Good morning, everyone. Thanks for being with us today. I'm Jason Ader from William Blair. I'm pleased to introduce Steffan Tomlinson, CFO of Confluent. Before we begin, I'm required to inform you that a complete list of research disclosures or potential conflicts of interest is available on our website, williamblair.com. Steffan is going to present a few slides, and then we'll get into Q&A. Thanks, Steffan. Thank you, Jason. Good morning, everyone. Good to see everyone today. I'm going to go over about, yeah, a handful of slides to give you an overview of what Confluent does, and then I'll turn it over to Jason for Q&A. I'm Steffan Tomlinson, CFO. When we look at the lineage of Confluent, it really starts with what the 3 engineers at LinkedIn were trying to do. Our 3 cofounders, starting with Jay Kreps, was an engineer at LinkedIn trying to run their business in real time. What happened was there was no software that existed that enabled all of the applications and databases to flow freely and have data be exchanged across a ubiquitous environment. It became a real problem, they looked at expand the overall environment. They couldn't find any off-the-shelf. Unfortunately, they ended up creating code at LinkedIn called Kafka. Kafka was ultimately released to the wild in the Apache Software Foundation. It became one of the most successful open source products of all time, with 150,000 organizations using Kafka to run their businesses in real time. What does that mean? It means that historically, data has been at rest. It's been resting in databases, it's stored in applications, and what Kafka did was effectively enable data to flow freely amongst and between databases, applications, multi-cloud environment, et cetera. When you look at the pervasiveness of Kafka and the code that was released, over 75% of Fortune 500 uses it. There's been a developer-led event over the last, call it eight or nine years, where literally hundreds of thousands of developers across the world use Kafka to help power their businesses in real time. What the founders of Confluent did was they ended up leaving LinkedIn, they created Confluent, and they've used their skills and expertise to build a really strong team, and they commercialized Kafka into Confluent. They've also created a cloud-based version, which is a big differentiator. We run both on-prem and in the cloud. If you just take a look at some of the momentum, we've been public for a couple of years now. We serve a $60 billion total addressable market. Our last quarter results were very strong, we're basically on about a $300 million a year run rate. I'm sorry, $700 million a year run rate. Our revenue last quarter was $174 million, with 38% year-over-year. Our cloud revenue is on a $300 million run rate. That grew 89% year-over-year, and we've been balancing growth and profitability. We improved operating margin by 18 points. The stickiness of our products, we're an infrastructure play. We sit in the Next-Gen technology stack, with high gross retention, high net retention characteristics. This is not something that you deploy and rip and replace. This has a stickiness that comes out with 130% net retention. We have over 1,075 customers who spend over $100K a year. This is not a small state item. This is a place where people go, and they spend a lot of money, and they continue to spend. Once we land an account, the expansion opportunity is very, very large. You may ask: What are the use cases? What is Confluent used for? Confluent is applicable across every industry in the world. If you think about some of the things that we interact with on a daily basis, there's real-time sentiment analysis, there's fraud detection, there is inventory management, there's point-of-sale transactions, there's curbside delivery pickup. Think of anything that an enterprise needs to do to both run an efficient back end and provide a rich customer experience, all of this needs to be done in real time today. This revolution that's happened across the data landscape is only increasing. Think about the last, call it 8 or 9 years, where AI and ML have been very pervasive. Generative AI is gonna be another kind of next level up in terms of more data needing to be transferred in real time, and Confluent is driving these use cases across every industry. The core problem that I've alluded to is historically, data has been at rest in databases, and it's been slow daily batch processing or end-of-day things get updated. No longer will companies tolerate that behavior. They just won't. I mean, people need the real-time telemetry to run their businesses. The tools that have existed in the past have been legacy tools. They are point-to-point solutions, ETL, data integration vendors. Oftentimes, when I speak with investors, like, hey, there's a lot of cover marketing that goes on. They're like, Isn't this just like a TIBCO, or an Oracle, or Informatica? The reality is those solutions never delivered on the ultimate promise of running the business in real time, which is part of the founding or origin story, which I covered in the first minute or so, where engineers were tasked with running businesses in real time. The existing legacy solutions just didn't work. To make it worse, if you look at this picture here, with the legacy solutions, all these point-to-point environments created a giant spaghetti mess where you had SaaS applications, databases, data stores, et cetera, all over the place with, like, there's no rational way to run an efficient network with this architecture. What Confluent did was really come up with a new paradigm, which we call data in motion, and we're continuously processing streams of data in real time. We're taking the transaction fee events that are happening, and we're letting them flow through and connect with other databases, applications, and data stores. What this results in, taking that giant spaghetti mess of an architecture, we're creating a central nervous system where applications and databases can be run in real