10%-15%, in 2023. You know, at the time, we felt like that was too big a change to make in coordination with everything else that was happening. Like, you can't both trim 23% of operating margin and also make big go-to-market changes at the same time. Right. But what this change means for those who don't follow us closely is really aligning our internal go-to-market with the way customers work with us. So customers buy our product, you know, on a consumption basis. And they have the ability to lock in a commitment, but, you know, their bill month to month is determined by the actual usage and applications they've built. And, you know, this has been a trend in our portion of the industry overall that companies are really aligning the go-to-market to that consumption, saying, "Hey, the sales team is going to be paid for the new applications that they bring in that drive consumption, not just the committed spend." And that actually turned out to be more important than we realized in 2023, heading into an environment where there was more conservatism. You know, having a model where the go-to-market team is focused on trying to lock in big three-year commits upfront before anything's been done that might have a lot of risk in terms of, you know, exactly how you're going to use the product and exactly how much you're going to need to consume. You know, that turned out to be, I think, a little out of step with where the market was. And I think we felt that as we went through the year. And so, you know, we've corrected that. You know, they say the best time to make the change would have been, you know, a year ago, but the second best time is now. And so, yeah, we did, you know, we've corrected that. We've kind of made a more complete transition heading into this year. That's gone well so far, but obviously a lot, you know, of the kind of focus for this year is around that change. Yeah. It makes total sense. So if we talk about as the founders of this data in motion market, the streaming market, starting with Kafka way back in 2010 and now Confluent itself being a platform, can you give us a sense, Jay, of where we stand, sort of mark to market, if you will, where we are on the journey in terms of these, real-time data, platforms becoming a fixture of the modern data infrastructure? And maybe in terms of just, like, the complexity of the use cases, the sophistication of use cases, where do we stand, in 2024 now, you know, sort of 15 years or just over 15 years into the journey? Yeah. Yeah, Yeah, a lot has happened. You know, as we were starting Confluent, you know, it was really based on the hypothesis that the infrastructure around data would go from really just focusing on data at rest, like how it sits, how it's stored, how you kind of look up bits out of a stored data, stored database, to, you know, a platform that's really about how all the different data systems in an organization connect and how data flows in real time. So that was obviously, you know, at the time, you know, it's a bit of a conjecture as to how the world's going to change. And, you know, what's happened since then is a whole set of things that I think take a lot of risk out of that transition. Doesn't mean the transition is finished, but it just means some of the, you know, the risk is gone. So, you know, what changed? In 2014, I think most technologists, if you ask them, would have said that there were kind of fundamental limitations in real-time streaming that meant that, it would never really be able to do the kind of batch things. And, you know, this was certainly the experience. If you looked at a lot of financial services, you know, firms, they had big sophisticated real-time platforms, but it was very limited in what it did. And it was very hard to build that stuff. And so the idea that that would become, you know, an easy, powerful primitive that every company would use all over, that was not obvious, right? Secondly, I think the use cases advanced pretty significantly, like just the pressure to have customer experiences that bring together all the ways that you interact with a customer across the business that are up to date and, you know, rich and contextual. You know, that became much more over the last 10 years, and that pushed on this. So, you know, we went from something that was unclear if it was possible, and it was unclear if everybody wanted to do it, to something where, you know, suddenly we've checked off a lot of these kind of technical areas. And we know, yeah, we can make streaming as efficient. We can make it as easy to use. We can make it as complete. Now it's kind of a question of just maturity of that technology stack. And then on the demand side, you know, a similar thing where we've seen this now take root in virtually every industry, large companies to small companies, use cases that range from, you know, the side of the business to things that are right at the core of payment systems, transaction processing, the kind of, you know, highest risk, most mission-critical systems in the world. And so, you know, I think a lot has happened there. It doesn't mean we're done. You know, if we think about this transition, when you think about infrastructure stacks, you know, they don't move overnight. This happens kind of application by application as things turn over. But I think it actually presents a really interesting point in time where there's some technology change. There's a lot of evidence for how it's happening. The kind of end state or stopping