Prospectus Summary
PROSPECTUS SUMMARY This summary highlights selected information contained elsewhere in this prospectus. This summary does not contain all of the information you should consider before investing in our Class A common stock. You should read this entire prospectus carefully, including the sections titled “Risk Factors,” “Special Note Regarding Forward-Looking Statements,” and “Management’s Discussion and Analysis of Financial Condition and Results of Operations,” and our consolidated financial statements and the related notes included elsewhere in this prospectus, before making an investment decision. Unless the context otherwise requires, all references in this prospectus to “we,” “us,” “our,” “our company,” and “Confluent” refer to Confluent, Inc. and its consolidated subsidiaries. Unless otherwise indicated, references to our “common stock” include our Class A common stock and Class B common stock.CONFLUENT, INC.
Confluent is on a mission to set data in motion. We have pioneered a new category of data infrastructure designed to connect all the applications, systems, and data layers of a company around a real-time central nervous system. This new data infrastructure software has emerged as one of the most strategic parts of the next-generation technology stack, and using this stack to harness data in motion is critical to the success of modern companies as they strive to compete and win in the digital-first world.
Our way of life has shifted to a digital-first paradigm, and the digital realm has become the new competitive battlefield in the global economy. In order to compete and win in today’s world, organizations must continually innovate on software systems that are increasingly critical to how they do business.
Being digital-first is not just a matter of adding an application or automating an existing process. It is an end-to-end reimagining of business. It means creating rich, digital front-end customer experiences as a primary way of interacting with customers. It means transitioning to real-time, software-driven back-end operations as a business. In retail, this is the difference between accurate inventory tracking across multiple channels to ensure a consumer can have an up-to-date snapshot of what is actually in-store, versus leaving a consumer disappointed on arrival when the product that they thought was available is out-of-stock. In manufacturing, this is the difference between harnessing a real-time flow of data from Internet of Things, or IoT, sensors to deliver predictive maintenance and reduce downtime, versus episodic, manual inspections of equipment.
This is a matter of life or death for companies. Tech disruptors are delivering rich, digital customer experiences and setting the standard for customer expectations. Businesses in every industry are in full mobilization to rebuild their businesses around the new experiences made possible with software and data. Organizations that get it right can experience stronger growth and improved customer loyalty and gain significant competitive advantage. Conversely, organizations that fail to deliver a real-time customer experience that is intuitive, informed, and reliable can expect frustration, dissatisfaction, and churn.
These innovations in front-end customer experiences and back-end business operations reflect a larger technology trend—the fundamental shift in the role of software in the modern organization. Today, software is no longer simply used as a set of applications to increase employee productivity (such as email and expense reporting). Instead, software is directly orchestrating customer experiences and operations that run the business. It is not just that companies are using more software—in a very real sense, they are actually becoming software.
Several waves of technology innovation have driven this changing role of software. Cloud has re-imagined infrastructure as code, making it easier than ever for developers to build applications. Mobile has extended enormous amounts of computing power to fit in the palms of our hands, making usage of technology ubiquitous in our lives. Meanwhile, machine learning is extending the scope and role of software to new domains and processes.
However, in order to complete this transition, another fundamental wave is required. The operation of the business needs to happen in real-time and cut across infrastructure silos. Organizations can no longer have disconnected applications around the edges of their business with piles of data stored and siloed in separate databases. These sources of data need to integrate in real-time in order to be relevant, and applications need to be able to react continuously to everything happening in the business as it occurs. To accomplish this, businesses need data infrastructure that provides connectivity across the entire organization with real-time flow and processing of data, and the ability to build applications that react and respond to that data flow. As companies increasingly become software, they need a central nervous system that connects all of their disparate software systems, unifying their business and enabling them to react intelligently in real-time.
Because of this, we believe that it is no longer enough for an organization to innovate based on the current paradigm of capturing data, storing it, and then querying or analyzing it. Organizations need a strategy, and a foundational data platform, to operate their business in real-time based on data as it is being generated in the moment. This idea of “data in motion” is at least as critical to the operations of a company as “data at rest,” and we believe the new generation of winning organizations will be defined by their ability to take action on it.
