Confluent : Q1'24 Earnings Report

Disclaimer

This report contains forward-looking statements including, among other things, statements regarding (i) our financial outlook, including expected total revenue, subscription revenue, Confluent Cloud revenue, non-GAAP operating margin, free cash flow margin, non-GAAP net income per share, revenue mix, Confluent Cloud growth, operating margins and margin improvements, targeted or anticipated gross and operating margin levels, earnings per share levels and improvements, improvements in unit economics and in-product optimizations of Confluent Cloud, continued business momentum, and expected revenue growth rate and efficient growth, (ii) our market and category leadership position, (iii) our expected investments in research and development and go-to-market functions and anticipated effectiveness and timing of product innovation, features and functionalities, (iv) our ability to drive efficient growth and rate and pace of investments, including expected capital allocation, (v) our expectations and trends relating to growth of our DSP products and Confluent Cloud, including following our planned reorientation of our go-to-market strategy and model around customer consumption, (vi) rates of Confluent Cloud consumption and demand for and retention of data streaming platforms like Confluent in the face of budget scrutiny, (vii) continued

high interest rates and macroeconomic uncertainty as well as our expectations regarding the effects of macroeconomic pressure on our go-to-market motion, durability of our offering with customers, customer use case expansion and overall consumption levels of Confluent Cloud, as well as potential benefits to our business and growth following any improvements to the macroeconomic environment, (viii) our pricing, our win rate and deal cycles and customer behaviors, such as budget scrutiny and preferences for consumption rather than large upfront commitments, (ix) customer growth, retention and engagement, (x) ability for Confluent Cloud to provide cost savings for users and customers, including lower total cost of ownership, and drive greater monetization of the open source Kafka user base as a result, (xi) increased adoption of our offering and fully managed solutions for data streaming in general, including from customers building generative AI applications, (xii) dependence of businesses on data in motion, (xiii) growth in and growth rate of revenue, customers, dollar-based net retention rate, and gross retention rate, (xiv) our ability to increase engagement of customers for Confluent and expand customer cohorts,

1. our market opportunity, (xvi) our ability to successfully reorient our go-to-market strategy and model around customer consumption as well as the timing, anticipated benefits, and overall effectiveness of such transition for our business, future durable and efficient growth, and ability to capture our market opportunity, (xvii) our go-to-market strategy, (xviii) our product differentiation and market acceptance of our products, including over open source alternatives, (xix) our strategy and expected results and market acceptance for our Flink offering and our DSP products, (xx) our expectations for market acceptance, direction and growth of stream processing, its potential to accelerate adoption of our platform and growth of our business, and our ability and positioning to capture this market, (xxi) our expectations of meeting near-term and mid-term financial targets, (xxii) our expectations regarding the generative AI landscape and our offering, including expectations of customers and partners using our offering for generative AI use cases, (xxiii) our expectations of relevance of certain
   key financial and operating metrics, and (xxiv) our overall future prospects. The words "believe," "may," "will," "estimate," "continue," "anticipate," "intend," "expect," "seek," "plan," "project," "target," "looking ahead," "look to," "move into," and similar expressions are intended to identify forward-looking statements. Forward-looking statements represent our current beliefs, estimates and assumptions only as of the date of this report and information contained in this report should not be relied upon as representing our estimates as of any subsequent date. These forward-looking statements are subject to risks, uncertainties, and assumptions. If the risks materialize or assumptions prove incorrect, actual results could differ materially from the results implied by these forward-looking statements. Further information on the risks that could affect Confluent's results is included in our filings with the Securities and Exchange Commission ("SEC"), including our Annual Report on Form 10-K for the year ended December 31, 2023, and our future reports that we may file from time to time with the SEC. Additional information will be made available in our Quarterly Report on Form 10-Q for the quarter ended March 31, 2024 that will be filed with the SEC, which should be read in conjunction with this report and the financial results included herein. Confluent assumes no obligation to, and does not currently intend to, update or revise any forward-looking statements, whether as a result of new information, future events or otherwise, except as required by law.