point, you could debate. You know, we've said a number of times that, like, hey, when we look at companies that are further along, you know, it's more like a third or more of their applications are kind of in this streaming domain. We think that's more the norm. Logically, that makes sense. If you think about how business works, it's a very real-time thing. As these software systems become more connected, they have to exchange data that way. They have to react that way. So that's where we think the, you know, the stopping state is, but we're far from that. But it's kind of clear that it's, you know, it's going to be a lot bigger than it currently is. We just don't know, you know, how far it goes. And so I think it's a very interesting time for this whole area of streaming. You're seeing streaming show up so that in all the different parts of the data stack to integrate with this, you know, that's a huge driver for us because now we can connect into all the other data platforms companies have, you know, be able to actually power Confluent off of that. So, yeah, I think it's a really exciting time. And I think a lot of the kind of more foundational risk of how this will turn out has gone away. And that puts us in that kind of deployment phase of something large going across the economy, which is cool. Yeah, it makes a lot of sense. And to pick up on a point that you made about, you know, customers, I get it, you know, roughly about a third of their applications are embedding real-time capabilities. But overall, we're not there yet. And a lot of the questions that I get from investors is like, are there any sort of analogs or paradigms to think about the penetration or how this category will evolve? I sort of point to the mix between sort of NoSQL relational databases 10 years ago. That was like 95% relational. Today, it's 75% relational. And in a category that big, that's $ multi-billions when you go from when a NoSQL goes from 5% to 25%. Is that like a decent way to think about this? Or are there any other sort of paradigms to think about the penetration of streaming into it? Yeah. You know, I think that's not a bad example. One of the things that I think is maybe a little easier to reason about with streaming is there's kind of a more fundamental logical reason why things have to move to streaming. So if I said, you know, is it the case that logically you have to move from relational to non-relational data stores, you'd say, well, I don't know. Maybe. Kind of depends on what developers like. You know, maybe they like it. Maybe they don't like it. Maybe they like it, and then they stop liking it. Like, it's hard to say. And, you know, it's turned out they have liked it, so there's been more. With streaming, I think the argument is actually really straightforward, which is, you know, businesses run in real time. A lot of the customer experiences that are now powered by software are very real time and cut across many parts of the business. They have to hook together many pieces of software to function. you know, software is really entering a lot more of the, you know, kind of the drivetrain of the business, how products and services are produced, manufactured, delivered, logistics, orchestration. To do that, fundamentally, it's a real-time thing. Like, reality happens continuously throughout the day. And yet, a lot of the technology stack around data came out of this paradigm of batch processing, meaning, you know, the job kicks off at midnight, and it computes through yesterday's data, and it spits out some results at, you know, 5:00 A.M. or whatever it is. You know, that's just very hard to integrate with a real-time business. If you look at what tech companies have done, they've kind of moved out of that batch into a more real-time stack. You look at the mainstream economy, that's happening. So I think the nice thing about this area is you can get there just kind of watching the line go up, just watching adoption empirically. But maybe unlike some of the move out of, you know, relational data management, you can actually get there just logically. Like, logically, if you can have something which is continuous and real time, and if that's not more expensive and not harder to use, then you're going to see a very significant portion of data processing move to that over time. And, you know, I think that logical pressure is actually exactly what gave us confidence as we were starting the company. We were like, look, you know, as we started Confluent, maybe we fail. But if we fail, somebody's going to succeed. And if we have to watch them be successful with it, it's going to be, you know, really aggravating. So we better get out there and start this company. And I think, indeed, we were close enough to the right time that we were not, you know, too early or vastly too late that we've been able to really kind of help push this forward. But there's no question that this is something that's moving in the world now. So, yeah, I think the relational analogy is not bad. I think the rise of databases themselves, if you're willing to go back in technology history a little further, is actually reasonable as well. This is a time where, hey, there used to be many hacky ways of storing data and building around data that ultimately coalesced to the relational database. As you got a layer that made that type of application much easier, you got a lot more usage and value that was created on top of it. Similar thing in the streaming world where you've had, you know, depending