Hi, everyone. Welcome to the Confluent Q1 2022 earnings conference call. I'm Shane Xie from Investor Relations, and I'm joined by Jay Kreps, co-founder and CEO, and Steffan Tomlinson, CFO. During today's call, management will make forward-looking statements regarding our business, operations, financial performance, value creation potential, and future prospects, including statements regarding our financial outlook for the fiscal second quarter of 2022 and fiscal year 2022, the potential growth runway for Confluent Cloud, and future year outlook for non-GAAP operating margins. These forward-looking statements are subject to risks and uncertainties that could cause actual results to differ materially from those anticipated by these statements. Further information on risk factors that could cause actual results to differ is included in our most recent Form 10-K filed with the SEC. We assume no obligation to update these statements after today's call except as required by law. As a reminder, certain financial measures used on today's call are expressed on a non-GAAP basis. We use these non-GAAP financial measures internally to facilitate analysis of our financial and business trends and for internal planning and forecasting purposes. These non-GAAP financial measures have limitations and should not be considered in isolation from or as a substitute for financial information prepared in accordance with GAAP. A reconciliation between these GAAP and non-GAAP financial measures is included in our earnings press release and supplemental financials, which can be found on our investor relations website at investors.confluent.io. With that, I'll hand the call over to Jay. Thanks, Shane. Welcome, everyone, to our first quarter earnings call. We had a successful start to the year, exceeding our guidance on all metrics. Total revenue grew 64% year over year to $126 million, a notable milestone as we surpassed the half- billion- dollar revenue run rate mark. Confluent Cloud grew 180% year over year, representing 31% of total revenue. Confluent Cloud is not only the fastest- growing part of our business, but it also serves the vast majority of our customers. In the first quarter, we closed the largest deal with Confluent Cloud in our company's history. This eight-figure, multi-year expansion deal with a massively high- scale tech company is significant on a number of fronts, which I'll touch on further in a few minutes. Today, I'd like to use the time to dive a little deeper into the use cases driving Confluent's success and the rise of data in motion. The underlying trend behind this shift is that we are experiencing a phase change in how companies use software. Software is moving from siloed applications on the edge of the business to fully connected applications that drive core parts of the customer interaction and the production of goods and services. This transition is imperative for companies to compete in the modern economy. At the heart of all this software is data, and we're seeing an equally large shift in the underlying data architecture to enable this software-driven business. Historically, data infrastructure has been built around data storage, and there's a rich set of file systems, databases, and data stores that allow applications to store and retrieve bits of data. However, increasingly, this storage-centric paradigm is not sufficient. As companies drive more of their core business with software, that software has to have an up-to-date view of the business and be able to react and respond intelligently as the business carries out its core activities. This has driven a shift from a storage-centric world of data at rest and has enabled the rise of data in motion. Broadly speaking, data in motion is about connecting disparate systems by extracting data as continuous streams and allowing these streams to flow to the rest of the applications and infrastructure that need that data, as well as allowing companies to react and respond to that stream of data in real time. Data in motion is now adopted incredibly broadly, with a presence in hundreds of thousands of companies in virtually every industry. Giving a complete overview of all use cases is an impossible undertaking. However, in today's call, I'd like to give an overview of some of the patterns we see illustrated with some examples. To engage with customers in the most meaningful and personalized way to foster brand loyalty, companies need to have a data-rich, 360-degree view of all aspects of their customers across all interactions. Confluent not only captures changes to the data as it happens but also stitches together data from disconnected databases, files, and custom applications to deliver a real-time view of all customer interactions, enable real-time engagement across all channels, and reshape experiences. Once this continuous view of the customer is present, stream processing enables the real-time reaction to customer events to drive personalization, so that the right suggestion, experience, or recommendation can be delivered to each customer at the right time. That's what Deutsche Bank does for their passengers. They have a single source of truth for all the vital travel and train information and make it available on the mobile app, website, even station displays, and public announcement systems. Real-time data is what improves the customer experience and gets happy passengers where they want to be on time. Another common pattern our customers embrace Confluent for is data mobilization across hybrid and multi-cloud architectures. Most enterprises on a cloud journey are finding it harder than expected to realize their benefits as the transition to the cloud is often an incremental, multi-year effort that's arduous and expensive. Whether it's modernizing their legacy on-premises data warehouse to fully managed cloud-native systems like Snowflake, BigQuery, Redshift, Synapse, and Databricks, or modernizing their monolithic applications to the cloud one microservice at a time, many of our customers use Confluent as the unifying persistent bridge, enabling data to flow freely between the old legacy stack and new cloud applications wherever it resides, on-premises and in more than one cloud. This pattern of running Confluent to span environments is increasingly common, and Confluent is becoming a critical data fabric for enabling integration across multi-cloud and hybrid cloud environments. That was certainly the case for SecurityScorecard. They're the global leader in cybersecurity ratings and the first in their industry to offer digital forensics and incident response services. Operating in 64 countries, they continuously rate more than 12 million companies. Their business requires a hybrid cloud architecture, so they turn to Confluent, and now data streaming helps them communicate instant ratings and constantly scans for threats to improve their customers' security posture. We're proud to work with them on their mission to make the world a safer place. In financial services, data in motion has become a mainstay of modern architectures, from small FinTech to the largest banks in the world. In capital markets, for instance, we're driving better business outcomes by enabling enterprises to gain a firm-wide view of their trades and risk. Retail bankers use Confluent for secure real-time payments, so their customers can transact with their banks with speed, confidence, and trust. Fraud has become a growing problem in payments and is harder to detect than before, and many customers in the payments