All right. Good morning, everyone. It's really great to see everyone at TMT 2023 at Morgan Stanley. I'm Sanjit Singh. I'm the Infrastructure Software Analyst at Morgan Stanley. Super excited to have the management team from Confluent CEO, Jay Kreps, and Chief Financial Officer, Rohan Sivaram. Before we get started, let me go through the disclosures real quick. For important disclosures, please see the Morgan Stanley Research Disclosure website at www.morganstanley.com/researchdisclosures. If you have any questions, please reach out to your Morgan Stanley sales representative. Let's talk some Confluent. The company started off or finished a 2022 that was successful, 50%+ growth. Cloud business grew 124%. I think the cloud mix is approaching about 40% big software company, a multi-billion dollar software company from your perspective. Yeah. You know what? We're lucky to benefit from kinda two big waves, right? One that everybody's aware of is, you know, the move to cloud. You know, this is something that I think has continued to pace, right? Despite all the economic pressure on IT, et cetera, you know, companies have found a better way to do stuff than the cloud, and data systems are moving to the cloud being offered as a service. That's the first wave I think most people are aware of. The second wave is data streaming. You know, there's a big paradigm shift from thinking of data as something that is just stored here and there, sprinkled throughout the organization, kind of in silos, to something that has to be able to come together in real time that drives the operation of a company. You know, that's a problem that's existed in some form for a while, but we haven't had a really good solution. You know, the rise of data streaming has really created a new paradigm for how to operate around this, and one that's gotten significant traction. You know, there are hundreds of thousands of companies using the open source Kafka that our offering is based around. That's an open source project that myself and my co-founders helped to create. You know, that's part of this larger move of data into, you know, continuous real-time processing. That's actually a really sizable shift and one of the few examples where you're really witnessing kind of the emergence of a major new data platform in companies. That's the other reason is, you know, if you look at that, you know, there's only so many of those. Empirically, you can kinda see it happening both in terms of the breadth of customer adoption as well as the depth, where, you know, the companies that are doing this for real have, you know, hundreds or thousands of applications and are spending, you know, a significant chunk of their spend on data, you know, with us. That's very exciting to see. You know, a popular question that I get from investors is, okay, I understand data streaming is a, y ou know, there's value there. What percentage of applications needs to be real time? What percentage of your data pipeline needs to be batched versus real time? Yeah. What's sort of your perspective on that and how that may evolve? Like, what does it look like today, and how does that evolve? Yeah. Yeah, it's a great question, right? It is the key question for this is, hey, is this streaming stuff some niche that, you know, you just kinda sprinkle on the edges, or is it a big deal? I think a good way to address it is actually kinda flip it on its head. There's very few areas where you're like, "Hey, I wish this data was like slower and more out of date." You know, that's actually not a good thing. It's not something where it's like, sometimes I want A and sometimes I want B. You actually always want it to be fast in real time. That's always good. The question is, A, do you have a way to get that? B, is there something kinda driving it along? I would say in both cases, there was kind of a gap there if you were to go back 10 years. There wasn't good infrastructure for this kind of real-time processing. The use cases were more few and far between. You would see this stuff maybe in financial services, some trading system or whatever that was very real time. Increasingly, what's changed is, you know, software and data has moved into the kind of drive train of the business, like how you interact with customers, how you produce and distribute your products, goods, services. All of that's now tied together by software systems. That operation of business is real time. It is something that happens in the real world all the time. It's not like a batch thing that happens at the end of the day. Yeah. Like, you know, reality is real time. Modeling that in software kind of requires these capabilities. Yeah, what, what does that amount to? You know, I think in the near term, in the companies that have kinda done this at scale, maybe this is about a third of their data footprint, which is quite substantial. You know, the bet with Confluent is that, you know, many or all companies will become like that, right? I think that's something where you're actually seeing good progress out there. Many technologists believe that there's kind of a clear path to that future. I would say, you know, probably in the, in the near term, a third, and then maybe over time, even more convergence between kind of batch and in real time. Yeah. It's very interesting. In Q4, you did see some impact on the macro, some longer times, longer sales cycles and getting customers to, you know, come to a conclusion on purchasing decisions. Is there anything about Q4 and the weaker bookings you saw in Q4 that has, in any sort of way, shaken your confidence about the size of the opportunity or, changed your view on