on how you count, half dozen, a dozen little micro categories that solve some part of data movement, real-time data integration, application integration, ETL, message usage, just all these crappy micro categories that are kind of being displaced by something much more general, much more powerful, and much more easy to use. And so, you know, I think, you know, if you see that analogy, I think you could see it as kind of like what databases did for data at rest. Streaming can do for data in motion. That's another way to think about it. Yeah, that's a great way to think. Great paradigm. I wanted to go back to the sales transition to consumption. And you guys have been clear. This is not a pricing change. This is around how you're orienting the go-to-market organization. I was wondering if you could take us behind, like, looking through the eyes of a salesperson who's been used to, you know, getting a quota, hopefully exceeding that quota, getting a big fat check at the end of the year. How does their job change, or what does their strategy change to make the same amount of money or money or more money in this new sales model? Yeah. Yeah, well, a lot stays the same. You know, you still have a quota. You still get a big fat check as you land new use cases. But, you know, if you think about maybe the best reps that we had, I actually don't think their behavior will change all that much. They were already out there driving adoption and getting new applications and, you know, working with customers on right-sizing the commit as that usage grew. But there is, you know, a place where maybe the commit-oriented compensation was a little bit misaligned with ultimately what the customer wanted and what Confluent wanted in terms of, you know, our revenue and growth. And that would be this pattern. So maybe the rep comes into account, and they see, hey, there's a lot of interest in Kafka here. There's a lot of use cases they could be pursuing that they're talking about doing. And you kind of go around the account, and you collect everything that could happen. And you put together this massive deal. So you say, OK, we're going to do Confluent for all these things. And you kind of drop this big three-year thing on the counter and say, hey, sign here. And the challenge with that is it can just turn into a very long process where you, the company, now have to de-risk this. You're like, well, are we really going to consume that much? We haven't even built the application. How much does it need? You know, go do a six-month study to determine the exact usage. In this case, if you have, you know, dozens of applications, in some cases, it's more than that, that are kind of being pursued, that suddenly turns into a massive project where all the risk is on the customer side. If they commit to too much, that's a big problem for them. And yet, the incentive of the go-to-market is to kind of maximize that upfront commit. So that's the bad case. That's where you have, you know, misalignment of what the company wants because it's not like our revenue changes at all when you make that consumption. We actually need the, or sorry, when you make that commit, we actually need the consumption to drive the revenue growth. So the model now, you know, really incentivizes the behavior of, you know, our best reps, which is go in. You know, if the customer wants to commit upfront to lock in pricing, we're happy to do that. If they want to wait and get some stuff out into production and then do it then, we're happy to do that. If they want to commit to a smaller amount, launch some things, and then take it up later, we're happy to do that. The focus of our team should be all around finding the new use cases, making sure that we're part of that stack, accelerating, you know, unblocking their deployment, and taking it out there. And, you know, that's what we want. That's what drives Confluent's revenue. That's what now drives the sales compensation. And that's ultimately what's valuable for the customer. You know, if you just kind of look at the history of software business models, I think things that create alignment between the customer and, you know, the provider are the things that have actually driven a lot of value when going from perpetual license to subscription licensing to SaaS. And I think consumption is like one more step on that where we're, you know, bearing some of the, you know, the, you know, we have skin in the game with them in making sure that we're creating value. And that ultimately means they are willing to move faster, and it becomes stickier because there's, you know, less chance of shelf wear or unused commitments. So I think all of that is positive. You know, we're kind of going through a lot of changes to make sure we do that right. Naturally, this changes not just the sales compensation, but your notion of pipelines. You have to be much more detailed about what are the applications that are kind of going out to production. We're kind of working through all that. You know, we're very optimistic about how this plays out in the business, both because, you know, we had taken the initial steps on this already last year, but also because we'd watched a whole set of peers that leaned into this very aggressively and saw really good results over time. You know, as they really mastered that new system, that became, you know, a significant, you know, accelerant for them. Yeah. It looks like Snowflake has adopted a similar approach too. So that definitely seems to be the