This report also contains statistical data, estimates and forecasts made by independent parties and by us relating to market size and growth, as well as other data about our industry and business. These data involve a number of assumptions and limitations, and we have not independently verified the accuracy or completeness of these data. Neither we nor any other person makes any representation as to the accuracy or completeness of such data or undertakes any obligation to update such data after the date of this report. In addition, projections, assumptions and estimates of our future performance and the future performance of the markets in which we operate are necessarily subject to a high degree of uncertainty and risk. The Gartner content described herein (the "Gartner Content") represents research opinions or viewpoints published, as part of a syndicated subscription service, by Gartner, Inc. ("Gartner"), and are not representations of fact. The Gartner Content speaks as of its original publication date (and not as of the date of this report), and the opinions expressed in the Gartner Content are subject to change without notice.

This report includes certain non-GAAP financial measures as defined by SEC rules. Because not all companies calculate non-GAAP financial information identically (or at all), the presentations herein may not be comparable to other similarly titled measures used by other companies. Further, such non-GAAP financial information of Confluent should be considered in addition to, and not as superior to or as a substitute for, the historical consolidated financial statements of Confluent prepared in accordance with GAAP. Refer to the slides in the section titled "GAAP to Non-GAAP Reconciliations" at the end of this report for a reconciliation of our non-GAAP financial metrics to the most directly comparable GAAP financial metrics.

1. reconciliation of forward-lookingnon-GAAP operating margin, free cash flow margin and non-GAAP net income per share to the most directly comparable GAAP measures is not available without unreasonable effort, as certain items cannot be reasonably predicted because of their high variability, complexity and low visibility. In particular, the measures and effects of our stock-basedcompensation-related charges, which include stock-based compensation expenses, employer payroll taxes on employee stock transactions, and amortization of stock-based compensation capitalized in internal-use software, are directly impacted by the timing of employee stock transactions and unpredictable fluctuations in our stock price, which we expect to have a significant impact on our future GAAP financial results.

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Confluent's Role in Making GenAI a Reality

GenAI continues to be top of mind for many companies. But most are coming to realize that LLMs don't stand alone. RAG or retrieval augmented generation has emerged as the common pattern for GenAI to extend the powerful LLM models to domain-specific data sets in a way that avoids hallucination and allows granular access controls. Data streaming platforms play a pivotal role in enriching RAG-enabled workloads with contextual and trustworthy data. It enables companies to tap into a continuous stream of real-time data from the systems that power the business, and transform it into the right format to be used by vector databases for AI applications.

Data Sources

Vector Store

AI App

and more...

What Confluent Delivers

With a complete data streaming platform (DSP) from Confluent, organizations can build a shared source of real- time truth for sophisticated model building and fine-tuning, bring real-time context at query time, reduce innovation friction as new AI apps and models become available with reusable and universal data products, and build governed, secured, and trusted AI to minimize hallucinations with trustworthy, real-time data streams.

Use Cases and Customer Adoption

Some of the popular GenAI use cases we have seen in our customer base include:

In the digital native segment, we're seeing great traction. Many GenAI companies such as OpenAI, Notion and Motive are using data streaming to build connected customer experiences and streamline their business operations to be more real-time. We're also seeing incredible traction with the GenAI incubators to build GenAI applications that are hallucination-free, trustworthy, real-time GenAI use cases.

GenAI Latest Innovation

Flink AI Model Inference allows customers to simplify the development and deployment of AI/ML applications through a unified platform for both data processing and AI/ML tasks. With AI Model Inference, companies can reduce operational complexity by enabling seamless coordination between data processing and AI workflows, and enable accurate, real-timeAI-drivendecision-making.

GenAI Partnership

To help customers unlock the full potential of AI with the freshest contextual data from across their business, Confluent continues to expand its partnership with leading companies in the AI and vector database space.

Learn More

Learn more about how Confluent helps customers to quickly scale and build real time AI applications with continuously enriched and trustworthy data streams at our AI webpage.