the market potential? I don't think so, right? I mean, we're part of this overall move to the cloud. To the extent that people are leaning into that further and further, it's happening faster. To the extent that there's more scrutiny on spend, maybe it's happening a little bit slower. I think there's a certain inevitability to this, right? You know, it's fundamentally just a better deal to be building your applications, maintaining your infrastructure in a cloud environment. Even though that will control the pacing, I don't think it changes the outcome at all. I, you know, I doubt very much that that's kind of a permanent state. If anything, you know, I would see that as more a temporary slowdown. That totally makes sense. You sort of mentioned in your earlier comments just about, you know, the ubiquity of Kafka. You know, we talked about 100,000+ organizations using Kafka, 75% of the Fortune 500. If we square that against the customer base, the paying customer base of Confluent, which is right around 4,500, how would you assess the ability of the company to more meaningfully convert the Kafka install base to Confluent paid customers through this, you know, slower, more difficult budget environment? Yeah. I think that's one of the exciting things in front of us, you know? In order to really convert the open source users in bulk, you really have to have a cloud service that's easy to use. We started with an on-premise offering. That was an important part of capturing, you know, kind of the larger enterprise part of the market. We added a cloud offering. In the last few years, that's become a really substantial part of our business. The interesting thing about cloud when it relates to open source is it's actually a better deal. Like, it's actually cheaper than doing it yourself with the free software. You would think, "Well, you know, how can that be the case?" The reason is because when you think about your spend on one of these big distributed data systems, you're kind of hiring a team of engineers to run it, you're spending on cloud infrastructure. The utilization of those things is not great, right? You're doing it just for yourself internally. It's probably not your core competency. You're kind of spending a lot of money, and you're probably not doing it all that well. You know, we do this analysis with our customers. You know, it's a very apples to apples comparison to say, "Hey, you know, assume all I want is just Kafka," right? What would it cost me to kind of do it with open source? What would it cost me to do it with Confluent? Assume there's no difference between the two. You know, even in that assumption, Confluent is just a much better deal, you know, substantially better. Then you layer on top of that the product is actually much more complete, you know, much more substantial data streaming platform with a bunch of capabilities around the connectors, the governance, stream processing. You know, you're actually getting something that is both better and cheaper, and that's compelling. I think that's something that's really just started to come true for us as our cloud offering came to maturity, and that's why I think we've shown the progress, you know, in the cloud. I think in many ways that becomes more compelling when budgets are a little tighter. You know, we're seeing that in our tech customers. You know, you probably hear it from some of the companies here. They're actually putting a lot of thought into, you know, how they deploy money internally. A lot of these companies had kind of big internal do it yourself efforts. A lot of those companies are looking at like, "Okay, you know, is there a cheaper way of doing that?" You know, "Do we need to have, you know, a small army of internal infrastructure engineers across all these systems, or can we just get, you know, a streaming service from Confluent and some stuff from the cloud providers and some databases from Mongo, and, you know, would that be a better setup for us?" I think that calculus has changed quite substantially for a lot of these companies, and now they're looking for how do they make the shift. Of course, they have, you know, big teams and big spend in this area, so it's not an overnight thing for them. Yeah. I think that it's happening really across a lot of different industries, where they're really looking at the build versus buy decision differently. Yeah. Looking at that total cost of ownership argument, that's kind of an opportunity for the sales team, as we look forward. Let's bring Rohan into the conversation. Sivaram, maybe to start with you. Confluent made the decision to accelerate its path to profitability by a full year. You made some difficult headcount decisions. Can you give us a sense of where the team is investing less, where you're sort of doubling down, and as CFO, how are you coming to the decision as to what initiatives will get funded, versus not, today versus, let's say, 12 months ago? Let's start with the growth side of the equation. We're leaning into a $60 billion TAM, and we're able to continue to grow at a high growth rate, 30% per year while pulling in profitability by one full year. While the decisions were difficult to make, the reality is we had doubled headcount over the last two years. There are places where we could have become more efficient, and we took this opportunity and this environment to do so. We are very much preserving our quota-carrying headcount capacity, and we're really leaning into ensuring that our unit economics around LTV to CAC, sales efficiency, are all improving. We're gonna continue to invest in that area. On the R&D side of the house, it starts with Confluent Cloud. We're a cloud-first company. We're gonna continue to make strategic investments in furthering many elements of the cloud offering. They range from security to data governance. We also, we acquired a company called Immerok w hich gets us into the Flink open source space. We'll be coming out with a commercially available product at the end of this year in the stream processing space, which is what Flink does. We're doing that with the same design principles as we have with Confluent Cloud, which is it's gonna be cloud native, it's gonna be complete and everywhere. That will be an area of focus for us. We think that the Flink offering that we will be coming out with over time can be as big as the Kafka opportunity. We're very much focused on building out a complete platform for not only data streaming, but stream processing. In this environment, as far as us green-lighting incremental projects, it is very much a continuation of what we've done in the past, which is looking at the ROI, looking at payback, and we're gonna be disciplined on that investment. We believe that we can grow at very healthy growth rates over the long term with that, with that philosophy of efficient growth. That makes a lot of sense. Another popular question, and I guess the context here, Q4 2021, the operating margin was around -40%. By Q4 of next year we're talking about getting breakeven, so that's a 40-point swing in operating margins. The question that I often get is, you know, what are the levers? Like, how are they, how are they getting there without, you know, sort of compromising what Jay sort of laid out as a pretty interesting opportunity. You touched on it a bit, but I was wondering if you could just sort of go through some of the levers on the margin trajectory, not only to the breakeven, but ultimately to your target model. Yeah. Yeah, when we look at the progression, it speaks to our growth and profitability framework that we laid out at the time of our IPO, starting with our gross margins. Yeah. Our gross margin profile of the business has been very resilient at roughly around 70%, even with a very large mix shift, where cloud has become a much bigger percentage of revenue. The cloud unit economics that we have seen improve over the last two years has been very dramatic, and it's been a real bright spot for the company, and we're gonna continue to make investments and operational decisions to improve that as cloud becomes a bigger part. We're gonna see improvement in gross margin towards our longer term target model of 72%-75%. When you look at the other elements of the P&L, sales and marketing is the area that we have the most wood to chop relative to getting from where we are today to our targets. We wanna get sales and marketing down to call it 30% range. We're doing that by really investing in the product side of the house, making sure that we have the right products for our sales teams to sell. We're looking at the product-led growth, where customers can be onboarded without having any salesperson speak to them. Then we're really leaning into our customer growth go-to-market journey, where we matriculate those customers that start with the frictionless pay-as-you-go through the committed contracts, through, you know, the million-dollar phase gate, and then multiple million-dollar phase gate as we look at the $5 million-$10 million+ ARR customer cohorts, and those will be increasing over time. G&A as a percentage of revenue will naturally come down over time. R&D, we're looking at having, I would say modest improvement. R&D is the lifeblood of the company, and what Jay and Jun and team have done relative to building out the best-in-class R&D organization in the data streaming and data platform space is something that we're gonna continue to lean into. Makes a lot of sense. You mentioned, Jay, sort of previewed my next question. Or Rohan, you previewed my next question for Jay, which is around the Flink acquisition or the Immerok acquisition. Jay, can you sort of frame us what does stream processing mean for your market opportunity? You know, what are the sort of potentially new use cases that are gonna be possible now that wasn't necessarily available pre-Immerok? Yeah. You know, I know this is like a new area, so it can be a little hard to make sense of all this data streaming, stream processing stuff. You know, an analogy that's, it's not perfect, but is actually pretty good, is if you think about kind of data at rest, how did that evolve? Early on, the focus was on storage, and you had storage systems at which you would store your data. It's obviously useful. You got to keep it somewhere. Over time, you start to see the emergence of databases, maybe relational databases that can combine storage with processing. That combination is really powerful, right? You can actually do a lot more with your data if you can do those two things together. You typically end up with a lot more data if you can do those things together. That became the basis for, you know, application development in kind of the data at rest paradigm. For streaming data, we see the same thing. You know, the first thing you need is you need to have data streams. You know, the core of Kafka is about how do I read, write, and store data streams, and how can I create like a hub for those that spans all the parts of my organization? You can think of that as kind of like a central nervous system that connects everything. Obviously, once you have those data streams, you're building these applications that use them. You're doing a lot of that building kind of from scratch. We've had some features that support building that kind of streaming application, the processing of streaming data. There really has emerged these rich frameworks like Flink that make that much easier. You can think of that as kind of the query processing layer of a database and bringing that together with the stream itself. We have a unique ability to do that because we're providing the stream. We can kind of bring in that processing, and we think that that's a big part of the solution, and the two things make each other more valuable, right? You're gonna have more streams because they're easier to process. You're going to, you know, create new streams because of that, and because we have that stream, your processing is easier to take advantage of. Yeah, we've seen this traction and the emergence of Flink. This is becoming kind of a popular de facto layer in the stream processing space that people wanna build against. When we think about successful cloud offerings, that's really the formula that works, is something that is kind of a, you know, open de facto standard to build against with a really differentiated cloud native offering of it. To do that, we knew that we would need to really pick the layer that people wanted to build against, and then bring together the team that had the most expertise and knowledge, both about that community of people as well as about the internals. This is a very technologically sophisticated area. Any query processing layer is, but a kind of horizontally scalable real-time query processing layer is even more complex. To offer that as, you know, a cloud service, you have to really put in the investment and build something that was kind of built for the cloud, not just, you know, put it on some servers. This was an opportunity to bring in those people and put them to work. It's proven immensely popular with our customers. We've been having this conversation since we had the announcement. Customers are extremely excited about it. Flink had a reputation as really delightful to build applications against, but really horrific to operate. You know, maybe even more so than Kafka, this is an area where you would have to hire this team of experts to run it. People really just want it done for them. You know, the value proposition of that managed service is, you know, if anything, even stronger. We're very excited about, you know, what it's gonna allow us to do, and then what it's gonna allow our customers to do, you know, what the applications they can build. Make total sense. We've been talking a lot about kind of the mentality of customers in this environment, we've been, you know, getting the leads around stream processing, we're also sort of talking about just big higher level questions. One that I always continue to get, you know, back from the IPO and even to today is: Why is this capability, data streaming capability appropriately delivered from an independent provider, like Confluent versus something I should just get from my, you know, primary cloud provider? Yeah. You know, I think to some extent that's a question, companies ask about any vendor, right? You usually, you have some big vendors who will sell you lots of stuff. You have some specialists who go deep in an area. For, you know, for an area of specialization to make sense, it has to be something deep and big where you're like, "Hey, this is gonna be important for me. It's, it's worth doing one more contract," right? That vendor has to bring something to the table. They have to do a better job than whatever your de facto. The de facto, maybe it used to be IBM, maybe now it's AWS. The, you know, it's the same thing of kind of there's some go to market contractual simplicity if you just bundle it all together, but there could be some advantage if you go deep. That, that's the general trade-off. In that general trade-off, you would ask, "Hey, is this data streaming thing a big deal?" Well, in a lot of the companies adopt it, this is one of their major data platforms. It's a really big deal. It's new. Getting it right and kind of plotting that path forward is something we have a unique advantage in. You know, that's how it would fall into that general trade-off, but there's something very specific to this area that's different, that isn't true of other technologies. That's that ultimately data streaming is something that connects together applications. Data flows with these streams across environments, between different data stores, between, you know, SaaS layers and clouds. It doesn't just exist in one environment. It needs to exist across all your environments. It needs to actually knit them together. One of the reasons that we didn't, you know, build just for the cloud or just for one cloud is really because of that. We knew that, you know, to properly serve customers at scale, you would have to have an offering that could exist on-premise, you would have to have an offering that could exist in each of the major clouds, and then it would need to be able to bridge between them. That's something that's particularly hard for the cloud providers to do. You know, for a variety of reasons. It's hard for them to offer services in their competitors' environments. There's been attempts at that, they haven't been successful. It's been hard for them to bridge into the on-premise environments. There's been attempts at that, kind of mixed success. You know, that's something that we were kind of built uniquely to do and is extremely valuable to our customers. When they think about the central nervous system, it has to kind of go across all the parts of the body, right? It can't just be in one area. It's a point that Rohan makes on the earnings call quite often, that your highest expanding customers are the hybrid ones, the ones who are using Confluent Cloud, commiserate or in conjunction with Confluent Platform. Staying on the topic of like partnerships with the hyperscalers, maybe give us an update on the AWS strategic partnership that you signed in 2021 and your ongoing relationships with Azure and GCP. You know, in this tougher environment that we're operating in, do you see the hyperscalers potentially getting more competitive in that continuum of cooperation, competition? Do you see any potential that they get more aggressive in this category? You know, we've seen the opposite. Actually, when we were small and the cloud overall was less mature, I think that there was an assumption in some of the cloud providers that these third parties would not really be a successful part of the ecosystem, and I think that that's changed. As companies have gotten to scale and they're doing something really valuable for customers, you know, ultimately these cloud providers, their competition is not Confluent. I mean, there's like one team or, you know, five teams in each that compete with Confluent, but the bulk of that organization, they compete with each other. One of the dimensions by which they compete is, you know, what's the ecosystem that I've got? When customers are selecting, they're really thinking about that ecosystem I'm gonna get. If Confluent isn't good in that ecosystem, that's a problem. Beyond that, you know, when they think even just purely economically, how am I gonna make my next dollar? It really is, how can I get a workload out of the on-premise environment into cloud, and how can I kind of spin the meter on all the differentiated services that I have in the cloud? If you think about what makes that hard, what's the root thing that makes it hard to move workloads and take advantage of the different systems they provide, it is ultimately about the kind of liquidity and flow of data. Data tends to be locked up on-premise. A lot of the core systems running these big businesses are on-premise. The question for them is, how can I chip out little chunks of that and move it into the cloud without having to, you know, rewrite it all from scratch? Once it's in the cloud, how can that flow and take advantage of these new systems? In that respect, I think there are, you know. Particularly excited about Confluent because we enable that. We enable the flow of data from on-premise environments. We deliver a huge amount of data, petabytes of data into S3 and other services that they have. That's obviously a huge thing for them just to, you know, get up and going, enable these workloads, and take advantages of the services they have. Of course, we fiercely compete with, you know, a handful of small services that they have internally. The reality is we've just gotten much deeper in this space with a much more complete vision. I think because we're going after something new, and we have a vision for what that should be, you know, this year, but three years from now, five years from now, that I think it's hard to match unless you have something like that that you're working towards. Makes total sense. I'm gonna go out to the audience and see if there's any questions. Before we get there, Rohan, I just wanna sneak in one just on stock-based comp and net share dilution, your approach to managing that. Is there a shared dilution percentage that we should think about on an annual basis in 2023 and going forward? First off, on a net dilution basis, in 2022 it was about 4.7%. In 2023, we are saying that it's gonna be between 3%-4%, and we're gonna continue to drive that down further over time. Stock-based compensation expense is a little bit of a different animal. It's reflective of grants that were done in the past. It has a four-year kind of timeline to amortize. You're not gonna see stock-based compensation expense as a percentage of revenue come down meaningfully until a couple years from now. Once it does, it'll get down into the mid to high teens. Great. I think we had a question right up front. A question about the edge. You guys don't do really well in the core clouds, but there's sort of cloud, Cloudflare, Akamai, Fastly, Fly.io, and these guys stand usually between your data source and wherever you're processing it and could potentially step in and either take share or augment or otherwise act on that data flow. How do you think about them, and how do you see them fitting in going forward? Yeah. The kind of edge environments and edge topologies are really important for, I would say, a subset of our customers, you know, the ones especially that have some kind of real world presence IoT use case. You know, I don't see those organizations as competitive in any way. We do support, you know, running Confluent Platform in these kind of low profile environments, that's one of the reasons, you know. I talked about on-premise, it works equally well at bridging into those environments. You know, that is the architecture we're seeing companies lean towards if they have that type of problem. It is kind of, you know, the hub of data is in the cloud where they have all the rich capabilities, but they need effectively an outpost everywhere that they are that kind of collects and maybe can do some light pre-processing. You know, we're serving a number of customers in manufacturing, automotive, some other use cases like that that have that kind of topology. With that, we're all out of time. Thank you, Rohan Sivaram and Jay, for giving us a really solid perspective. Thank you. On Confluent and the opportunity. Thank you very much. Thank you. All right. Thanks.