way sort of infra software sales is going. Jay, how long do you think these changes are going to take to bear fruit in terms of the behavior you want to see out of the Salesforce? And then, Rohan, to bring you into the conversation, what does this do to, what does this transition do to some of the financial KPIs? Any highlights there that you want to call out? Yeah. You know, there's two answers to the question you asked. I mean, we've modeled this, you know, as more impactful in the first two quarters of the year because that's when the most changes are happening. You know, we've rolled out the, you know, kind of cutover to the new comp, kind of the new motion. But that kind of ramp-up of adoption, you know, we've really modeled in that time frame. Yeah, that said, I think the companies that have really leaned into this have continued to evolve it and tune it. And so, you know, I think that's where we've modeled this as a, you know, impact or headwind or change. I think this is something that, through next year, we'll see as an accelerant where there's opportunities to, you know, accelerate further based on the alignment we've created. Awesome. Yeah, Jay touched on the things that are changing as part of this consumption transformation. It's important to understand what's not changing. What's not changing is our business model. It's the same. What's not changing is how we recognize revenue. What's not changing is anything related to the Confluent Platform business. And the reason I'm saying this is because a large amount of KPIs that we currently run the business with will continue to be the same. Three things I'll call out, which I'd say, from an investor perspective, it'll be important to look into. The first is we've called out in our earnings prepared remarks that subscription revenue will be a focus. Why? Well, it does a really good job of capturing the ACV for Confluent Platform and consumption for Confluent Cloud. So that'll be a good, I would say, visibility into the organic momentum of the business. That's one. The second, net retention rate has been a focus for us. Obviously, that's a good way to track the retention and expansion of our existing customers. We call that out. The third piece that Jay briefly touched on, our focus with the consumption transformation is to make sure that our sales reps are having the next new use case conversation, which means that we are focused on driving consumption and not getting the commitment from our customers. As a result of that, RPO as a metric, we've started to de-emphasize that. We'll continue to report it as part of our accounting disclosures. But that's not going to be the only forward-looking indicator for our business. To summarize, like, I'd say subscription revenue and NRR should be focused, and we should de-emphasize RPO. Yeah, makes total sense. To give Jay a bit of a break and to focus a little bit on your side of the house, Rohan, going back to sort of the trajectory of operating margins, Q4 of 2021, the business was at negative 41% operating margins. I think last quarter, you guys were positive 5%. That's a massive amount of margin expansion in just 8 quarters. Do you feel that, you know, you got the sort of pace right? Did you improve margins too fast? And then, broadly, how are you thinking from here the balance between growth on one side but continuing to get more efficient over time? Yeah. When we think about resource allocation in general, like, just philosophically speaking, it's never a one-year exercise. It's a multi-year exercise. And the reason I say that is because when you invest in R&D, your returns typically come in 18 months. -24 months. When you invest in sales and marketing, your returns come in 9 months. -12 months. So anytime we look at resource allocation, it's a multi-year process. And our true north is always the opportunity that's ahead of us. And how can we take advantage of that opportunity by driving durable growth over a long period of time? So that's the overall philosophy. To directly answer your question, no. I think we've been investing in the right areas. We did have a ruthless prioritization with respect to where we want to invest. And the proof's always in the pudding. If you look at our 2023 and 2024 product roadmap that Jay touched on, we've made this transition from a single product streaming company to a multi-product data streaming platform. We have a series of unlocks, which start with the Flink GA in Q1 and the rest of our data streaming platform through the year. These are all, I'd say, proof points that show that we've been investing over the last couple of years and putting our money in the right places with respect to where we want to be. Long term, I mean, in 2024, our margin guidance is to be net neutral, margin neutral, which is another seven-point improvement from where we ended 2023. And we've also said that over the medium term, we want to be in the zip code of 5%-10%. And that continues to be the case. That's awesome. Let's talk a little bit about the 2024 revenue guidance, right? It no longer assumes, like, with the strong results in Q4 and the sort of update to the guide, you're looking for more consistent growth throughout the year. You do have some easier compares starting to layer on. If things sort of play out the way you guys hope, what are the factors that can go right that can deliver potential upside to that outlook? Yeah. I mean, our guidance philosophy has always been to make sure that we are setting guidance that's