time. Think about the leverage that companies get by having a central nervous system as opposed to that spaghetti mess, and they can deliver these great use cases and real value to their customers. How do we serve this market? We have a data streaming platform, and it starts with the foundational elements of taking that original Kafka protocol, and we've put in a platform where we can serve customers on-prem, in the cloud, and multi-cloud environments. Think of this, I know many of you may not be core technologists, but data is everywhere. It's on-prem, it's in the cloud, and multi-cloud environments. You have to have a product that serves your customers wherever their data resides. One of the key differentiators around Confluent is that we work everywhere. We've built in security feature functionality, data governance, and then we put a whole suite of connectors and tools on top, and it's become a very powerful platform that we're delivering to our customer base. There are future growth drivers here. We just got into the space of streaming applications through an acquisition that we did that gets us into the Flink space. If you think about Kafka and Confluent being the connective tissue that's driving real-time data, application developers are going to need to be tapping into that real-time data and building these applications, and that comes with our Flink offering that will come out next year. At the end of this year, we'll commercialize it and really see benefit next year. Of course, again, GenAI will also be a catalyst in terms of incremental data that needs to be processed in real time. Where are we? Where do we serve our customers? Again, on-prem and in the cloud. Starting with our cloud product, we have Confluent Cloud. We can start with [audio distortion] engagement. It can be developer led. A developer come in and swipe their credit card and just start easy go, and that's a great onboarding mechanism. Then the Confluent Platform is our on-prem solution. We can, again, serve customers wherever their data resides. We are proven across every industry. This is just a small subset of our customer logos, but literally every industry can benefit from data in motion. Looking at a few financial slides, we've had significant revenue growth at scale. Since 2018, we've had a 72% CAGR. We've also demonstrated our ability to grow revenues quarterly on a sequential basis. Our cloud business is the fastest-growing part of our business. It's a 200% CAGR over since 2018, and the quarterly revenue growth of our Confluent Cloud business continues to demonstrate. Once we land in an account, the consumption-based model that we have for our cloud business, is a winning formula for us. We've also focused on managing both the growth side of the equation and the profitability side of the equation. We have been improving profitability. This is, this is just a way to show our progress here. You can see from an operating margin standpoint, we've improved the operating margin 15 points year-over-year in the last quarter. We have a target of non-GAAP operating margin break even in Q4 this year. We've been driving efficiencies across the rest of the company within sales and marketing as a percentage of revenue, R&D and G&A. We're definitely taking an eye towards improving productivity and increasing efficiency, so we're building an efficient growth engine. With that, I'll turn it over to Jason for any questions. Great. Thank you, Steffan. You're gonna be okay. You know, there's a lot to think about with Confluent. My observation is a lot of people just don't really understand what you guys do. I think that was a helpful overview. I guess for a non-technical investor, can you explain the concept of data streaming? Like, what does that actually mean, data streaming? Yeah. Data streaming effectively is every application that runs in an organization is throwing off streams of data. Historically, those streams of data would be put into a database, and in order to get that data out, you'd query it. What Confluent has done is basically we have unlocked the stream of data, so it's flowing freely. It's a continuous flow, and we've separated the compute from the storage. What we're able to do is, if you think about the central nervous system, analogy that I gave before, you have applications and databases that are proliferated across an enterprise, topology, right? Historically, they were never really connected. Now Confluent is the connective tissue that enables streams of data to flow freely across and amongst the topology. Why is that important? It's important because the speed at which customers are trying to run their businesses is only increasing. The telemetry that they're using to deliver rich front-end experiences to their customers and run efficient back-end operations is only increasing as well. I often get the question: How pervasive is streaming in a company? Like, does every application have to be streaming? The answer is, today, that's not the case, but it will be in the future. We've seen that in customer examples where they start with for a project that they need to have streaming applications. Think like in the financial industry, fraud detection. Yeah. Fraud detection, you don't turn it on and off. It has to happen all the time. Well, that fraud detection engine also needs to be basically, like, linked to other elements within a bank's infrastructure, like environmental management, you know, arm of a bank, et cetera. As one department or organization starts to run in real time, there is a network effect that happens, more applications and databases need to get connected in there. Ultimately, you know this from a financial standpoint, you look at retention rates, you look at the consumption model. We're seeing customers onboard more applications and databases into our data platform. Instead of connecting things on a custom basis, you're creating a standard, a common denominator technology that everything can sort of plug into. Once you plug into the system, then you can