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The Confluent Data Streaming Platform

Confluent pioneered the Data Streaming Platform (DSP) category to turn the spaghetti mess of complex and rigid point-to-point connections created by traditional data management tools into a virtuous cycle of data in motion. This allows retailers to build real-time inventory systems, banks to build real-time fraud detection and manufacturing organizations to collect real-time diagnostics for an assembly line. With Confluent, organizations can set their data in motion to win in the digital first world.

Complete Data Streaming Platform

Key Pillars of a Complete Data Streaming Platform

Our DSP enables a complete set of capabilities for working with data, and captures the virtuous cycle of streaming data:

• It starts with Stream: Batch data is low-quality, which doesn't meet the needs of a real-time world.
• Connect data from everywhere in an organization and seamlessly integrated into a continuously moving whole.
• Process data by itself is useful, but with stream processing, data is infinitely more valuable when combined with other data and enriched with business context.
• Lastly, Govern. Data is worth less if it can't be trusted and secure.

Confluent DSP Innovation

Confluent's leadership of the DSP category has been recognized by leading analyst firms, including Forresterand IDC. We continue to expand our leadership position with a relentless pace of product innovation, including 15 major features and pricing optimizations announced at Kafka Summit Bangalore and Kafka Summit London in 2024, including Tableflow, and the GA of Confluent Cloud and Confluent Platform for Apache Flink.

Confluent DSP Adoption

This value proposition of our multi-product platform is resonating with customers. This adds to our growth vectors and extends our runway to drive durable and efficient growth for a number of reasons:

Connect, Process, Govern: Each DSP product has the potential to become an independent business larger than Kafka

Connect, Process, and Govern grew substantially faster than cloud and accounted for only ~10% of cloud revenue

Multi-product $100K+ customers increased 47% YoY, with a substantially higher NRR due to strong network effects

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Jay Kreps

Co-Founder and CEO

Total revenue grew 25% to $217 million, Confluent Cloud revenue grew 45% to $107 million, which now accounts for the majority of our subscription revenue and remains our fastest growing offering. Non-GAAP operating margin improved 22 percentage points, our 4th consecutive quarter of more than 20 points in improvement. These results reflect our team's strong execution amid a still uncertain but stabilizing macro environment.

Consumption Transformation Update

In Q1 we launched our consumption transformation. We oriented our sales compensation for cloud towards incremental consumption and new logo acquisition; we rolled out new systems, metrics and measures; and made pricing adjustments to reduce friction in landing new customers. It remains early days, but we are encouraged by the strong promising signals of our consumption orientation, particularly around new customer acquisition and stabilization of consumption trends.

With an increased focus on new logo growth, we added 160 customers to our total customer count, our largest sequential growth since Q1 2023. We not only increased the volume of customer additions, but were better able to target high potential customers, increasing the quality of our customer adds as well.

Note: Refer to the section below titled, "Definitions & GAAP to Non-GAAP Reconciliations" for a reconciliation of our non-GAAP financial metrics to the most directly comparable GAAP financial metrics.

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Kafka Summit (Bangalore & London)

We recently hosted Kafka Summit in London and Bangalore. Kafka Summit Bangalore was the first ever Kafka Summit in APAC. These events are a great illustration of the tremendous growth and innovation happening within the data streaming category. Between the two events we had more than 7,000 people joining us in-person or registered virtually, spanning startups, enterprises and everything in between, including organizations like Apple, Bloomberg, CERN, ING, Stripe, Uber, and many others. Our relentless pace of product innovation was on full display, with 15 major customer-facing features and price and performance optimizations announced across both events, including the general availability of Flink and early availability of a powerful new feature we call Tableflow.

Tableflow

Tableflow makes all the data streams that flow through Confluent Cloud available as structured tables in cloud object storage using an open table format called Apache Iceberg. Let me provide a little background on what this means and why it is so powerful.

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Closed Data in the Analytics Estate

Historically data in the analytics and data warehousing world has existed in closed systems that trapped their data inside a walled garden. As the complexity of the analytics world has grown this has led to a mishmash of data warehouses, datalakes, AI products, and reporting systems. This created lots of value for technology vendors, but created yet another data silo for the end user. However over the last five years a trend has emerged of standardizing around open data formats and metadata on top of cloud object storage.

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