prudent and achievable. And as we are doing that, it's really important to provide the investor community with the right levers and what's going on in the business. And we've been fairly consistent with that. When I think about 2024, there are a few puts and takes. So number one is macro. There are geopolitical situations. There's the Fed and the interest rates. There's elections going around, like 40+ elections around the world. I mean, that's a bucket that we do not control. So our thought process there is, let's assume it's going to be as is, and it's just going to be a continuation of where we are today. So that's number one. The second piece is around the product unlocks. We have our Flink GA happening in Q1. And the rest of the data streaming platform, we'll have unlocks. We're working on FedRAMP certification in partnership with NASA. So these are all, I'd say, things to monitor. But the real benefit is going to happen in fiscal year 2025. And the last but not least that Jay touched on is our go-to-market transition that we are making with respect to consumption transformation. And we've baked in the impact of that slightly more in the first half of the year versus the second half of the year. So at balance, we feel that all of these implications are baked into our guidance for 2024. Awesome. Let's get back to talking about the core of the growth opportunity, Jay. You know, coming off that conversation we had on sort of the sales transformation that's sort of in flight right now, I think one of the fundamental aspects about your category of software is that you're competing for the workload of the use case versus, let's say, an application software, application SaaS, where it's more of a displacement market, right? You get one CRM, usually, right? And so what initiatives does the team have in place to make it easier for customers to go from, let's say, that initial use case to use case two, three, four, and five over time? Yeah, I would say there's a few things on the go-to-market side and then a few things on the product side and then something that's a little bit inherent to the category. On the go-to-market side, it's really about making sure that we have a clear picture of the use cases that are prevalent in each industry. And we can give people a very nice picture of, hey, what is it you could do? What's the art of the possible? How would you do it? You know, what might competitors in this space be doing? And I think that that helps you go from, you know, the most advanced companies who, of course, will figure all this out on their own to the kind of broad majority who like to see a clear roadmap of those who've gone ahead of them. Yeah, that's something we've put significant energy into. We have continued investment in partners that help, you know, especially SIs kind of take you out into the broader set of initiatives and transformations that a company may be orchestrating. I think that's an area that's just really gathering steam for Confluent and is contributing this year but will continue to grow in the years ahead. Both of those, I think, are important. On the product side, it's really about, you know, completing this data streaming platform, right? If you're just offering Kafka, that kind of low-level stream of data, of course, companies can bake that into their applications. And they do, right? That's, you know, has enormous traction in open source. But as you start to have connectors that just plug it in off the shelf, as you have, you know, real-time processing capabilities in Kafka, which is kind of the universal language of data, as well as programmatic capabilities, suddenly, it gets easier and easier and easier to build applications in this way. You know, you can take something from, you know, a nine-month application cycle to, you know, being able to build very simple data pipelines, you know, in days, right? And that's a huge deal in terms of accelerating the adoption. You know, the most mission-critical applications will always be carefully built and tested over a long period of time. But making the easy things fast is absolutely important for us. So that's on the product side. And then the last accelerant, I think, is really foundational to the category, which is, you know, there is a kind of inherent network effect within companies for streaming. And that's because these streams of data often go between parts of the company. The goal is to connect or to share the real-time flow of data. So as a company spins up this area, the first use cases are kind of siloed applications. But over time, you have these critical data streams, which draw in those new applications organically. And that's what we've seen take hold in, you know, our largest customers. And, you know, it makes sense. In a retailer, maybe the first use case would be, hey, we need to get the kind of real-time flow of sales for some kind of, you know, marketing, pricing, promotion use case. But sure enough, what's selling in a retailer is one of the most critical data sets they have. And it's very likely that the only place you can get the real-time view of that is Confluent. And that then becomes the basis for all kinds of applications, whether it's managing inventory, logistics, you know, analysis, analytics, fraud, a whole set of things will ultimately feed off of that. The first one came for the capabilities of the platform. But the latter ones mostly came for the data. You know, of course, they benefit from the capabilities. But they came because that