Traditional database technologies were not designed for data in motion, but architected for stored data at rest. Despite significant developments in the scalability and speed of analysis in both traditional and more modern databases (such as NoSQL, time-series, and graph databases), they remain limited to data-at-rest use cases and cannot harness data in motion. The leading open source offering for data in motion, Apache Kafka, was originally created by our founders at LinkedIn in 2011 and brought to the mainstream a new paradigm of data processing. However, this was only the beginning. Confluent was founded to create a product that could make data in motion the central nervous system of every company in the world.
Confluent is pioneering this fundamentally new category. Our offering is designed to act as the nexus of real-time data, from every source, allowing it to stream across the organization and enabling applications to harness it to power real-time customer experiences and data-driven business operations. Our offering can be deployed either as a fully-managed, cloud-native SaaS offering available on all major cloud providers or an enterprise-ready, self-managed software offering. Our cloud-native offering works across multi-cloud and hybrid infrastructures, delivering massive scalability, elasticity, security, and global interconnectedness, enabling agile development.
Our open source roots are a key driver of our go-to-market success. Apache Kafka has become the industry standard for data in motion. It is one of the most successful open source projects, estimated to have been used by over 70% of the Fortune 500. Modern applications are expected to integrate with Apache Kafka, and the technical skill set for Kafka has become a critical requirement in the industry. Confluent’s products provide the capabilities of Apache Kafka but do so on a platform built for the cloud, complemented by connectivity to the larger enterprise, and with the ability to process and govern at scale. The developer community understands the benefits of a complete platform for data in motion. Consequently, software developers within our prospective customers’ engineering or IT departments are often very familiar with our underlying technology and value proposition and evangelize on our behalf.
Confluent has built an operationalized customer journey focused on data in motion that ties together product features, go-to-market efforts, and customer success capabilities, and helps take customers from their initial experiments with the technology to organization-wide adoption as one of their most critical data platforms. This starts by landing use cases in a high volume, low-friction manner while projects are still being conceived and the architecture of the solution is being designed. Awareness and use of our offering begin even before our sales efforts, given the widespread adoption of Apache Kafka by developers and the self-service adoption made possible with our cloud product and community downloads. Our enterprise sales force takes these initial engagements and helps users progress to production use cases and paying customers either on a pay-as-you-go model or with a committed contract. Once customers see the benefits of our product for their initial use cases, they often expand into other use cases and lines of business, divisions, and geographies. Our deep technical expertise, coupled with our product capabilities and laser focus on customer outcomes, enable us to form strategic partnerships with our customers on this journey. This expansion is helped by a natural network effect in which the value of our platform to a customer increases as more use cases are adopted, more applications and systems are connected, and more data is added. Over time, by enabling data in motion across the organization, Confluent can become the central nervous system for their entire organization, allowing data to be captured and processed as it is generated in real-time across hundreds of teams, systems, and applications throughout the company. This expansion effect is reflected by our dollar-based net retention rate as of December 31, 2019 and 2020 and March 31, 2021 of 134%, 125%, and 117%, respectively.
Our business has experienced rapid growth around the world. As of March 31, 2021, we had 561 customers with $100,000 or greater in annual recurring revenue, or ARR, across a wide range of industries, compared to 374 such customers as of March 31, 2020, representing year-over-year growth of 50%. As of March 31, 2021, we had 60 customers with $1.0 million or greater in ARR, compared to 33 such customers as of March 31, 2020, representing year-over-year growth of 82%. Our revenue was $149.8 million and $236.6 million in 2019 and 2020, respectively, representing year-over-year growth of 58%, and $50.9 million and $77.0 million for the three months ended March 31, 2020 and 2021, respectively, representing year-over-year growth of 51%. Our Confluent Cloud revenue was $14.4 million and $31.4 million in 2019 and 2020, respectively, representing year-over-year growth of 117%, and $6.2 million and $13.9 million for the three months ended March 31, 2020 and 2021, respectively, representing year-over-year growth of 124%. Our Confluent Platform revenue was $115.8 million and $177.2 million in 2019 and 2020, respectively, representing year-over-year growth of 53%, and $37.7 million and $54.1 million for the three months ended March 31, 2020 and 2021, respectively, representing year-over-year growth of 43%. In 2020 and the three months ended March 31, 2021, revenue from outside the United States accounted for 34% and 36% of our total revenue, respectively. In 2019 and 2020, we incurred operating losses of $98.1 million and $233.2 million, respectively, and our net loss was $95.0 million and $229.8 million, respectively. For the three months ended March 31, 2020 and 2021, we incurred operating losses of $33.4 million and $45.1 million, respectively, and our net loss was $33.6 million and $44.5 million, respectively. As of December 31, 2020 and March 31, 2021, we had an accumulated deficit of $406.1 million and $450.6 million, respectively.