industry are using us for real-time fraud detection. Adoption of data in motion in financial services is incredibly broad. The top 10 largest banks in the U.S. are all Confluent customers, and we're seeing ongoing demand across the world. For instance, at Bank Rakyat Indonesia (BRI), the largest bank in Indonesia and the largest microfinance institution in the world, Confluent powered their digital transformation with an event-driven architecture for real-time credit scoring, fraud detection, and merchant assessment services. Now they're able to detect ATM skimming in real time, block and disable the cards of their customers, and proactively protect their customers from fraudulent transactions. They've also been able to reduce loan disbursement times from two weeks to two minutes with automated digital verification by processing massive amounts of information in real time, all flowing through Confluent. In retail, Confluent serves as a massive advantage for retailers looking to meet the demands of heightened expectations from customers for real-time, omni-channel personalized experiences. Many retail organizations use Confluent to fundamentally transform inventory and supply chain, and for hyper-personalization to enable real-time customer interactions that build brand loyalty. In the U.S., nine of the ten largest retailers are Confluent customers. Confluent helps Sainsbury's reimagine its supply chain and inventory management by enabling a continuous view of product inventory. By using Confluent, they can drive lower inventory levels while avoiding selling out and enable agile response to changes in supply and demand. Confluent also acts as interconnectivity between different cloud providers like AWS and Azure, adding a secure, fault-tolerant, real-time data pipeline that spans these environments. This means they're protected if any cloud service experiences downtime, which greatly improves the strength and flexibility of their supply chain. One very compelling area of opportunity that Confluent is growing into is the tech sector. Tech companies were the earliest adopters of open- source Apache Kafka but were not typical customers for earlier open- source companies. The emergence of cloud as a delivery model has changed that. Our cloud services reach scale, cost efficiency, elasticity, and reliability that no internal self-managed data system can hope to achieve. As we've done this, we've seen some of the earliest Kafka adopters start to shift to our service, including companies like Square that have been mentioned on a previous earnings call. This customer segment is significant because it has been built around data in motion in a very foundational way and has masked huge scale. As I mentioned at the beginning of this call, this quarter we added a very significant new customer in this segment that runs one of the largest Kafka installations in the world. New Relic is the leading observability platform that helps engineers plan, build, deploy, and run great software. New Relic is fundamentally a service for providing powerful insights into streams of observability data. Indeed, Kafka is the backbone of all observability and log data ingested into their platform. New Relic is the kind of company that's always thinking about their customers and always innovating. Our expertise, having re-architected Kafka for the cloud and the proven scalability and reliability of our fully managed multi-cloud platform, are foundational to this new partnership. Customers can benefit from the joint product innovations we have planned with New Relic, allowing for more insights from their Confluent Cloud telemetry data. We're very excited for the future and how we'll innovate and create even more value going forward. The success with all these customers is driven by the deep differentiation of our product. Confluent builds this differentiation around three key pillars: being cloud-native, being a complete offering, and being everywhere. We've discussed these pillars quite a bit on past calls, but we continuously add to them to deepen our competitive moat and better demonstrate value to internal IT teams who might otherwise be building around open source, Kafka. First, being cloud-native, we announced various new capabilities that make our platform more scalable, more elastic, and more reliable. These include greater scalability and unlimited data retention in Azure. Now across all three clouds, customers can scale up to enormous data throughput and store data in Confluent Cloud forever without any limits on size or retention. Finally, we improved our observability and security capabilities, allowing richer insight into the use of Confluent and more detailed audit tracking of actions taken with our product. These capabilities come together with our operational practices to allow us to deliver industry-leading reliability around data streaming. In recognition of this, we've strengthened our contractual SLA to 99.99% as Confluent Cloud offers unmatched reliability for real-time streaming. Our second pillar of being a complete offering continues to be an area of ongoing innovation for us. We expanded role-based access control to help enable secure usage across large companies and teams while still ensuring tight control on data access. We added 6 new fully managed connectors to Confluent Cloud, and our Oracle CDC Source Premium Connector went GA. This allows us to set in motion the vast amount of data at rest locked up in Oracle databases, a mainstay technology in our enterprise customer base. Our third pillar of product differentiation, being able to run everywhere, manifests in our ability to run across all our customers' environments, whether on-premise, hybrid, or multi-cloud. In this area, we simplified the user experience for Cluster Linking, our proprietary technology that allows transparent linking of clusters across cloud providers and on-premise clusters. We also had a major release of Confluent Platform, which supports on-premise and private cloud environments. This new release brings hundreds of new features and improvements that were first launched in our cloud to our customers' on-premise environments, enabling richer hybrid cloud deployments. A key part of our strategy to be everywhere is our relationship and integration with the three major cloud service providers. These relationships are critical for us, but also mutually beneficial for the cloud providers because Confluent enables data flow from on-premises environments into the cloud, unlocking use cases that would otherwise be tied to legacy environments by data gravity. In our last earnings call, we announced a significant deepening of the partnership with AWS. This quarter, we followed that up with the announcement of a new multi-year strategic partnership with Microsoft, which extends our existing relationship with joint technical, marketing, and sales investments across our organizations. This deeper partnership helps us tighten the integration with Azure as well as better serve joint customers. We also had two notable announcements around our partnership with Google Cloud. The first was the founding of the Data Cloud Alliance, a new initiative that aims to make data more portable and accessible across disparate business systems, platforms, and environments, with a goal of ensuring that access to data is never a barrier to digital transformation. Additionally, we have been recognized in the Google Cloud Ready for BigQuery, which ensures the best possible integration with BigQuery using our fully managed connector. As part of this program, Confluent will collaborate closely with Google partner engineering and BigQuery teams to develop joint roadmaps and continue improving our products. By driving deeper alignment, we are making it easier for our customers to connect and migrate hybrid and multi-cloud data to BigQuery to power real-time analytics. To wrap up on the point of product differentiation, the value of Confluent versus open- source Kafka can be summarized from the findings of Forrester's Total Economic Impact Study, which they recently conducted for Confluent. The study specifically quantified the cost savings and benefits that businesses can achieve when they offload the burden of self-managed Kafka to Confluent. Overall, Forrester identified TCO savings of more than $2.5 million for businesses that used Confluent, which translates to an ROI of 257% with a payback period of less than six months. The two key areas of savings included development and operations cost savings of over $1.4 million, plus scalability and infrastructure cost savings of over $1.1 million. Perhaps most importantly, Confluent enables organizations to free their teams to focus on strategic efforts that drive competitive differentiation versus managing the underlying data infrastructure. We think this is a key point of understanding that's emerging. Confluent is not just better and faster than self-managed open source, but can also be cheaper as well because of the high expense of cloud infrastructure and developers. We have vast economies of scale by offering these services to thousands of customers. We think these cost savings are a critical aspect of our value proposition and have made us successful in both companies focused on innovation as well as those focused on cost savings and efficiency. A great example of this ROI is Swiggy, India's leading on-demand food ordering and delivery platform that serves millions of customers every day. To connect those customers with hundreds of thousands of restaurant partners and all of their drivers requires real-time data. Originally, they managed their own Kafka clusters, but the time and energy spent on that deviated from delivering key business goals. Swiggy needed a fully managed solution to refocus their engineers' time, reduce costs, and handle significant spikes in demand. Speed of delivery is a competitive advantage for Swiggy, and Confluent is at the heart of their data in motion architecture. Another key competitive advantage for Confluent is our customer growth go-to-market model. We discussed this in detail on our last call, but I wanted to provide a brief recap on our strategy and some updates that we've made since last quarter. Our go-to-market effort is product-led, consumption-oriented, and purpose-built for data in motion, aimed at driving customer lands and growing usage of our product from early experiments to large-scale central nervous systems. We continue to innovate at each stage in this journey. In the first quarter, we made it even easier for developers to get started with Confluent by allowing sign-ups with existing Google and GitHub account credentials, as well as removing our credit card paywall, allowing developers to test drive our product without the hassle of adding payment information. We also expanded our developer learning center, developer.confluent.io. We launched expanded training materials for Kafka and Confluent, including material written and presented by one of my co-founders and Kafka's original co-creators, Jun Rao. In addition, we've started a library of code samples and step-by-step tutorials to help customers apply Confluent in common use cases, such as the ones described in the call today. This effort helps us to train the next generation of Kafka users on Confluent Cloud. It helps us more quickly progress our customers to additional use cases and applications. The success of this strategy was reflected in a significant increase in sign-ups and another record quarter of customer additions, growing our total customer count 62% year-over-year to approximately 4,120. This has continued to be a driver of the strong growth in our customer base, with 100K or more in ARR, which grew 41% year-over-year to 791 customers. Finally, the spread of use cases, the powerful network effect, and the customer's desire to build out their central nervous system with Confluent are reflected in the growth of our largest customer base. Growth of customers with $1 million or more in ARR accelerated to 62% year-over-year, ending the quarter with 97 customers. We are still in the very early innings of this opportunity and look forward to what's ahead. We also recently hosted our first in-person Kafka Summit since 2019. More than 1,200 members of the community gathered in London, and many more tuned in to the live stream. I've got to tell you, it was great to be back together again. The prevalence of Kafka is also evident in the huge growth of the community. We've seen hundreds of thousands of organizations adopt Kafka, tens of thousands attended meetups throughout the pandemic, and a rich community working to document, improve, and contribute to Kafka. We also announced our new data streaming industry event, Current 2022, the next generation of Kafka Summit, where we will bring the preeminent thought leaders and experts in data streaming together in Austin this October. We invite you to join Current 2022 to learn more about data streaming and our leadership in this space. It's hard to measure open source adoption, but I'd like to share one illustrative statistic today that shows the strength of the growing movement around Kafka and data in motion. Many stats, such as downloads, aren't available for Apache projects, and stats like GitHub stars aren't very representative of actual production usage. One data source we do look at is the active unique IPs using the Kafka Java library, which is available from the company Sonatype that distributes those libraries. This is a comparatively rigorous measure. Because of the deduplication by IP address, full companies may appear only once. Duplicates and automatic downloads are suppressed, and users who do not remain active fall out of the measure. On a trailing twelve-month basis, Kafka downloads grew over 50% year-over-year. People often ask about the competitive landscape, and the reality is that we don't feel Kafka has a close competitor in terms of scope of usage, breadth of ecosystem, or developer mindshare. As an illustration of this, it's worth considering the adoption rate of one of the most commonly mentioned competitive systems, Apache Pulsar. Apache Kafka sustained a significantly higher growth rate on a percentage basis than Pulsar, despite the fact that Kafka's growth rate is off a user base that is over 10x larger. This sustained superior growth at scale is what has made Kafka the de facto standard for data in motion and is a tribute to the strength of the Kafka community, the massive ecosystem of integrations, the network effect inherent in data streaming, as well as the simplicity and superior performance that Kafka offers. Before turning to Steffan, I want to highlight a key leadership hire in the first quarter. Gunjan Aggarwal, our new Chief People Officer. Gunjan joined us from RingCentral and is a 20-year industry veteran whose team's efforts have been widely recognized with an A-plus culture rating and a host of awards for diversity, happiness, and leadership. I look forward to working with Gunjan as we continue to scale our team and culture. We intend to continue to attract top industry talent in every function and create an organization that gets even better as it gets bigger. With that, I will turn the call over to Steffan to walk through the financials. Thanks, Jay. Q1 was another excellent quarter of operational execution and delivering on our commitments, with results exceeding the high end of our guidance on all metrics. Key highlights include strength in our cloud business, accelerating growth of remaining performance obligations, robust revenue growth, and strong unit economics. Q1 RPO accelerated to 96% growth year-over-year, reaching $551.1 million, of which we anticipate approximately 60% to be recognized as revenue in the next twelve months, representing 66% growth year-over-year. Confluent Cloud is powering the growth of RPO. We closed the largest deal in the company's history with Confluent Cloud. This eight-figure, three-year deal had an immaterial contribution to current RPO, and the vast majority of the contract value is reflected in our non-current RPO. Additionally, we recognized zero revenue from this deal in Q1. The deal marks another key milestone for our company and enhances the durability and visibility of cloud revenue growth in the out years. Total revenue in the first quarter grew 64% year-over-year to $126.1 million. Subscription revenue grew 68% year-over-year to $113.9 million and accounted for 90% of total revenue. Within subscription, Confluent Platform revenue was $75 million, up 39% year-over-year, and accounted for 59% of total revenue. Confluent Cloud revenue exceeded our expectations, up $5.1 million sequentially and up 180% year-over-year to $38.9 million, accounting for 31% of total revenue, a 13-point increase from a year ago. We're also encouraged by the underlying momentum we continue to see in our cloud business. For the second quarter in a row, cloud accounted for more than 50% of new ACV bookings, marking a mix shift in the business we had anticipated, but that is happening faster than we had expected. Cloud also represents the vast majority of our total customer count and has a world-class net retention rate of greater than 150%. Our outperformance in cloud reflects not only the substantial progress we've made in all the verticals we've been serving, but it also demonstrates our ability to expand into the highly strategic digital native segment. This momentum is being powered by our customers' strong desire to have our fully managed multi-cloud offering, which has superior product capabilities and lower total cost of ownership compared to open source. Confluent Cloud abstracts away the complexities of managing and scaling open source Kafka on its own and relieves the burden of hiring Kafka engineers in the current labor environment. We see a long runway for Confluent Cloud to grow substantially over the long term. Turning to the geographic mix of revenue, the US grew 60% year-over-year to $79 million. Revenue from outside the US grew 70% year-over-year to $47.1 million. Because we price in USD globally, we don't expect foreign exchange to be a direct headwind to our top line. The net retention rate in the quarter remained above 130% for the fourth consecutive quarter as we continue to see very strong gross retention and expansion across both of our product offerings. As mentioned previously, NRR for cloud was greater than 150%, and NRR for hybrid customers running both platform and cloud continues to be the highest. Moving on to gross margins and profitability, I'd like to note that I'll be referring to non-GAAP results unless stated otherwise. Total gross margin was 69.7%, and subscription gross margin was 75.5%. We are pleased with our ability to maintain healthy gross margins even as cloud accounted for a larger share of revenue. We've driven substantial improvements in cloud gross margin, and we remain focused on increasing our cloud gross margin over time. In the near term, we anticipate total gross margin to fluctuate near our midterm target of approximately 70%. Turning to profitability and cash, the operating margin was -41%, which represents a bit relative to our guidance. This was partly driven by increased sales productivity, lower- than- expected spend from in-person events, travel, and real estate, and other operating efficiencies that we're continuing to drive throughout the business. Free cash flow margin was -46.3%, which was better than our plan. As mentioned on our Q4 earnings call, we expected free cash flow margin to be the lowest in Q1, driven by the timing of cash flows related to our new corporate bonus program and employee stock purchase plan. Net loss per share was -$0.19 using 272.9 million basic and diluted weighted average shares outstanding. We ended the first quarter with $1.99 billion in cash equivalents, and marketable securities. Our exceptionally strong balance sheet gives us the flexibility to fund our growth plans as we steadily move towards profitability. Looking ahead, I want to continue to share our plan for managing growth and profitability, which is guided by our framework. The level of investment is informed by our track record of delivering on our commitments, our significant market opportunity, and by assessing unit economics such as our consistent and strong NRR, increasing sales productivity, and improving cloud gross margin. We remain committed to delivering high revenue growth and annual improvement in margins in 2022 and plan on accelerating the rate of margin improvement in 2023. Based on our current top line growth projections and investment priorities, we plan to exit Q4 2024 with a positive non-GAAP operating margin. Turning now to guidance, we're raising numbers for the quarter and the year. For the second quarter of 2022, we expect revenue to be in the range of $130 million-$132 million, representing growth of 47%-49% year-over-year, non-GAAP operating margin to be approximately -41%, and non-GAAP net loss per share in the range of -$0.21 to -$0.19, using approximately 279 million weighted average shares outstanding. For the full- year of 2022, we now expect revenue to be in the range of $554 million-$560 million, representing growth of 43%-44% year-over-year, non-GAAP operating margin to be approximately -38%, and non-GAAP net loss per share in the range of -$0.79 to -$0.73, using approximately 282 million weighted average shares outstanding. I'd also like to provide some modeling points. We expect sequential revenue dollar growth for cloud to be the lowest in Q1 and will increase each quarter for the remainder of the year. Our margin guidance for the second quarter and full year takes into consideration an increase in expenses related to travel, in-person events, real estate, and facilities throughout 2022. As a reminder, free cash flow margin is expected to be the lowest in Q1, followed by Q3, and we expect free cash flow margin to trend roughly in line with non-GAAP operating margin. In closing, our strong first- quarter results underpinned our momentum and leadership position in data streaming. Since becoming a public company, we have proven our ability to execute and deliver on our commitments consistently. Capitalizing on the secular trend of digital transformation and cloud migration, we're well-positioned to drive continued high growth and deliver annual margin improvements. With that, Jay and I will take your questions. Great. Please raise your hand if you'd like to ask a question. I'll pause for a few moments for our team to assemble the Q&A roster. Our first question today comes from Sanjit Singh of Morgan Stanley, followed by William Blair. Sanjit, please unmute. Hey, Sanjit. Hey, Jay. Good to see you. Good to see you, Steffan. Really appreciate the Pulsar data. Something that we've been looking for and just sort of highlights the adoption of the market of Kafka and the scale that you're seeing. Thank you for that. I just wanted to talk product, and congrats on the good start to the year. As Confluent Cloud takes on more and more percentages, a bigger part of the business, how is the customer experience going from, say, one use case, let's say if they're doing fraud detection and then wanted to do like a database use case or observability use case, being able to go from use case number one to two, three, and four, how is that progressing? How much effort do they need, or how much hand-holding do they need from Confluent or professional services help to get there when they're doing it on cloud versus on-prem? Yeah. This is one of the things that makes cloud so exciting. You know, with kind of self-managed infrastructure, there is a pretty high burden. You're often, like, buying servers and adding capacity and setting up new components in the offering. That all goes away, right? You can expand elastically. The components are all there if you want to use them as part of our platform. Maybe most importantly, we can start to put together these patterns. I mentioned briefly these recipes that we now have available for use cases, which kind of specify something end- to- end in ksqlDB. This makes it much easier to take something that's a lot like what you wanna do and maybe modify it for your use case and just get going, and we believe that will allow us to drive much faster adoption. Now, obviously, we're seeing good adoption, you know, even in our on-premise customers. But for each use case, even though that's spread overall, maybe, you know, fast, each use case takes, you know, some time to build up. As our tools get higher level and as cloud enables, you know, faster progress, you know, we think that can improve over time, which is part of the reason we're kind of leaning into it so heavily, and that was illustrated. You know, we broke out the NRR for cloud as being, you know, greater than 150%. I think it's illustrated by that. It makes total sense. One follow-up, again, sort of on product and more on product adoption. A theme of your presentation was around newer industries coming online and adopting them. Can you give us sort of the history? Like, well, who were sort of the early adopters? Then as we get into the pandemic, what were sort of the motivations of some of those newer industries to come online? What's driving some of the, I guess, who would you call out as sort of under-penetrated in terms of Kafka or Confluent adoption today that's starting to ramp up? It's been interesting to watch. The early adopters were all the tech companies. It was Silicon Valley, right? You would see this as a massive platform in the big tech giants, and, you know, we're interestingly just coming to them as Confluent customers now, right? They were open source adopters. You would see that with, you know, Instacart or Square or this time around New Relic. New Relic has been using open source Kafka for quite some time at scale, you know, now switched over to our managed offering. From there, probably around the time that Confluent got started as a company, we started to spread to other industries, and the spread in each industry has been. It's almost like top-down and bottom-up. These very small tech companies will be early adopters, and then kind of the apex companies in each industry, you know, the ones that have the most significant investment in technology and are the most sophisticated about deploying things, they're first. Then it kind of spreads to the middle. You would see that in, you know, financial services, which was an early adopter, retail, which was an early adopter, where now there's pretty good penetration, and those top companies work kind of well out into the middle. You know, some of the areas where I feel like there's more opportunity, you know, relative to what we've done so far, public sector, I think there's enormous opportunity there. You know, telecom, healthcare, I think these are areas where we obviously have, you know, wonderful marquee customers, but, you know, we're still just getting full coverage of the logos, let alone, you know, starting the larger growth. I think there's a lot of opportunity even in these industries where we have, you know, kind of at least some use case in each customer. Of course, our goal is to grow that significantly. Yeah, I think some of the industries that have been a little slower are probably the ones that actually have less competitive pressure to kind of innovate around technology, and that's at least been our internal explanation for why some industries which have, you know, huge use cases around real-time potentially have been a little slower to pick up the new wave of technology. Okay. Yeah. Thanks, Sanjit. We'll take our next question from Jason Ader of William Blair, followed by Derrick Wood. Jason? Yeah, thanks. Can you guys hear me okay? Yep. Absolutely. Great. I guess, you know, it's really good growth, but it's the smallest beat you've had as a public company, and I understand the dynamics around the kinda shift to cloud maybe happening faster than you expected. My question is really on the go-to-market side, and I guess towards you, Jay. Where are some of the areas you feel like you could be getting even better leverage to kinda execute on what clearly is, you know, pretty tremendous market demand? And I'm thinking specifically maybe about partner ecosystem, GSIs, ISVs, et cetera. You talked a little bit about Azure. I know you also deepened your relationship with AWS recently. Can you just talk through, you know, kind of the puts and takes there or the. Where do you really see the most bang for your buck? Yeah. I think there's two things that are building that will contribute more over time. You know, one is the partner effort, which you mentioned, the SIs. I think that helps accelerate what we're doing, take it into other areas. I think the, you know, the other one I would, you know, describe is the awareness of the category and brand. I think for any new thing, as that builds, it does kind of transition from something that's, you know, new and nobody's heard of to something that's very mainstream to something you have to do to, you know, stay up to date. Now, I think that's been building for us, just the awareness among senior personas in organizations. You know, that's changed dramatically over the last few years, and I think that's a huge help for us in just, you know, enabling us to spread faster in an organization. Then I touched already on, you know, both what cloud enables as a product, but it's not just about having the product features. You know, this customer growth go-to-market, really taking advantage of this to be able to land as quickly as possible, be able to drive that spread across the organization, you know, I think that's something that we've really kind of started to build around as a philosophy, and I think as that kinda comes to full fruition, I think that's gonna be one of the really powerful things that can allow us to move even faster. Great. A quick follow-up for Steffan. Steffan, are you contemplating any kind of macro impact in your guidance for either Q2 or the year, either you know, Russia-Ukraine, FX, or anything else in that guidance? Well, first off, the demand environment remains very strong for our technology and in the setup for our company. When we look at guidance, we absolutely take in signals from different geographies. We look at the sales pipeline, we look at close ratios, et cetera. We reflect that in both our Q2 and our fiscal year guide. I can say that I'm very pleased that we're able to beat and raise this quarter and for the year. So all of that macro is factored into our guide. Now, if things change, we'll reassess, but we feel very good about our position heading into not only Q2, but for the balance of the year. Thank you. Jason, we'll take our next question from Derrick Wood of Cowen, followed by Wells Fargo. You guys, one of the metrics that really stuck out is this customer number, the total customer number. If my math is right, you had about 650 net new customers in the quarter, which was a big step up from historic levels. I mean, if you could put your finger on it, what was the change factor? I mean, we've been seeing this rise in new customer generation, but it does seem like it took another step up forward. Anything you'd call out in terms of what's driving that? Are the majority going to cloud, or is there some sort of mix also still choosing? Yeah Platform? Yeah. The vast majority of the total customer count is cloud. That's because, of course, you can start very easily in a kind of frictionless way and build up. I touched on a few of the kinds of product innovations that are actually coupled with go-to-market changes to help that, you know, removing the paywall in the product, allowing people to log in with existing accounts from, you know, Google or GitHub. A lot of these low-friction things really help. Our goal is to make this the default way of getting started with Kafka or any kind of streaming project and just make it the first thing developers do rather than, you know, have you kind of start with the open source and then convert over at a later time. I think we're still building around that. That's a stat which I do expect will kind of fluctuate over time. You know, we see it as a very important thing to build this broad base at the bottom, and we've been pleased with how it's built. Obviously as we kind of experiment with this, it does go up and down more. We were pleased with the results this quarter, though. Great. Jay, both you and Steffan kind of referenced the tighter labor market and maybe wage inflation and how your cloud platform can really help address the cost that would normally come from open source. Are those things actually coming into conversations with customers and perhaps driving a bigger flywheel of open source to commercial conversion? Yeah, absolutely. I would say, you know, both in the tech world and in mainstream enterprise, you saw these very large kinds of platform teams that ran internal infrastructure efforts. Of course, Kafka itself came out of one of those that I ran at LinkedIn. You know, there are pros and cons to that, right? There's lots of innovation that can happen on this internal, you know, infrastructure, but it's extraordinarily expensive. You know, if you have the best 50% of your engineers working on internal infrastructure, that's a lot that's not going into innovation in that company. I think that there's a mindset shift overall. You know, I do think in uncertain times, that shift tends to happen a little faster 'cause people are actually looking at efficiency. Even in the absence of that, just, you know, the competitiveness in the market does make people think about, "Hey, where? If we can't get the people, if we can't retain the people, what? Where are those people going? You know, what are they working on? Are they working on the things that are our special sauce, or are they working on these underlying data layers where we have another solution?" That makes them kind of look outside their walls, and that's a very important change. I think you're seeing that in tech, which is opening that up. You're seeing it in a lot of the large enterprises that would have similar kinds of internal teams that would, you know, operate infrastructure at scale. I think that powers the whole move to cloud. It's a huge deal for the, you know, open source companies like us where, you know, back in the day on-premises, you might have, you know, a relatively low conversion rate from open source to paying customers. But now in the cloud, I, you know, I think you can have something which is, you know, a better product, but also a better deal, like cheaper to operate when you take into account all the infrastructure costs and especially the people costs. Great. Thanks. Congrats on another strong quarter. Yeah, thank you. We'll take our next question from Michael Turrin of Wells Fargo, followed by Credit Suisse. Hey, thanks. Good afternoon. Appreciate you taking the question. Jay, we're fielding lots of questions across software on just the overall macro demand environment and how certain markets could perform in a stress test. The event streaming market is still in its infancy. The net new customer adds metrics remain healthy and strong. We've seen growth from the hyperscalers prove more resilient here thus far as well. What would you say to investors who are just asking around the durability or where the spend and demand comes from within that context? Yeah, there's a couple things I'd point to. You know, we've been really happy, you know, with the growth and overall trajectory of the demand environment. I think that, you know, because we tend to serve application layer software, there's less risk of it kind of disappearing overnight, right? It's not the kind of thing you can just turn off. Beyond that, because we have access to this large installed base of open source users, I showed that graph of Kafka adoption. You know, even in the event where new use cases slow a bit, right? Which we haven't seen, but were that to be the case, you know, just actually going out and bringing more of the existing open source user base into the platform as there's more pressure on operational efficiency, you know, is absolutely a significant path for growth for us, you know, even in the current environment and can accelerate in that environment. So yeah, we haven't seen a big change. We're kind of watching it closely with the you know, same level of paranoia. You know, even just, I was out in Europe for the Kafka Summit, conversations with customers, you know, I would say the level of enthusiasm for this at even very senior levels now, much higher than, you know, the last time I was out there, which was, you know, probably over a year ago. Yeah, that's great. The open source piece is a sneaky visibility factor for you. Yeah. One more, if I could sneak it in with Steffan. We mentioned the Q1 beat was maybe a little tighter than what we've seen, but the full-year raise was very substantial. I think two times the size of the upside we saw in Q1. Anything you'd call attention to from either a Q1 seasonal perspective or just something in the RPO, or what drives the full-year confidence in the raise there? A lot of it comes from the strength of the business that we already have booked, and we look at the current RPO and total RPO. The signals there point to again, increasing revenue for cloud sequentially throughout the year. The number of new logos we've brought into the business is also a factor. Then, a real bright spot for the company is our NRR metrics, which have consistently been improving. We gave additional disclosure this quarter around our cloud NRR being above 150% and our hybrid NRR being the highest. If you think about where the puck is going, most companies are going with cloud, and a lot of our existing companies that were CP, Confluent Platform customers only, they're just getting started on their journey with Confluent Cloud. That hybrid cohort and the cloud cohort should be contributing a lot more to the business going forward. We feel good about the setup for, you know, for the year. Cool. Thanks very much. Thanks, Michael. We'll take our next question from Phil Winslow of Credit Suisse, followed by Piper Sandler. Phil? Thanks for taking my question. I just want to focus on the large deal metrics, Jay. That's one of the things that jumped out at me in the quarter here. I think you added more than twice as many million-dollar accounts this quarter as you did in Q1 last year. Just wondering if you can dig into what you're hearing from these customers, what's driving this expansion? I mean, is it sort of the scale use cases call it vertically, or are you seeing just sort of broader adoption, just horizontally, more and more multiple use cases? That central nervous system example. Steffan, just to build on the last question in terms of, obviously, you highlighted strong CRPO again this quarter, particularly the unbilled backlog component of that, and your comment about sequentially improving cloud. Wonder if you could just remind us, sort of in the commit and consume model of Confluent Cloud, how we should think about converting sort of those bookings into revenue, and how that kind of relates back to your guidance that you gave for improving sequentially over the course of the year. Two questions there. Thanks. Yeah. Yeah, I'll address the first one. I mean, you know, the $1 million-plus customers, we do use that as kind of a loose proxy for people who are kind of going big in this kind of central nervous system mode and really thinking about this across the business. Of course, you could probably get to that spend with one very large use case, but it's much more common that it has broad adoption in those organizations. Yeah, it's a strong signal. Quarter- to- quarter, that will vary a bit, right? Just because it's a smaller number, so there's a little more variance. Yeah, it's absolutely strong. I, you know, I spent a week, you know, over the last week meeting with senior folks in Europe, and I would say probably the biggest change from my point of view, probably two things that I observed. You know, one, much broader senior awareness of what's happening with data in motion. You know, I feel like this is a problem that people have had. It's like a pain people have had where they've been reaching for solutions, and now there's kind of something there, and there's enough adoption that they're willing to bet on it big, and I think that helps us significantly. I think that's coupled with a much more concerted push to the cloud. You know, that may be more Europe- specific, where I think it was happening maybe a little bit more seriously in the U.S., already. You know, certainly among you know, senior leaders I met with there, you know, the seriousness with which they were, you know, pushing that transition, I thought that was different as well. I think both of those are tailwinds for us. On the cloud piece, there are multiple elements to your question, Phil, so let me cover off I think the main ones. First, when we look into the cloud rhythms of the business, we have very impressive cloud revenue growth, but more importantly, new cloud ACV was greater than 50% again for this quarter. That's coming off of what was a strong Q4 as well. We have really good underlying momentum there. Mechanically, when you think about the transition from cloud CRPO to revenue, there are multiple factors at play. One of the biggest factors is, in a consumption-based business, you know, our revenue is still, you know, modest, but growing, you know, incredibly fast. The timing of software projects throughout the year will have a more pronounced impact on sequential growth at this stage of our business. What we've talked about in the past is it takes a few quarters for customers to reach their committed consumption curve. Operationally, we're focused very much on decreasing the time to consumption for our customers. We do that at each stage of the customer journey that we've outlined. Because we've been doing a lot of those things operationally, and you combine that with the high NRR for cloud that we have, and the new customer count for cloud that we have, we can see this progression in our model that shows cloud revenue increasing sequentially quarter- over- quarter for the, you know, for the remainder of the year. That’s the dynamic at play. We think cloud is going through this real robust growth in our business. It’s accounted for a large proportion of our bookings. We just landed, you know, an apex customer in the digital native segment. We feel like we're just getting started on cloud. All right. Thanks, guys. Keep up the good work. Thanks. Thanks, Phil. Our next question comes from Rob Owens of Piper Sandler, followed by Bank of America. Rob? Hi guys. Thanks for taking my question. Wonder if you could touch a little bit more on the international versus domestic growth and the success you saw within this quarter, both from a go-to-market standpoint, how you're building, and also are you seeing any difference in terms of big deals versus kind of those transactional velocity deals as we look internationally versus domestically? Thanks. Yeah, you know, international has continued to outpace U.S. growth. Kind of in just observing and talking with customers, I would say, they're probably a little bit behind the curve on overall cloud adoption and a little bit behind on streaming, but catching up quickly. I think that, you know, in all regions, I've met with customers who are kind of going big at very senior levels. I think that's an exciting thing to say. You know, for us, the regional differences at this layer of the stack are not huge, right? There's many other products that have much bigger differences. By and large, I feel like this type of technology is kind of very international, but it is true that I would say the European companies were a little slower on cloud, so just the total dollar spend in cloud is not at the same level yet as the counterparts, you know, similar size company in the same industry would be at in the U.S. Maybe a little bit, maybe a year behind the curve, you know, across the board on the world of streaming. By and large, doing exactly the same projects in exactly the same way. You know, we see that as a super healthy thing, as that business is, you know, kind of catching up to what we see in the U.S. You may have bits to add to that, Steffan, but those are my observations. All right, thank you. All right, we'll take our next question from Brad Sills of Bank of America. Brad? Hey. Thank you. This is Adam Bergere for Brad Sills. Just as you guys, you know, see deals get bigger, can you just remind us, you know, how we should be thinking about seasonality again? For example, like should we expect to see a more pronounced, you know, Q4 this year than, you know, maybe last year or the year prior? Yeah. Do you wanna speak to that, Steffan? Sure. When we think about seasonality, it's really from a booking's basis. Primarily, that's where we start. Given the way that business is done in the enterprise, typically near the end of the calendar year, larger deals have more momentum, and we ink deals in Q4. It's also aligned with sales compensation plans. We're looking at doing very substantial deals throughout the whole year, but it just so happens that the way the enterprise business works, it tends to be Q4 that tends to be higher from a booking standpoint. When you look at revenue, there's gonna be a little bit of variability in our revenue on a quarter-to-quarter basis in terms of growth rates, and that's because we have a hybrid revenue model. Part of our model has a Confluent Platform, which you have part of it up front, and then the balance recognized ratably. We are seeing an increased impact, positive impact around our consumption business. We talked about that there are some either seasonality or cyclicality type of dynamics to a consumption-based model. You know, you could have Q4 to Q1 having less sequential consumption growth, but that's normal. There are lots of different companies that are out there with a hybrid revenue model that sees that on a consumption basis. We're talking about this in the context of us continuing to raise numbers. We're leaning into a $50 billion-plus market. We're also leaning into improved visibility around how we're managing growth and profitability. I devoted a little bit of my comments to this in this script, but we remain committed to delivering high revenue growth and operating margin improvement in FY 2022. We plan on accelerating that in FY 2023 from an operating margin standpoint, and we plan on exiting Q4 2024 with positive non-GAAP operating margin, and we're doing this all in a high growth format. The last point I just wanna make on that is if you think about the importance of managing growth and profitability and the timing of when we are achieving positive non-GAAP operating margin, we're a 2014 vintage company, and we've laid out basically like a 10-year dynamic from 2014 to Q4 2024 of when we achieve non-GAAP operating margin, you know, positivity. We feel good about that. A lot of our peer group companies in the next gen tech stack are call it 10 to 15 or 16 years post-inception. We're kind of right, you know, in line with our peer group on that front. Awesome. Thank you. Just as a quick follow-up, maybe for Jay. You know, you guys called out, you know, very strong, you know, Confluent Cloud performance, you know, with the mix, the new customers are mostly cloud. Can you just talk about, you know, within the existing Confluent Platform customers, you know, are these customers kind of migrating faster than you expected or, you know, running a hybrid, you know, deployment faster than expected? Thanks. Yeah. You know, we're seeing a ton of growth in this hybrid cohort that has both Confluent Platform and Cloud. You know, the two misconceptions that come up around our Confluent Platform business, you know, first of all, it's not really exactly a transition where people are swapping out an instance of Confluent Platform and swapping in Confluent Cloud. This tends to be about the environment you're running in. If you have applications on-premises, you're using Confluent Platform. If you have applications in the public cloud, you're probably using Confluent Cloud. You can link those together so that data flows between. I talked about some of those use cases earlier. You know, as more use cases are being built in the public cloud, we see faster growth there. That doesn't necessarily mean that the, you know, the growth of Confluent Platform goes to zero. That has actually continued to grow throughout this. That use case of bridging across is actually a really important and strategic use case for customers. We see it as a really strong asset. This is different from most technologies in that it's about the flow of data, so we have to be in all the environments that our customers are in, that everywhere value proposition. We are seeing a ton of growth in that segment. As Steffan alluded to, we see the highest NRR in that hybrid segment, you know, even higher than the cloud-only NRR. Great. Thanks Adam and Brad. Our last question comes from Eric Heath at KeyBanc. Great. Thanks for fitting me in here. Jay, just curious to hear more color on that eight-figure expansion deal. I mean, just number one, was that a cloud customer win? Number two, is that what was the sales playbook there, and were any channel partners involved in that? It is a cloud deal for us. You know, we feel like the only product that makes sense for tech customers is cloud. They're virtually all in the cloud. Yeah, it is. You know, we're just starting to enter these very large open- source Kafka users in tech. You know, certainly New Relic is in the kinda, call it, top 25 biggest Kafka users. I don't have an exact ranking, but something like that. You know, we think that's a really important group of customers to go after. It pushes us to, you know, really be high performance, high reliability, cost-effective. Like, all the things have to be right to serve these most aggressive customers who have the most internal capabilities as well, and get them to make the change. If you do, you know, there's a big prize there, right? 'Cause there's massive teams built around this. You know, some of these organizations have, you know, a team of 50 people that's running the Kafka infrastructure, and it's, you know, attached to every application they run. Yeah. I think we're just getting started in tech, even though we have some great large customers there. You know, we intend to expand that set of large tech customers. We've been doing well for some time now in the kind of commercial-sized tech customers that, you know, lower revenue, smaller tech companies. I think that's a great area for us, and I do think it's part of an overall mindset shift in these companies to try and get out of the, you know, self-managed in-house infrastructure business and kind of put the engineers on something that matters. If I could just sneak one more in. Yeah. Just on the competitive landscape with the public cloud vendors, you announced some deeper partnerships there. I'm just curious if it's turning more from competition to maybe cooperation on that front. Yeah. Well, you know, it's a little bit of both. The cooperation comes from the fact that we actually play a really important role, I think, in the cloud and the kind of getting data into it, actually the flow of data into their systems. You know, much more than other infrastructure areas, I think we're viewed as a really positive and strategic element of their ecosystem, and that's hence all the cooperation with BigQuery and Google and the other data warehouse technologies and in the other clouds. You know, 'cause we're a big deliverer of data to these services through our connectors. That part's incredibly cooperative. They obviously have different products we compete with. You know, some of those products you would never hear about. Some do come up, but we feel that we compete very well. You know, we have an excellent win rate when they come up, and I think that comes out of the depth in this space. You know, I don't think in any of the cloud vendors, you know, real depth around streaming with a kind of integrated strategy around all the parts has really emerged, and that's put us in a unique position in going after this. Coupled with our ability to actually span the different environments, which is something they kind of can't really do or can't really do well. Great. Thanks, Jay. Great. Thanks, Eric. This concludes today's earnings call. Thank you all for joining us. Take care.