was the only place you could get that stuff. And that's what helps us get to scale so that, you know, the nth application is a lot less work than the first one. Awesome. You know, this time last year, we were all talking about AI, Gen AI. And we're kind of, I think, as a community, as a tech community, kind of trying to guess where this is all going. And we probably still are to a large degree. You know, how have you sort of, is there any update to your views on the role of streaming, stream processing as it relates to enabling Gen AI-infused applications and the building of those applications? What's the role here? Yeah, you know, at least the short-term changes we've seen have played out, you know, as we'd hoped. And so I think it's been a solidifying. And then, of course, over time, we may see quite a lot in this area. And, you know, that'll be interesting. So, you know, what did we think the two opportunities were in this area? Well, the first one was, there's a bunch of AI companies that are building out their infrastructure stacks. And they need streaming the same as every other tech company. So we went and sold to all those companies. And we brought in awesome customers. You know, last earnings call, we talked about OpenAI, which became a substantial customer. But really broadly, across that set of next-gen companies, there's an opportunity for us to sell to them. So that's the smaller thing. The bigger thing is when we look at our larger customer base, you know, we're seeing all kinds of enterprises bring together their data with these large language models to augment customer service, you know, other customer interactions, you know, really drive productivity in their employee base, try to help people do what they do. We're seeing that across all different types of departments, all different types of businesses. That type of application is really following an architecture which has come to be called Retrieval-Augmented Generation, or RAG. It's really about, hey, how do I bring my data together with this language model? The language model kind of knows about the world at large. My data is something that is more up to date with the current state of the world, has to be tightly controlled in terms of who can see what. That's what I need. If I have a customer service rep that needs to answer a question or if I want to directly answer a question with some kind of chatbot to you, I have to know about what you're doing with my business and product right now. Otherwise, the answer's not going to make any sense. You know, that really drives a lot of data flow and integration across companies. That's the use case that we've seen most prevalent with our customers, is that kind of data supply chain for these AI use cases. The reason we're so bullish about it is because these applications are not rocket science to build. We've been playing a similar role in the architecture from, you know, older kind of predictive machine learning and AI applications going back since the beginning of Kafka. That was actually one of the reasons, you know, I helped make it at LinkedIn was to power that type of thing. But the reason that this is so appealing now is both the power of these applications but also just the fact that it's, you know, it's a very achievable thing for all types of businesses to actually put this to practice, whereas a lot of the predictive machine learning applications were pretty hard to build and operationalize, pretty hard to get good results for. And so I think the scope of what we're seeing is just much, much larger. And I think people know that. But that's why we see it as such a promising driver in the business. Awesome. I want to talk a little bit about Flink. But if anyone in the audience has a question, you know, just raise your hand. And we'll get the mic to you. But let's talk about Flink. You mentioned it's going GA in Q1. If we use Kafka in streaming as a baseline, how do you think the adoption of stream processing for Confluent will compare? Yeah, yeah. So there's two questions. You know, what will the pace of that adoption be? And then what's the end state? Like, how much value is there in this processing layer? And so I'll start with the end state because I think that's probably the most important is why we invested in this space. You know, if you look at data platforms in the data at rest world, it would kind of divide into storage and processing. And, you know, that's where a lot of the value is. Databases bring those two things together. And, you know, it can be hard to pull apart how much value is there in processing and how much value is there in storage. But indeed, you can look at some of these cloud applications that price them separately, like Snowflake. You would see, yeah, the majority of the money they make is on the processing. And logically, if you talk to customers, they would say, yeah, that processing, that's our business logic. That's the intelligence. That's what we bring to the data. That's quite important. And so, you know, if you take that analogy seriously, then in the streaming world, you would believe, hey, this processing opportunity is quite significant. Furthermore, when we look at how customers build around data streams, you know, their spend on their custom applications that have that logic is significantly larger than the spend on the data stream itself, you know, certainly on the order of 5x or so larger, right? So if you're able to build a platform that makes that easier for them, that makes it