basically talk to everything else. That's right. You're right. Yeah. All right, you talked about the customers spend a lot with you guys. What does a typical customer journey look like? Is it like start with a specific use case, where they use an open source and then they convert over to you? Just give some examples. Yeah. The way that we typically start with customers is there's a need. The need from the customer is they need to start running their business in real time, and they have either tried the open source version, which, by the way, a lot of people are like, "Well, open source is free, so it must be better." What's free up front is actually very expensive down the road. In fact, when we run a TCO and ROI ratios we're 10X. We have a 10X better TCO and ROI proposition than the open source version of Confluent. What companies typically start with is usually one use case. They will look at it for a discrete project, and once they run Confluent in a discrete project, they understand the power that we bring to the table, and they'll want to expand. The very critical element here is anytime you touch one part of the business, and then you start to migrate out to other parts of the business, there's a mission criticality that happens, and security, feature, and functionality is incredibly important. The product suite that we have enables customers to, without a lot of heartbreak, go through a process of transforming from their legacy solutions to Confluent, and they get the security feature functionality. They get that performance, they get the TCO, it usually starts with one discrete project and then builds over time. We have this 5-stage customer growth and adoption journey. Many customers start without ever even talking to a salesperson. They'll go to our website, they'll go to Confluent Cloud, they can get started for free. Then once they see the power, they'll swipe a credit card, they become a customer, and then they will matriculate to what we call a committed contract. Then they'll come to us, and they'll say, "Hey, we started with a pay-as-you-go format in Confluent Cloud. Now we want to do something more significant. We want it to be more pervasive." The beauty of the cloud product that we have, it's scalable, it's elastic, it's a fully managed hosted offering. We don't need as many very expensive personnel running an on-prem version. We can use the cloud-native version. Think of like the tailwinds that happen just across the industry that benefits lots of companies, not just Confluent, the move of workloads to the cloud. The security feature functionality, Confluent Cloud taps into that in a big way. With the new product offerings that we're gonna be coming out with, you know, specifically for streaming applications, Flink, that will be a growth vector for us in 2024 and beyond. You talked about the rationale for Flink, and you touched on it a little bit in your slides. What does it look like breaking the table for you? Yeah. Okay, if you think about where Confluent sits in a next generation technology stack, we are an infrastructure player, and we're again, enabling all your databases and applications to be connected in real time and data to be flowing. Okay? Once all that's flowing, then how do you tap into all of the data that's in there and build streaming applications? The initial person we're selling to for Kafka and Confluent is the engineering architect, et cetera. With all of that data that's flowing, the next person we'll be selling to is the developer. The developer who's developing applications that is tapping to the real-time data flows. In today's world, you speak to any developer, a first order principle is they're developing applications with streaming in mind. That is the starting point today. The streaming applications that are happening that can deliver the point of sale, that can deliver curbside pickup, and deliver fraud detection, et cetera. All of those applications are being primarily written in a language called Flink. Flink is this open source project that didn't really have a lot of commercial mapping, and Confluent bought a company called Immerok to tap into the Flink open source project. Now we have Kafka in Confluent, which is powering real-time data in motion, and then we have Flink, that is all about the application developer developing real-time streaming applications. That is additional sales that we'll be doing. It'll be a cloud-based offering. It's gonna be offered in our, in our Confluent Cloud ecosystem. It's the next evolution of our company. This, guessing customers are kind of pushing you in that direction? Is that fair? Customers were absolutely like, with the, with all the engagement that we have, they've been coming to us and saying, "Hey, if you can help us unlock more value, we can do so by getting Flink." Some of the largest organizations in the world, are using Flink to write streaming applications, like [audio distortion], like many of the largest banks in the world, entertainment companies, they're already using this, but they're using an on-prem version, which is very costly, and it's very hard to manage. We're gonna be providing a cloud-based version, which basically takes a lot of the hard work out of creating this. We are really delivering a lot of value, just like we did with Confluent Cloud. Great. Let's talk about the competitive landscape in data streaming and stream processing. What we compete against the most and what we see the most? Yeah. The competitive landscape really starts with the open source Kafka world that's out there for Confluent. Again, we're, you know, there's, like, 150,000+ organizations that run Kafka for free. We have 4,690 paying customers. There is a very large opportunity for us to go after that open source community and convert them from Kafka to Confluent. That by far and away is the biggest competitor. There's no real direct one-for-one, like, competitor at scale against Confluent. There are cloud service providers that are out there that offer competing solutions, we have what I call a, like, co-competition dynamic with AWS, Azure, and GCP. They all are key partners of ours. They all have some form of competing product. Ultimately, those companies are agnostic around which solution they sell because they are very much focused on getting as many workloads to their infrastructure as possible. We've driven hundreds of petabytes of workloads to the cloud service provider infrastructure. The 3 cloud service providers have their sales representatives quota when they sell Confluent. We have strategic collaboration agreements with them. We wake up, we're living and breathing data in motion, data streaming and streaming applications. For the 3 cloud service providers, they have lots of stuff going on, and this is just 1 part of what they're trying to do. Gotcha. I have to ask you, the GenAI, the requisite GenAI question. How does Confluent benefit from GenAI? In the space of GenAI, how I think about it is all the large language models that need to effectively be updated in real time in order to provide a really good customer experience, will be a catalyst for increased data in motion and data streaming. Confluent is very well positioned and situated to enable the overall ecosystem out there to have their data in motion and data streaming. For the companies that are really embracing GenAI and having either their proprietary models or, you know, for internet companies, if they're more open source. It's all about real-time data, and that is pretty to our benefit. I'm going to open it up to audience. I see questions. Gentleman in the back there. On AI, have you seen in the last 4 months, at least the initial AI workloads that have been released, have you seen them use your product or come to your product? I think the question was, in the last 4 months, have we seen... Four, five months. Four, 5 months, have we seen any increase GenAI decision in our customer base? Is that it? That's correct. Yeah. The short answer is, like, what we've seen is customers, existing customers of ours have been improving their, like, chatbots that leverage data in motion. Like, you know, we have a large travel company that is a customer of ours that has been building this over time. It's something that we will continue to see them, like, leaning into data in motion. There has not been any sort of, like, you know, hockey stick around data usage at the moment. That's something We think will improve our benefit over time. The second question is, do you think that as the AI workloads become, right now, it's mainly strictly related to public information, but as the workloads start, AI workloads start to dig into proprietary data, that's what you mentioned, dig into internal data sets, that then your product will actually become more strategic? That's what it may not have to say, but that's where you are reflecting from an AI standpoint, or does that make sense? Yeah, no, that makes sense. You know, AI and ML have always been core use cases that have powered our business. With Generative AI coming into the mix and the proprietary models that you will need to be updated, that should improve our benefit. I will tell you that the way that Confluent's revenue model has worked and the purchasing patterns for our customers, this tends to be a slow build over time because applications and databases often have to be replatformed, reconfigured in order to, like, benefit from our solution. This is something where, like, the AI, ML, and now GenAI will ramp over time, and it should be a catalyst of growth for our business. I think there was another question over there. Yeah. How has cloud optimization affected your business or work? Cloud optimization, we are less, like, less exposed to massive cloud optimization than other companies because we sit lower in the technology stack. Oftentimes, the workloads that are put through our platform come pre-optimized. When companies are looking to save costs, they're oftentimes changing their storage requirements, or they're running less queries, or they're changing the frequency around observability. Those things typically are farther up the stack than we are. This is not to say that we're totally immune to cloud optimization workloads, but we've been able to manage through it. I'd say we, you know, we have a little bit of exposure to it, but we don't have a ton of exposure to it. We'll take 2 more. We're going to wrap up. This follows the AI question. You were splitting it up into training and inference. Does Confluent come in on the inference side after the LLM has already been built out, or is that going to be pushing the data into the training aspect that you have? Yeah, it's a combination of both. I would say the first part would be training because it's helping get the data into the model itself, for sure. Then the inference side of the house, this is something where we believe that the acquisition that we did of Immerok to get us into the Flink space, streaming applications will be part of the GenAI space. As from an inference standpoint, we will be playing in that definitely. Last question. Interesting question on the sales and marketing at 56% of sales. When would you expect that to come down, and could you explain how that gets leveraged over time? Yes. Sales and marketing as a percentage of revenue has been coming down. It's still high, but it has been coming down. A year ago, it had a 6 handle on it. Our midterm target model is to get sales down into the low to mid-30s. That's something that we're going to drive through by increasing sales efficiency and productivity. We also have this land and expand motion. The cost of customer acquisition, once we acquire a customer, there are network effects that enable us to sell incremental workloads at a lower cost of sales. When you look at some of the cohort math that we've shown in the past, the profitability of customers over time increases pretty dramatically. Awesome. All right. Thank you, everybody, for coming. Thank you, Steffan. Thank you so much. Great day.