Industry Background
Important industry and technology trends are fueling the rise of data in motion and are important tailwinds for our business, including:
Traditional Data Infrastructure Is Not Designed for Data in Motion
The traditional approach to how applications are built and deployed has been to pair applications with a database which stores data that is then retrieved by the applications periodically.
This database-centric approach is common in data warehouses and relational and NoSQL databases. Databases grew out of a heritage of data storage. They manage a repository of stored data and allow an application to access that point-in-time dataset on demand through querying. They are, in short, a platform designed for managing data at rest. Although there are countless flavors of databases, and they comprise a category worth over $94 billion in annual spend, all databases are rooted in the paradigm of data at rest and share the resulting limitations.
Databases remain an important category but are no longer sufficient as the central data platform in a company.
The systems that carry out the operation of the business and deliver customer experiences must be integrated and real-time. They must cut across infrastructure silos and continually react, respond, and adapt to an ever-evolving business as events unfold. To accomplish this, data infrastructure must support continuous flows of data from across the organization and enable the building of applications that react, process, and respond to that flow of data in real-time. In other words, to address these challenges, companies need a data platform built for data in motion.
This is a fundamental paradigm shift and cuts to the heart of how we think about data and architect applications. Data in motion is not just a missing feature in databases, it is a bottoms-up rethinking of the computer science underlying data systems.
At their core, databases are designed to bring queries to stored data at rest. The challenge with this construct is that when asked to handle data in motion, the whole paradigm crumbles. Traditional databases allow processing only at a point in time and compute an answer which is immediately out of date as the business continues to evolve around it. Building systems using only the infrastructure for data at rest means businesses must build separate point-to-point connections for every system that needs to be connected, resulting in an overwhelming proliferation of point-to-point connections. This results in an “n-squared” problem as every new system needs to separately connect to every existing system and forces companies to resort to periodic data dumps and “batch” processing. Databases are simply too slow to serve the real-time nature of modern customer experiences and operational needs.
Data in motion flips this design 180 degrees. Rather than bringing queries to data at rest, our platform is architected to stream data in motion through the query. This continuous stream makes the data always available and is what fundamentally enables companies to tap into flows of data being generated anywhere in the company and continually process it.
Our Solution
Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion for developers and enterprises alike. In order for enterprises to deliver rich customer experiences, it is critical for all of their business functions, departments, teams, applications, and data stores to have complete connectivity, be thoroughly integrated, and be able to analyze data as it is generated. Confluent is designed to be this intelligent connective tissue by having real-time data from multiple sources constantly streamed across an enterprise for real-time analysis.
Our offering enables organizations to deploy production-ready applications that run across cloud infrastructures and data centers, and scales elastically, with enhanced features for security and compliance. Our platform provides the capabilities to fill the structural, operational, and engineering gap that is required for businesses to fully realize the power of data in motion. We enable software developers to easily build their initial applications to harness data in motion, and enable large, complex enterprises to make data in motion core to everything they do. As organizations mature in their adoption cycle, we enable them to build more and more applications that take advantage of data in motion. The results have a dual effect: businesses continuously improve their ability to provide better customer experiences and concurrently drive data-driven business operations. We believe that, over time, Confluent can become the central nervous system for modern digital enterprises, providing ubiquitous real-time connectivity and powering real-time applications across the enterprise.
Confluent’s solution can be deployed either as a fully-managed cloud-native SaaS offering available on-demand, Confluent Cloud, or an enterprise-ready, self-managed software offering, Confluent Platform.