more cost-effective because they don't have to stand up a bunch of custom servers, that makes it kind of more elastic, more fault-tolerant, and reliable, easier to build, then you can capture a lot of that larger spend in addition to the data stream itself. So that's, you know, that's kind of the first principle of thinking. The other way to get there is, what's that near-term look like? So we've seen the adoption of the open source for Flink. That's really become a kind of de facto standard in the stream processing world. It offers a whole set of interfaces around real-time data across the popular languages that developers would use, you know, really rich and thriving community around it. And so we feel like, hey, yeah, that can be on a very similar trajectory to Kafka itself in terms of the adoption and with a bit of an accelerant because, of course, we can take this out to our existing customer base, whereas we had to go and land all those customers the first time. One of the wonderful things about a consumption model is how little friction there is in adopting the next piece of functionality. And so when I look at a business like AWS or, you know, maybe a little closer to us as a standalone company, Datadog, you know, I think one of the things they did so well was that expansion from one thing to many, many more things. And if you think about what attaches to streaming data, the answer is, like, so much, right? There's so much that can kind of be pulled into that orbit, that kind of data gravity, the functionality you can offer around it. So we think that that's a huge opportunity. Now, you know, I do want to kind of temper expectations. A lot of people think, well, OK, if you've released it in Q1 with GA, then we should see, you know, Confluent revenue doubles in Q2, right? The reality of, you know, cloud infrastructure services is the ramp is more gradual than that, right? People have to first see it be a solid target to build against. Then they have to build their applications, get them to production where they kind of, you know, run at scale and generate workloads. Then those workloads have to accumulate to add up to the revenue. So it's not, you know, it's not an L-shaped curve. But we do think, you know, kind of coming into next year, this is a, you know, significant tailwind for the business. That's certainly what we've been kind of building towards and our expectation for it. Great. See if there's any questions from the audience back there, Bob. Hi, thank you. You mentioned Amazon and Datadog. You know, and I would add Microsoft as, you know, companies that reported and sounded, you know, quite good on, you know, the consumption patterns, you know, for this year. I think a lot of people in this room are probably confused because some of the January quarter companies that are reporting more recently, you know, are singing a different tune, so to speak. So just any perspective you can give us on kind of the state of the world in software, any changes would help. Yeah, yeah. You know, I would draw out two factors. So, you know, I do think we've seen at least some amount of stabilization in patterns. I don't know that that means it's not like a recovery. It's just that if you think about 2023, I think especially in tech, a lot of companies were in heavy optimization mode where literally all resources are going to that. I think as you get out into the larger economy, even some of that flows through to there, where a lot of IT departments were more focused on kind of getting the value out of the cloud spend they'd already made than they were on kind of the next new project. I think we're seeing a little bit of moderation of that. But I don't think it's like a complete reversion to 2021 spending patterns by any means. I think that kind of plays out across, you know, the larger set of companies. So I think there's one other factor that you do see, which is I do think companies that serve kind of production use cases, like kind of mission-critical application-type use cases, I do think that they tend to see a more continuous build because there's less room typically to kind of optimize it away. Oftentimes, the things which are the easiest to add, you know, that could be a, you know, some kind of monitoring observability framework. It could be some kind of analytics thing on the side. Those things are often also the easiest to take away. So they kind of, you know, they come the easiest. But they may go the easiest as well. And so I think you might see a little bit of a dichotomy there as well. But, you know, I'm not a great prognosticator of results for other companies. So, you know, take that for a grain of salt. But that's certainly one of the things we've seen in our business is, you know, what drives us is the pace of new application development. There's typically less optimization. There's always some optimization in any business of how they use the product. But because these are production applications, they kind of come out pretty well thought out and optimized. Whereas if I look at, you know, say, our data warehouse usage, for sure, it's not that hard for us to be like, you know, take all the reports nobody looks at and get rid of them or take the retention of data and shrink that down. There's lots of optimizations we can make, you know, which are much harder in a kind of production application setting. With that, we've got to end it there. Thanks so much, Jay and Rohan, for the conversation. Thank you so much. Thanks for having us.