A high-performance, low-latency infrastructure for harnessing data in motion requires operating wherever a customer’s applications and systems reside. Customers with applications in a particular cloud would use Confluent Cloud in that cloud provider and region. Customers with applications on premises, or on a private cloud, would use Confluent Platform in that data center. Customers with both on premises and cloud, or even multiple clouds, need Confluent in each of these environments. Together, these solutions can act as one unified fabric for data streams that connect all of these customer environments.
Our solution has three differentiated elements:
Confluent Is Becoming the Central Nervous System of Organizations
As Confluent grows within an organization, the network effects we generate create even more value to the organization as a whole. By fundamentally re-architecting how data flows, we are able to replace complexity with simplicity, delays with real-time, and disparate data with a unified view across the modern enterprise software stack.
Most organizations start off with a complex mess of point-to-point connections between their applications, databases, and data warehouses. This is unavoidable when data is primarily at rest, held in storage across the organization, and requiring these connections to be built. Adopting a new technology to connect this mess would be prohibitively slow if there were not an underlying force driving this change. Fortunately, our platform has a unique network effect that helps speed its adoption. The first application that utilizes our platform generally does so for the capabilities in harnessing data in motion. In doing so, it brings into the platform the data streams needed for its usage. However, although these data streams are brought for one application, they are usable by all future applications and bring value to the entire ecosystem. As a result, future applications can connect to the platform to access these data streams, bringing with them their own data streams. As a result, there is a clear virtuous cycle: applications bring data streams, which in turn attract more applications.
As customers expand with our foundational platform, we set more and more data in motion across the organization and replace the various point-to-point connections with our complete platform. This means data can intelligently be made available in real-time to more and more of the organization as applications connect to a single platform. We are able to hold a highly strategic position to create greater value to existing applications and databases as data in motion across the entire organization begins to flow, be directed, and be processed through Confluent. We believe that this eventually leads to Confluent becoming the central nervous system of an organization, allowing data to be captured and processed as it is generated around the whole organization, enabling organizations to react intelligently in real-time.
Key Benefits to Our Customers
Our platform delivers the following key business benefits to our customers:
Competitive Strengths
Our competitive strengths include the following:
data in motion but can become the central nervous system for their entire organization, allowing data to be captured and processed by the whole organization as it is generated in real-time.
Our approach to supporting this end-to-end customer journey is a significant competitive moat for us. Legacy technology vendors cannot easily rebuild their go-to-market to support high volume, low-friction open source and SaaS lands. Startup companies cannot muster the full spectrum of go-to-market tactics and resources needed to support this journey or the heavy investment in customer success required to take customers to scale. Even though large cloud providers have broad go-to-market capabilities, these capabilities are generally focused on the broader transition to the cloud addressing hundreds of products and services. We believe they take a broad but shallow approach that is not built to focus and support the specifics of the data-in-motion customer adoption journey and cannot easily be repurposed without a larger remaking of their go-to-market strategy.
Our Market Opportunity
Confluent is well-positioned to succeed in the large and growing market for data infrastructure. We have intentionally built our technology to support both cloud and on-premises environments because enterprises today are in different stages of their journey to the cloud. This strategy has positioned us to be able to serve every type of company, in every industry, and in every geography.
Today, we believe our product roadmap targets each of the following four core Gartner-defined market segments: Application Infrastructure & Middleware, Database Management Systems, Data Integration Tools and Data Quality Tools,2 and Analytics and Business Intelligence.3 According to Gartner’s 2021 estimates, the aggregate of these four markets represents a total market size of approximately $149 billion.
We estimate that we serve approximately $50 billion of this total market today, broken down as approximately $31 billion in Application Infrastructure & Middleware (excluding Full Life Cycle API Management, BPM Suites, TPM, RPA, and DXPs), $7 billion in Database Management Systems (excluding Prerelational-era DBMS), $7 billion in Analytics and Business Intelligence (excluding Traditional BI Platforms), and $4 billion in Data Integration Tools and Data Quality Tools (excluding other Data Integration Software). Based upon the above Gartner data and Gartner’s estimates for 2024 total market size in these four segments, we have estimated that our total market opportunity will increase to $91 billion in these four market segments by 2024, representing a 22% compounded annual growth rate.4
Our Growth Strategy
We are pursuing our substantial market opportunity with growth strategies that include: