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Welcome Shane Xie VP, Investor Relations 1 1
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Safe Harbor This presentation includes express and implied forward-looking statements. All statements contained in this presentation other than statements of historical facts, including expectations of Confluent, Inc. (“we,” “us,” “our,” or “Confluent”) regarding our revenue, revenue mix, revenue growth, expenses and other results of operations; total customers; net dilution; operating margins and margin improvements, targeted or anticipated margin levels; future financial performance, business strategy and plans; potential market and growth opportunities; competitive position; technological or market trends; addressable market opportunity; ability for Confluent Cloud to provide lower total cost of ownership; the timing, anticipated benefits, and overall effectiveness of our transition to a consumption-oriented sales model; consumption growth rate and efficient growth; and our objectives for future operations, are forward-looking statements. 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. We have based these forward-looking statements largely on our current expectations and projections about future events and trends that we believe may affect our financial condition, results of operations, business strategy, short-term and long-term business operations and objectives, and financial needs. 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. Risks include, but are not limited to: (i) our limited operating history, including in uncertain macroeconomic environments, (ii) our ability to sustain and manage our rapid growth, (iii) our ability to increase consumption of our offering, including by existing customers and through the acquisition of new customers, including by addressing customer consumption preferences, successfully adding new features and functionality to our offering, and partnering with our customers to help them realize increased value in Confluent in an efficient and sustainable manner, (iv) our ability to successfully execute our go-to-market strategy and initiatives, including following the reorientation of our go-to-market strategy and model around customer consumption, (v) our ability to attract new customers and successfully ramp their consumption of our offering, as well as retain and sell additional features and services to our existing customers, (vi) shifts in industry trends relating to data storage and cloud usage, including in AI, (vii) uncertain macroeconomic conditions, including high inflation, high interest rates, bank failures, supply chain challenges, geopolitical events, recessionary risks, and exchange rate fluctuations, which have resulted and may continue to result in reduced consumption of Confluent Cloud, volatility in consumption, including due to customer focus on cloud cost controls and increased efficiency, customer pullback in information technology spending, lengthening of sales cycles, reduced contract sizes, generally increased scrutiny on IT spending from existing and potential customers, or customer preference for open source alternatives, as well as the potential need for cost efficiency measures, (viii) our ability to achieve profitability and improve margins annually, by our expected timelines or at all, (ix) the estimated addressable market opportunity for our offering, including our Flink offering and stream processing, and our ability to capture our share of that market opportunity, (x) our ability to compete effectively in an increasingly competitive market, (xi) our ability to attract, ramp, and retain highly qualified personnel, including following the reorientation of our go-to-market strategy and model around customer consumption, and the impacts of sales personnel attrition and levels of ramped capacity in our sales organization, (xii) breaches in our security measures, intentional or accidental cybersecurity incidents or unauthorized access to our platform, our data, or our customers’ or other users’ personal data, (xiii) our reliance on third-party cloud-based infrastructure to host Confluent Cloud, (xiv) public sector budgetary cycles and funding reductions or delays, (xv) our ability to accurately forecast our future performance, business and growth, and (xvi) general market, political, economic, and business conditions. These risks are not exhaustive. It is not possible for our management to predict all risks, nor can we assess the impact of all factors on our business or the extent to which any factor, or combination of factors, may cause actual results to differ materially from those contained in any forward-looking statements we may make. You should not rely upon the forward-looking statements as predictions of future events. The future events and trends discussed in this presentation may not occur and actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. Although we believe that the expectations reflected in the forward-looking statements are reasonable, we cannot guarantee that future results, levels of activity, performance, achievements or events and circumstances reflected in the forward-looking statements will occur. Except to the extent required by law, we do not undertake to update any of these forward-looking statements after the date of this presentation to conform these statements to actual results or revised expectations. In addition, statements that “we believe” and similar statements reflect our beliefs and opinions on the relevant subject. These statements are based on information available to us as of the date of this presentation. While we believe such information provides a reasonable basis for these statements, such information may be limited or incomplete. Our statements should not be read to indicate that we have conducted an exhaustive inquiry into, or review of, all relevant information. These statements are inherently uncertain, and investors are cautioned not to unduly rely on these statements. This presentation 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 presentation. 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 presentation), and the opinions expressed in the Gartner Content are subject to change without notice. This presentation includes certain non-GAAP financial measures as defined by Securities and Exchange Commission (“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 “Definitions & GAAP to Non-GAAP Reconciliations” at the end of this presentation for a reconciliation of our non-GAAP financial measures to the most directly comparable GAAP financial measures. 2
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Data Streaming in the Age of AI Jay Kreps Co-Founder & CEO 3 3
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Agenda Data Streaming in the Age of AI → A Conversation with Databricks Jay Kreps | Co-Founder & CEO 01 02 Winning with Our Data Streaming Platform Shaun Clowes | Chief Product Officer 03 04 Partner and Customer Discussions → Saudi Cloud Computing Company, Affirm Holdings Stephanie Buscemi | Chief Marketing Officer 05 06 07 08 Management Q&A Driving Durable, Profitable Growth Rohan Sivaram | Chief Financial Officer Our Next-Gen GTM Model Erica Schultz | President of Field Operations Ryan Mac Ban | SVP, Global Head of Sales Event-Driven Multi-Agent Demo Mike Agnich | GM & VP of Product Management The Future of Agentic AI is Event-Driven Andrew Sellers | Head of Technology Strategy 4
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A New Paradigm for Data in Motion: Data Streaming Govern Process Data Systems Custom Apps & Microservices StreamConnect and more… AI/MODELING FRAUD RECOMMENDATIONS PERSONALIZATIONS PAYMENTS ACCOUNTS INVENTORY FROM DATA MESS TO DATA PRODUCTS TO INSTANT VALUE EVERYWHERE 5
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MARKETING FINANCE PRODUCT OPERATIONS SALES OPERATIONAL APPS 6
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OPERATIONAL APPS MARKETING FINANCE PRODUCT OPERATIONS SALES ANALYTIC SYSTEMS 7
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OPERATIONAL APPS MARKETING FINANCE PRODUCT OPERATIONS SALES ANALYTIC SYSTEMS 8
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Software is Now Running Significant Parts of the Business Software used by people Action happens periodically INPUT LOGIC OUTPUT Software used by software Action happens continuously INPUT LOGIC OUTPUT 9
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10 Factories, Inventory, and Supply Chain / IoT Connected Software Enabled Cars Ecommerce 10
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DATA STREAMING PLATFORM 11
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The Rise of AI 12 12
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13 AI BI 13
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DATA WAREHOUSE Reporting & Analysis Batch Processing & ETL Batch view of all enterprise data AI & Mission- critical Apps Stream Processing Real-time view of all enterprise data Use Case / Application Processing Data Feeding data to humans The Platform for BI 14
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DATA WAREHOUSE DATA STREAMING PLATFORM Reporting & Analysis Batch Processing & ETL Batch view of all enterprise data AI & Mission- critical Apps Stream Processing Real-time view of all enterprise data Use Case / Application Processing Data 15
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Fundamental Architectural Shift in AI Training of bespoke models (offline / batch) Inference with general purpose model (online / real time) CLASSIC ML GEN AI Use of Enterprise Data 16
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Artificial Intelligence Patterns of Usage RAG AI AGENTS 17
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Closing the Loop Increases the Speed Decision with People Action happens periodically INPUT LOGIC OUTPUT Decision with AI Action happens continuously INPUT LOGIC OUTPUT 18
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OPERATIONAL ESTATE Apps & Real-Time Events ANALYTICAL ESTATE Data Intelligence & AI 19
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Data Cleansing, Aggregation, Normalization and Governance → ← Generated Insights Flow Back to Applications Tableflow Unites Streams and Tables ANALYTICAL SYSTEMS Data Lake Lake House Data Warehouse ML/AI/ GenAI Models X TABLEFLOW OPERATIONAL SYSTEMS Domain 1 Database GenAI Apps Custom Apps SaaS Apps 20
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Tableflow Unity Catalog OPERATIONAL ESTATE Apps & Real-Time Events ANALYTICAL ESTATE Data Intelligence & AI 22
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Jay Kreps Co-Founder & CEO Ali Ghodsi Co-Founder & CEO A Conversation with Databricks 23
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Data Streaming Platform FROM DATA MESS TO DATA PRODUCTS TO INSTANT VALUE EVERYWHERE Govern Process Data Systems Custom Apps & Microservices StreamConnect and more… AI/MODELING FRAUD RECOMMENDATIONS PERSONALIZATIONS PAYMENTS ACCOUNTS INVENTORY 24
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One Team, One Mission: Set Data in Motion Ryan Mac Ban SVP Global Head of Sales Shaun Clowes Chief Product Officer Colleen McCreary Chief People Officer Stephanie Buscemi Chief Marketing Officer Melanie Vinson Chief Legal Officer Rohan Sivaram Chief Financial Officer Erica Schultz President, Field Operations Jun Rao Co-Founder Jay Kreps Co-Founder & CEO 25
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Congratulations & THANK YOU! 26
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Key Takeaways 01 Data streaming is key to closing the loop with AI. 02 The DSP will be the most strategic data platform in a modern enterprise. 03 Confluent is uniquely positioned to capture this opportunity. 27
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Winning with the Confluent Data Streaming Platform Shaun Clowes Chief Product Officer 28
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OPERATIONAL ESTATE ANALYTICAL ESTATE Traditional ML and AI is Stuck in the Analytical Estate, Far from Real-time ML Modeling / Predictive AI Reports & Dashboards 29
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Data Cleansing, Aggregation, Normalization and Governance → ← Generated Insights Flow Back to Applications Real-time AI Needs Real-time, Always-on Data ANALYTICAL SYSTEMS Data Lake Lake House Data Warehouse ML/AI/ GenAI Models OPERATIONAL SYSTEMS Domain 1 Database GenAI Apps Custom Apps SaaS Apps Unprecedented processing speed Cost-effective at big data scale Real-time context at query time Governed and trustworthy data 30
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Rethinking Data Architecture in the GenAI Era Chatbots Cybersecurity / Observability Smart Analytics AI Customer Service Agent AI Vishing Protection Personalized Recommendations IoT / Edge Analytics ... 31
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STREAM CONNECT PROCESS GOVERN DATA STREAMING PLATFORM 32
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The Exploding Data Landscape: A Market Ripe for Streaming Exponential Data Growth Over the Years Data Volume in Zetabytes Traditional Data Movement Methods Webhooks As data explodes, the need for real-time, scalable data movement has never been greater. Streaming is the unifying solution. 2010 2015 20252020 2 15.5 64 182 GoldenGate Cron Jobs Note: Graph from ‘Amount of data created, consumed, and stored 2010-2023, with forecasts to 2028,’ Source: Statista 33
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The Real-time Rise of Apache Kafka Continuously ingesting and continuously sharing streams of data in real time Real-time Data A Sale A shipment A Trade A Customer Experience Rich Front-End Customer Experiences Real-Time Backend Operations New Products and Services 34
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Cost-efficientResilientElastic Fast 35
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36 Confluent Cloud Warpstream BYOC with managed benefits inside customers’ cloud environment Confluent Platform Enterprise-grade self-managed software offering Enterprise Production-ready, enhanced security w/ private networking Common Use Cases: Microservices, real-time pipelines NEW Freight Low cost for high throughput, relaxed latency workloads Common Use Cases: Logging, monitoring, telemetry NEW NEW Basic Getting started Production ready for most applications Standard Flexible, Cost-Effective Streaming To Meet the Needs of Every Workload and Use Case Dedicated Customizable for any applications Common Use Cases: Ultra security sensitive workloads Common Use Cases: Logging, observability, data lake ingestion, analytics 36
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ENTERPRISE FREIGHT WARPSTREAM CONFLUENT SERVER DEDICATED Form a Streaming Mesh for Seamless Data Mobility 37
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Why Enterprises Choose Confluent Over Point Streaming Tools Deployment simplicity and flexibility High availability, security and reliability Ultra-low latency and high throughput Hybrid and multi-cloud interoperability Reduced total cost of ownership 38
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“WarpStream helps us achieve all of our objectives for cost-effective, multi-AZ deployments and decoupled architecture, making WarpStream an even more attractive alternative to running open source Apache Kafka.” “I was able to make some back-of-the-envelope calculations, to show how much running MSK was costing us a day, and …my management overhead and total cost of ownership was drastically reduced with Confluent.” “To have data streaming implemented at a global scale called for a platform that’s reliable, auto-scales depending on our need… and can cater to our future growth. That’s why we invested in Confluent.” 39
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STREAM CONNECT PROCESS GOVERN DATA STREAMING PLATFORM 40
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ANALYTICAL ESTATE Fragmented connector stack lacking seamless, continuous event-driven data flows across operational and analytical estates OPERATIONAL ESTATE 41
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The Leading Zero-Code Streaming Ecosystem 120+ pre-built connectors for operations and analytics Data Diode 42
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Powering Native Streaming and Integrations Through Ecosystem Partnerships Data Warehouses / Data Lakes Streaming Databases Operational Databases Vector Databases IoT Platforms Stream Processing Data Integration Real-time App Development Compute 43
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Accelerating Innovation with Leading AI Stack Collaborations VECTOR STORES RAG MLOps CLOUD SERVICE PROVIDERS INNOVATIVE AI 44
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STREAM CONNECT PROCESS GOVERN DATA STREAMING PLATFORM 45
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The Need to Bring Real-time and Historical Data Has Never Been Greater What is the status of my flight to New York? It is currently delayed by 2 hours and expected to depart at 5 pm GMT. Is there another flight available to the same city that will depart and arrive sooner? What are the seating options and cost? The next available flight to New York with United departs later but will arrive faster than your current flight. The only available seats in this flight are first class window seats and costs $1,500. You can use your loyalty points to cover half the cost. Past Bookings Meal Preferences Seat Pricing Loyalty Rewards Weather Conditions Passenger Itinerary Seat Preferences New Flight Status Ticket Class Personalized Recommendations Current Flight Status Available Inventory Airline Policies 46
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OPERATIONAL ESTATE ANALYTICAL ESTATE Data Processing Silos Leads to Inefficiencies and Inaccuracies Data Lake Data Warehouse SaaS App Database PROCESSING PROCESSING PROCESSING PROCESSING ML Modeling Analytics Interactions Queries … DatabasesSoftware Applications Static queries ETL/ELT Pipelines Custom logic Static queries 47
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Apache Flink Revolutionized Stream Processing 0 50,000 100,000 150,000 2020 2021 2022 2016 2017 2018 Flink Kafka Monthly Unique Users Flink Fueled Innovation in Leading Enterprises… ● Real-time fraud detection ● Real-time logistics ● Personalized recommendations ● Dynamic pricing ● Ad campaign performance ● Anomaly detection ● Business process monitoring ● ML Pipelines … …Across Real-time and Batch Use Cases 48
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Process your data once, process your data right Unify Real-time and Batch Processing with Confluent Flink GovernJoin Standardize Route FilterEnrich Real-time Apps Cloud-native Apps Cloud Data Systems Events Legacy Data Systems Mainframes SaaS Apps 49
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Enterprise-grade Security Cloud-native Experience Runs Everywhere Built-in Observability A Frictionless On-prem and Cloud Flink Experience For Faster Innovation Eliminated Data Redundancy Enhanced Real-time Insights Improved Personalization Future-proofed Architecture 50
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Expanding Developer Reach Customize logic with User Defined Functions & multiple languages Developer-friendly Flink to Build AI Applications Faster Model Inferencing Made Simple Query popular AI engines directly within Flink Effortless Federated Search Simplify data access in diverse platforms with unified search 51
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Bringing AI Agents and Copilots to Life with Confluent at Airy “With Confluent, we help build AI copilots that allow users to interact with data in natural language, turning Flink jobs into agents that continuously monitor data streams. Flink AI Model Inference simplifies our stack… providing real-time context to generate the most accurate Flink SQL queries.” Copilot User Ask questions in natural language Response in natural language Vector DB (RAG) Generates context on data text_generation embeddings predictions Flink Stream Processing Flink AI Model Inference Produces Reads data Data Lakes Data Sources Reads 52
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STREAM CONNECT PROCESS GOVERN DATA STREAMING PLATFORM 53
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PROVIDES CONSUMES Empowering Data Integrity and Trustworthiness with Data Contracts Data Provider Data Consumer STRUCTURE SEMANTICS POLICIES AGREES TO AGREES TO Data Contract Data 54
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Empowering Data Integrity and Trustworthiness with Data Contracts PROVIDES CONSUMES Data Provider Data Consumer AGREES TO AGREES TO Data Contract Data STRUCTURE SEMANTICS POLICIES EVOLUTION 55
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Removing Data Friction to Unlock Value Stream LineageStream Catalog ` Stream Quality Data producer/owner Ensure data is clean, high-quality, and ready for immediate use Data platform team Create a self-serve platform to scale enterprise data access Data consumer Find, understand, and use trustworthy data for faster use case delivery 56
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DATA STREAMING PLATFORM STREAM CONNECT PROCESS GOVERN 57
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OPERATIONAL ESTATE ANALYTICAL ESTATE Two Distinct Estates, Two Distinct Messes 58
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OPERATIONAL ESTATE Kafka is the Open Standard for the Operational Estate 59
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Process and govern OPERATIONAL ESTATE ANALYTICAL ESTATE Queries Analytics AI / ML …Databases Applications SaaS Apps BUSINESS-READY BUSINESS-READY BUSINESS-READY Data Warehouse / Data Lake CLEANSED CLEANSED CLEANSED RAW RAW RAWELT / ETL rETL Batch Data Pipelines Are a Bottleneck for Efficiency, Cost, Complexity and Innovation “Dirty data” “Clean data” “Joined data” ELT / ETL ELT / ETL 60
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OPERATIONAL ESTATE ANALYTICAL ESTATE The Analytical Systems Data Trap Wasteful duplication, brittle pipelines and endless maintenance 6161
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OPERATIONAL ESTATE ANALYTICAL ESTATE Tableflow From streams to tables within minutes TABLEFLOW 62
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OPERATIONAL ESTATE ANALYTICAL ESTATE From Data Chaos to Universal Data Products with real-time, reliable and reusable data flows across both estates Govern Process Connect Queries Analytics AI / ML … Databases Applications SaaS Apps GenAI Apps Tableflow BUSINESS-READY BUSINESS-READY BUSINESS-READY Universal Data Products Data Warehouse / Data Lake CLEANSED Stream 63
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OPERATIONAL ESTATE ANALYTICAL ESTATE Service Bus GoldenGate Each Estate Has Silos of Tools to Connect, Process, Govern and Move Data Connect Process Govern Move Connect Process Govern Move 64
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We’re Unifying the Data Landscape From the Ground Up Confluent Data Streaming Platform Process Flink on-prem and in Cloud Multi-language Support | AI Model Inference Govern Stream Catalog | Stream Quality Stream Lineage | Data Portal Stream Enterprise | Freight | Warpstream | Confluent Server | Dedicated Connect 120+ Pre-built Connectors Connect with Confluent | Custom Connect Tableflow Delta | Iceberg | Unity Catalog Real-time Inventory Real-time Fraud Detection Real-time Customer 360 GenAI Applications Agentic AI ... 65
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Stream Process Connect Govern Our TAM Continues to Expand As We Build a Unifying End-to-End Platform $100B+ $50B 2021 2025 Tableflow ● Data Portal ● Data Contracts ● CP Flink ● CC Flink ● AI Inference ● User Defined Functions ● Custom Connectors ● Ecosystem integrations ● CDC, ERP and Fully-managed Connectors ● Freight ● WarpStream ● Enterprise ● Availability on all major public clouds ● Hybrid and Multi-cloud Data Interoperability w/ Cluster Linking ● Catalog, Lineage, Quality, Schema registry ● ksqlDB, KStreams ● On-prem Connectors ● Confluent On-prem ● Early Confluent Cloud 2021 2025 Confluent TAM based on estimated share of each Gartner market from 2021 to 2028, which is tied to our current product offering and planned product roadmap: 2021 TAM estimate sources: Gartner, “Enterprise Infrastructure Software, Worldwide, 2021-2027, 1Q23 Update” by Arunasree Cheparthi et al, 28 March 2023 and Gartner, “Forecast: Enterprise Application Software, Worldwide, 2021-2027, 1Q23 Update”, by Amarendra et al, 28 March 2023 2025 TAM estimate sources: Gartner, “Forecast: Enterprise Application Software, Worldwide, 2022-2028 4Q24 Update,” by Amarendra et al, 20 December 2024 and Gartner, “Forecast: Enterprise Infrastructure Software, Worldwide, 2022-2028 4Q24 Update,” by Arunasree Cheparthi et al, 17 December 2024 [1] Market size based on Gartner estimates and Confluent product share based on internal analysis of use cases in each Gartner market category addressable with generally available Confluent products Confluent TAM based on estimated share of each Gartner market from 2021 to 2025, which is tied to our current product offering and planned product roadmap 66
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Industry Analyst Leadership Recognition The Forrester Wave™ Streaming Data Platforms, Q4 2023 The Forrester Wave™ Cloud Data Pipelines, Q4 2023 67
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Our Massively Unique Strategic Opportunity The Market Leading Data Streaming Platform Company Large and growing TAM, accelerated by GenAI and Agentic AI From single product to multiple products in our portfolio to unify data everywhere High innovation velocity expanding to new growth vectors Ecosystem partners fueling seamless integrations for a powerful flywheel effect Category recognized, with analysts increasingly researching and positioning us as leaders 68
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The Future of Agentic AI is Event-DrivenAndrew Sellers Head of Technology Strategy, Confluent Andrew Sellers Head of Technology Strategy 69
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We Are Entering the Third Wave of AI The Reflex: Learned once, then hardwired: touch the hot stove, pull your hand back. Use Case: Customer churn prediction, account manager alerted to reach out. THEN Purpose-built AI NOW Generative AI The Brain: Reason and respond to conversational requests in real-time. Use Case: Customer service bot able to answer customer questions, grounded in domain and customer context. FUTURE Agentic AI The Body: Reason, coordinate, and act based on their environment. Use Case: Customer service bot is able to decide when to offer a refund, execute a refund, reschedule, or offer compensation for a cancellation. 70
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What Makes Agents Different? Agents are software systems that have agency to make decisions VARIABLE FLOWFIXED FLOW AUTONOMY TRADEOFF CONTROL LOGIC PROGRAMMATIC AGENT 71
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How a Human SDR Qualifies and Engages a Lead EVALUATE AND RESEARCH LEAD HOW TO ENGAGE? Check lead inputs, look at lead website, determine persona and ICP fit, add lead enrichment data Determine next steps, should I nurture the lead or actively engage and try to book a meeting? ACTIVELY ENGAGE Connect on LinkedIn, send a message, craft an email, and send the email trying to book the meeting NURTURE Design a new highly personalized campaign based on the lead source information and persona 72
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Automating the SDR Workflow with AI Agents HOW TO ENGAGE? Check lead inputs, look at lead website, determine persona and ICP fit, add lead enrichment data Determine next steps, should I nurture the lead or actively engage and try to book a meeting? Route lead into a pre-existing nurture campaign ACTIVELY ENGAGE Connect on LinkedIn, send a message, craft an email, and send the email trying to book the meeting NURTURE Design a new highly personalized campaign based on the lead source information and persona Lead Ingestion Agent Lead Routing Agent Active Outreach Agent Nurture Campaign Agent EVALUATE AND RESEARCH LEAD 73
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SINGLE AGENT Agents at Scale are Complex Systems They don’t exist in isolation, they have many dependencies Agent 1 Memory Reason Act Perceive LLMPlan External Systems Feedback Systems Tools MULTI-AGENT Agent 2 Shared Memory Agent nAgent 1Learn 74
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Scaling Microservices Has a Similar Set of Challenges Breaking the monolith created new challenges, like tight coupling and brittle dependencies Microservice C Microservice D Microservice EMicroservice B Microservice A Microservice B Microservice A Microservice E Microservice C Microservice DEvent Broker Published Events Consumed Events 75
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Event-driven Decoupled architecture Immutable Robust security controls Real-time and performant Fully managed cloud-native service Databases SaaS apps Telemetry Data Sources 120+ pre-built Connectors Stream Processing Stream Governance Scalable, independent agents Context-aware data sharing Low latency Timely and accurate decision-making … DWH, Data Lake CRM, CDP Apps & microservices Data Sinks … Multi-Agent Systems Orchestrator- worker pattern Blackboard pattern Market-based pattern Hierarchical pattern Data Streaming: The Shared Language of Agents Continuously ingests, process, and govern streams of data in real-time Agent 1 Agent 2 Agent 3 Agent n Agents 76
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Confluent is the Communication Layer and Data Enabler in the Modern AI Stack APPLICATION FRAMEWORKS LLMOps DATA PLATFORM & MANAGEMENT FOUNDATION MODELS Prompt Engineering Fine Tuning / Model Eval / Model Deploy Monitoring / Observability Enterprise Data Data Ingestion Cleaning Vector Store Data Lake / Warehouse CLOUD PLATFORMS COMPUTE End-to-end Apps with Confluent Data Streaming Platform Llama 2 77
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“We rely on Confluent to stream GenAI and Agentic AI outputs across our architecture for our customers, tracking token usage and enabling accurate billing. Confluent makes it easy for us to iterate quickly and build new AI features in days instead of weeks.” Adam Watkins, Cofounder & CTO Enabling Customers to Build Real-time GenAI “To save our users time, write faster, and boost creativity… we use Confluent to share new content and updates in real time. Our product and engineering teams use data products without worrying about infrastructure. This speeds up our GenAI use cases.” Daniel Sternberg, Head of Data & AI “With Confluent, we build AI copilots that allow users to interact with data in natural language, turning Flink jobs into agents that continuously monitor data streams. Flink AI Model Inference simplifies our stack… providing real-time context to generate the most accurate Flink SQL queries." Steffen Hoellinger, Cofounder & CEO 78
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Event-Driven Multi-Agent Demo Mike Agnich GM & VP, Product Management 79 79
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View my order $45.00 $60.00 Life Jacket Size M Waterproof Pants Size M ITEMS 80
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APPS AND AGENTS ANALYTICAL ESTATE Govern Process Connect Queries Analytics AI / ML … Tableflow BUSINESS-READY BUSINESS-READY BUSINESS-READY Universal Data Products Data Warehouse / Data Lake CLEANSED StreamCustomer Insights Agent Engagement Strategy Agent Content Generation Agent Event Driven Multi-Agent AI Same Great Architecture, New AI Use-Case 81
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We will enable developers to take action on everything that is happening at their “company—every click, every database change, every application log—and make it all available as a real-time stream of well structured data” - Jay Kreps 2015 82
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APPS AND AGENTS ANALYTICAL ESTATE Govern Process Connect Queries Analytics AI / ML … Tableflow BUSINESS-READY BUSINESS-READY BUSINESS-READY Universal Data Products Data Warehouse / Data Lake CLEANSED StreamCustomer Insights Agent Engagement Strategy Agent Content Generation Agent Event Driven Multi-Agent AI Same Great Architecture, New AI Use-Case 83
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OPERATIONAL ESTATE ANALYTICAL ESTATE Tableflow From Streams to Tables Within Minutes TABLEFLOW 84
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Our Next-Gen Go-To-Market Model Erica Schultz President of Field Ops 85 85
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Note: Revenue Run Rate calculated by extrapolating total quarterly revenue. Profitable refers to positive non-GAAP operating margin. The charts are not to scale and are for illustrative purposes only. FY’14 FY’15 FY’16 FY’17 FY’18 FY’19 FY’20 FY’21 FY’22 FY’23 FY’24 Confluent Founded $100M+ RevenueRun Rate100+ Total Customers Act 3 Complete Data Streaming Platform Cloud Majority >50% of Sub Rev since Q1’24 Act 1 Confluent Platform IPO June 2021 $500M+ RevenueRun Rate 1,000+ Customers with $100K+ ARR 1,000+ Total Customers $10M+ Revenue Run Rate Act 2 Confluent Cloud >50% of Total Customers ~5,800 Total Customers Profitable 1st Non-GAAP Profitable Year $1B+ Revenue Run Rate Confluent’s 10 Year Journey 86
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ACT 2: CP+CC ACT 1: CP ACT 3: CP+CC+DSP GTM Bets to Support Our Multi-Act Journey BETS FinServ and Public Sector Focus Professional Services New Country Entry BETS CSP+ISV Partnerships Global Expansion Digital Native; PLG Consumption Transformation BETS Tech Exec Focus GSI Partnerships Specialization Verticalization One of the greatest open-source monetization engines One of the most powerful cloud businesses Becoming a best-in-class multi-product platform company Strategic Objectives 87
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Successful Transition to Consumption GTM Y/Y Customer Net Addition in FY’24 ~2x Growth Confluent 2000 Targets ~23% Penetration Consumption Operating Rigor Successful roll-out of sales tools, processes, and compensation structure Consumption Pipeline Visibility Landing customers earlier provides earlier visibility into future workload acquisitions & expansions Frictionless Adoption Evaluation of new products without delays from contracting and procurement cycles ROI and TCO- Driven Sales Drive strong ROI by delivering cost- effective solutions for a wide variety of workloads HIGH PROPENSITYHIGH VELOCITY 88
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Consumption Transformation Where the entire industry is headed toward Multi-Product Platform Experienced with a motion pioneered at multiple other industry leaders Act 3 has a Proven Playbook for Success ACT 2 ACT 3 89
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”We have 40,000+ ETL jobs today. It’s chaos… shifting data governance left would be transformational for our organization.” — Head of Data, Fortune 50 Financial Institution Shift Left 90
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Tech Execs Streaming Practitioners Analytic Practitioners 91
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“Only 3% of IT leaders report facing no significant data-related challenges when implementing AI solutions within their business.” — Confluent Data Streaming Report, 2024 92
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Confluent Powers AI AI Infrastructure Applications Embedding AI Enterprise Use Cases Partners & Customers 93
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Accelerating Our Databricks Partnership with SIs ”Progressive customers, including a top 10 U.S. financial institution, have embraced GoodLabs' Streaming AI solution, pairing Confluent and Databricks to deliver governed, real-time insights from mainframes and critical event sources." Thomas Lo | Founder & CEO GOODLABS “Improving is deepening our Confluent and Databricks partnership with a dedicated practice to deliver innovative, scalable data streaming solutions." Matt Russell | SVP of Sales IMPROVING “The digital economy demands a hyperconnected Enterprise Data Strategy. Onibex, with Confluent & Databricks, enables governed, real-time Analytics + AI data.” Gustavo Estrada | Chief Executive Officer ONIBEX “By integrating Confluent’s cloud-native platform with Databricks’ Lakehouse, businesses build real-time AI apps, driving faster insights and better decisions.” Liron Ben Yosef | Partner, Chief Technology Officer KPMG “EPAM has successfully worked with Databricks and Confluent to deliver quality AI workloads for our clients. Together we aim to usher in the era of real-time Al, and we are committed to making that a reality.” Valentin Tsitlik | SVP Head of Data and Analytics EPAM 94
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Stream Process Connect Govern $100B+ 2025 Tableflow DSP: Expands TAM and Captures Our $100B+ Opportunity Chart created by Confluent based on Gartner® reports 2025 Source: Gartner, “Forecast: Enterprise Infrastructure Software, Worldwide, 2022-2028 4Q24 Update,” by Arunasree Cheparthi et al, 17 December 2024 2025 Source: Gartner, “Forecast: Enterprise Application Software, Worldwide, 2022-2028 4Q24 Update,” by Amarendra et al, 20 December 2024 Market size based on Gartner estimates and Confluent product share based on internal analysis of use cases in each Gartner market category addressable with generally available Confluent products Confluent TAM based on estimated share of each Gartner market, which is tied to our current product offering and planned product roadmap Confluent Cloud Revenue (DSP) Confluent Cloud Revenue (Stream) Confluent Platform Revenue FY’19 FY’20 FY’21 FY’22 FY’23 FY’24 95
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Ryan Mac Ban SVP Global Head of Sales PREVIOUSLY 96
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Joint GTM with SIs Joint GTM for customer data transformations GTM Bets to Support DSP Shift-Left with Tech Execs Roundtables & EBCs Specialization Experts to drive adoption in largest accounts Verticalization Deeper understanding of use cases and value of DSP 97
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>150,000 ~5,800 Organizations using Kafka Confluent customers > OSK Orgs Soak up the World’s Kafka 98
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Continued GTM Bets to Support Kafka Strategic Partnerships Market Expansions Acquiring New Logos and Driving Growth Full-Spectrum Offering 99
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Confluent Cloud Warpstream BYOC with managed benefits inside customers’ cloud environment Confluent Platform Enterprise-grade self-managed software offering Enterprise Production-ready, enhanced security w/ private networking Common Use Cases: Microservices, real-time pipelines NEW Freight Low cost for high throughput, relaxed latency workloads Common Use Cases: Logging, monitoring, telemetry NEW NEW Basic Getting started Production ready for most applications Standard Soak Up the World’s Kafka Compelling TCO Across All Streaming Workloads Dedicated Customizable for any applications Common Use Cases: Ultra security sensitive workloads Common Use Cases: Logging, observability, data lake ingestion, analytics 100
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Delivering GTM Operating Leverage at Scale Drives leverage as expansion takes hold Land & Expand Revenue per S&M headcount 15%+ Y/Y Sticky customer base lowers cost to retain GRR >90% Improvement in non-GAAP S&M % revenue since 2021 25pts Efficiency Drivers Ahead ● Continued land, retain & expand with frictionless consumption model ● Leveraging partner ecosystem & driving strategic partnerships ● Cross selling multi-product DSP ● Prioritization on highest value segments and verticals 101
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Note: The charts are not to scale and are for illustrative purposes only. Confluent Founded Soak Up the World’s Kafka Deliver Incremental Value Through DSP Well Positioned For Act 3 102
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103 Stephanie Buscemi Chief Marketing Officer Talal AlBakr Chief Executive Officer A Conversation with Saudi Cloud Computing Company 103
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104 Stephanie Buscemi Chief Marketing Officer Shyam Mani Director of Engineering A Conversation with Affirm 104
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Driving Durable, Profitable Growth Rohan Sivaram Chief Financial Officer 105
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Confluent’s Growth and Profitability at $1B+ Scale Subs Rev Run-Rate Cloud Rev Run-Rate Non-GAAP Op Margin (41%) 5%$552M$1,003M $272M $56M >40pts~10x>3.5x 20% of Subs Rev 55% of Subs Rev Note: Refer to the slides in the section titled “GAAP to Non-GAAP Reconciliations” in the Appendix, for a reconciliation of our non-GAAP financial metrics to the most directly comparable GAAP financial measures. Subscription Revenue Run Rate and Cloud Revenue Run Rate calculated by extrapolating total quarterly revenue for the applicable quarter At IPO (Q1’21) Free Cash Flow Margin (28%) 11% >35pts Now (Q4’24) 106
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$100M+ Revenue Run Rate by FY’18 Act 1 Data Streaming Category Creation CP Leading the Way Note: Revenue Run Rate calculated by extrapolating total quarterly revenue Category Creation with Open Source Traction FY’14 FY’15 FY’16 FY’18FY’17 Confluent Platform Revenue Note: FY’18 Revenue Run Rate calculated by extrapolating total quarterly revenue for the quarter ended December 31, 2018. 107
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One of the Fastest to Achieve $100M+ Run Rate 4 5 6 7 7 7 7 9 CFLT ESTC TWLOSNOW NET OKTA DDOG MDB Note: Peer data derived from publicly available sources, including SEC filings. Years to $100M+ Revenue Run Rate based on number of years since founding date. Revenue Run Rate calculated by extrapolating total quarterly revenue. 108
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45%+ Subs Rev FY’19-24 CAGR Note: Subscription Revenue Run Rate and Cloud Revenue Run Rate calculated by extrapolating total fourth quarter revenue for the applicable year. Cloud revenue prior to FY’19 and DSP revenue prior to FY23 were not shown in the chart given immateriality. 1) Represents mix as a % of subscription revenue for the applicable year. ~10% to 50%+ cloud mix1 shift in the last 5 years $20M to $550M+ Cloud revenue run-rate in the last 5 years Act 2 Leadership in Data Streaming Category FY’14 FY’15 FY’16 FY’17 FY’18 FY’19 FY’20 FY’21 FY’22 FY’23 FY’24 Confluent Cloud Revenue $1B+ Revenue Run-rate within 10 years of founding Confluent Platform Revenue 109
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One of the Fastest to Reach $1B+ Run-Rate 9 10 11 11 11 12 12 14 SNOW CFLT DDOGESTC TWLO NET OKTA MDB Note: Peer data derived from publicly available sources, including SEC filings. Years to $1B+ Revenue Run Rate based on number of years since founding date. Revenue Run Rate calculated by extrapolating total quarterly revenue. 110
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Everything You’ve Heard About Confluent Earlier High Innovation Velocity Market-Leading Data Streaming Platform Soak up the World’s Kafka Consumption Driven GTM Model Secular Tailwinds: Cloud, Data, and AI Large and Growing TAM Partner Ecosystem 111
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Act 3 Complete Data Streaming Platform for the AI Era FY’14 FY’15 FY’16 FY’17 FY’18 FY’19 FY’20 FY’21 FY’22 FY’23 FY’24 Confluent Cloud Revenue (DSP) Confluent Cloud Revenue (Stream) Confluent Platform Revenue DSP Products Connect, Process, Govern, Tableflow Data Streaming Platform Single product to multi-product platform AI Problem = Data Problem DSP essential for AI success Note: This chart is not to scale and is for illustrative purposes only. Cloud revenue prior to FY’19 and DSP revenue prior to FY’23 were not shown in the chart given immateriality. 112
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Durable Growth 01 02 03 04 Large and Expanding TAM Leadership in Streaming Selling Multi-Product DSP Land and Expand Customer Momentum 113
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Confluent Benefitting From Cloud, Data & AI tailwinds... Note: 1) Public Cloud Services CAGR for the 2023-2028 period calculated based on *Gartner®, Forecast: Public Cloud Services, Worldwide, 2022-2028, 4Q24 Update, 19 December 2024. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. 2) Amount of data created, consumed, and stored 2010-2023, with forecasts to 2028, Source: Statista 3) Worldwide spend on AI-supporting technologies Source: IDC, "IDC Unveils 2025 FutureScapes: Worldwide IT Industry Predictions", October 2024. Cloud Data 20% CAGR Public Cloud Services, WW End User Spend Growth 394 ZB+ Global Data Creation by 2028 $749B+ Worldwide Spending on AI-Supporting Technologies by 2028 AI 1 2 3 114
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Stream Process Connect Govern …Which Supports a Large & Growing TAM $100B+ $50B 2021 2025 Tableflow Confluent TAM based on estimated share of each Gartner market from 2021 to 2028, which is tied to our current product offering and planned product roadmap: 2021 TAM estimate sources: Gartner, “Enterprise Infrastructure Software, Worldwide, 2021-2027, 1Q23 Update” by Arunasree Cheparthi et al, 28 March 2023 and Gartner, “Forecast: Enterprise Application Software, Worldwide, 2021-2027, 1Q23 Update”, by Amarendra et al, 28 March 2023 2025 TAM estimate sources: Gartner, “Forecast: Enterprise Application Software, Worldwide, 2022-2028 4Q24 Update,” by Amarendra et al, 20 December 2024 and Gartner, “Forecast: Enterprise Infrastructure Software, Worldwide, 2022-2028 4Q24 Update,” by Arunasree Cheparthi et al, 17 December 2024 [1] Market size based on Gartner estimates and Confluent product share based on internal analysis of use cases in each Gartner market category addressable with generally available Confluent products Confluent TAM based on estimated share of each Gartner market from 2021 to 2025, which is tied to our current product offering and planned product roadmap ● Data Portal ● Data Contracts ● CP Flink ● CC Flink ● AI Inference and User Defined Functions ● Custom Connectors ● Ecosystem integrations ● CDC, ERP and Fully-managed Connectors ● Freight ● WarpStream ● Enterprise ● Availability on all major public clouds ● Hybrid and Multi-cloud Data Interoperability w/ Cluster Linking ● Catalog, Lineage, Quality, Schema registry ● ksqlDB, KStreams ● On-prem Connectors ● Confluent On-prem ● Early Confluent Cloud 2021 2025 115
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Durable Growth 01 02 03 04 Large and Expanding TAM Leadership in Streaming Selling Multi-Product DSP Land and Expand Customer Momentum 116
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>95% of Kafka base yet to be monetized ~5,800 Confluent Customers 150,000+ orgs using Kafka Our Large Streaming Opportunity Note: Customer count for the quarter ended December 31, 2024 117
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SAM (2023) Confluent Cloud Warpstream Confluent Platform Enterprise-grade self-managed software offering Enterprise Production-ready, enhanced security w/ private networking Common Use Cases: Microservices, real-time pipelines NEW Freight Low cost for high throughput, relaxed latency workloads Common Use Cases: Logging, monitoring, telemetry NEW Basic Getting started Production ready for most applications Standard <5% mix1 Note: SAM stands for serviceable addressable market. Visuals are illustrative only. (1) Represents dollar mix of Enterprise, Freight and WarpStream as a percentage of Confluent Cloud streaming consumption for the quarter ended December 31, 2024 SAM (2025) Soak Up the World’s Kafka Compelling TCO Across All Streaming Workloads Dedicated Customizable for any applications Common Use Cases: Security sensitive workloads BYOC with managed benefits inside customers’ cloud environment NEW Common Use Cases: Logging, observability, data lake ingestion, analytics 118
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Winning on All Fronts: Faster, Better & Cheaper 10x Better than OSK, up to 90% Cheaper1 Note: $-based Win rate for the quarter ended December 31, 2024 1) Based on Confluent’s internal estimated operating cost comparison of Freight clusters vs self-managing Kafka. ~95% Win Rate vs CSPs ~91% Win Rate vs Startups 119
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Durable Growth 01 02 03 04 Large and Expanding TAM Leadership in Streaming Selling Multi-Product DSP Land and Expand Customer Momentum 120
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Connect Self-managed Process Govern Stream Quality Stream Lineage Stream Basic Standard Dedicated Enterprise Freight WarpStreamCluster Linking Confluent Server Tableflow DSP Fully-managed DSP DSP DSP Custom Flink Stream Catalog Deployment Switzerland Cloud BYOC On-prem Hybrid Platformization: One-Stop Shop for Data Streaming Data Switzerland Database Data Lake Data Warehouse Lakehouse Cloud Switzerland 121
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DSP On-Prem Momentum ON PREM CP Connect CP Flink GA date: Q4’24 $100M+ ARR 20%+ of total CP $20M+ ACV Open Pipeline Note: ARR for the quarter ended December 31, 2024. Open Pipeline as of end of February 2025 122
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DSP Cloud Momentum Cloud Customer NRR by # of products Avg Lifetime Cloud Spend by # of products Note: All metrics are cloud metrics based on cloud customer cohorts for the quarter ended December 31, 2024. DSP Products represented are Connect, Process and Govern. Cloud Customer NRR based on median cloud NRR of customers with 1, 2, 3 and 4 products. Average Lifetime Cloud Spend illustrates the difference in average ARR lifetime spend across cloud customer cohorts. Cloud Customer Mix by # of products DSP % of Cloud Consumption: ~13%DSP % of Cloud PipeGen: ~20% +35 PTS 28% 48% 18% 6% + 1 DSPStream + 2 DSP + 3 DSP 18x CLOUD + 1 DSPStream + 2 DSP + 3 DSP + 1 DSPStream + 2 DSP + 3 DSP 123
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DSP Cloud Customer Example Cloud Consumption ARR Expansion since Land $1M+ ARR Customer Uses Streaming plus 3 DSP products DSP accounts for 50% of usage in Q4’24 up from 15% in Q4’23, supported by Flink adoption Regional Superstore CLOUD Streaming % DSP % Q2’21 Q4’22 Q4’23 Q4’24 6x 10x 32x 1x 124
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Durable Growth 01 02 03 04 Large and Expanding TAM Leadership in Streaming Selling Multi-Product DSP Land and Expand Customer Momentum 125
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Robust Customer Growth: Land & Expand Momentum ≥$1M ARR Customers≥$100K ARR CustomersTotal Customers 194 27 1,381 337 ~5,800 820 48% CAGR 33% CAGR 48% CAGR FY’19 FY’20 FY’21 FY’22 FY’23 FY’24FY’19 FY’20 FY’21 FY’22 FY’23 FY’24FY’19 FY’20 FY’21 FY’22 FY’23 FY’24 126
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<5% 150,000+ orgs using Kafka Solidify Our Streaming Leadership 24% of Confluent Customers paying $100K+ 14% of $100K+ Customers paying $1M+ >95% of Kafka base yet to be monetized ~5,800 Confluent Customers Note: Customer count for the quarter ended December 31, 2024 127
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Top Fortune 500 Companies Use Confluent Fortune 500 Companies Using Confluent42% Note: Confluent customer counts by Fortune 500 top industry and 42% are as of Q4’24 ended December 31, 2024, using the 2024 Fortune 500 list. Banking 10 OUT OF 10 Global Telco 10 OUT OF 10 Global Car Makers 9 OUT OF 10 Insurance 9 OUT OF 10 Technology 9 OUT OF 10 Travel 9 OUT OF 10 Entertainment 7 OUT OF 10 Healthcare 7 OUT OF 10 128
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Customer Spend Momentum Minimum ARR to be a Top Customer ($ millions) Top 10 Customer Top 20 Customer Top 100 Customer ~8x Growth ~6x Growth ~5x Growth FY’18 FY’24FY’21 FY’18 FY’24FY’21 FY’18 FY’24FY’21 $1.4M $3.4M $7M+ $0.8M $2.4M $5M+ $0.2M $0.9M $1.5M+ Note: ARR for the quarter ended December 31, 2024. 129
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Stream + 2 DSP Products9 Unique Industries 45% Hybrid 25x+ ARR expansion since land(1) 70%+ ARR growth in last 2 years $5M+ minimum ARR required for top 20 Top 20 Customers Q1 17 Q2 17 Q3 17 Q4 17 Q1 18 Q2 18 Q3 18 Q4 18 Q1 19 Q2 19 Q3 19 Q4 19 Q1 20 Q2 20 Q3 20 Q4 20 Q1 21 Q2 21 Q3 21 Q4 21 Q1 22 Q2 22 Q3 22 Q4 22 Q1 23 Q2 23 Q3 23 Q4 23 Q1 24 Q2 24 Q3 24 Q4 24 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 $100K+ ARR $1M+ ARR $5M+ ARR<$100K ARR(1) ARR Expansion multiple based on median since Land to Q4’24 130
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Profitable Growth 131
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Track Record of Margin Expansion Non-GAAP Operating Margin % Note: Refer to the slides in the section titled “GAAP to Non-GAAP Reconciliations” in the Appendix, for a reconciliation of our non-GAAP financial metrics to the most directly comparable GAAP financial measures. Time to profitability defined as the first publicly reported fiscal year with positive non-GAAP operating margin and based on number of years since founding date; Datadog’s profitability calculated based on FY17 full year non-GAAP operating margin period. Peer data derived from publicly available sources, including SEC filings. Non-GAAP operating margin for peers may not be calculated in the same manner as for Confluent. FY’21 FY’24 Years to Full-Year Profitability 3% (7%) (30%) (41%) FY’22 FY’23 10 18 15 1212 11 8 11 10 CFLT ESTC SNOWDDOG TWLO OKTA NET MDB PLTR 132
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Consistent Operating Discipline Last 3 year Op Margin Expansion (in percentage points) GTLB CFLT IOT BASE PRO DOCNMDB NET APPNESTC FROGYEXT DOMO FSLY DDOG PLTR MANHSNOW AMPL PATH DTSEMR NICE MSFT Note: Based off difference of CY’24 vs CY’21. For those companies that have not reported CY’24, CY’24 represents CY’24E from Factset as of 3/5/25. 49 44 34 29 21 17 18 15 14 13 13 12 15 11 11 11 9 9 8 7 6 3 3 3 133
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Operating Above Best-in-Class Gross Margin of 80%+ FY’21 FY’23 FY’24FY’22 Subscription Gross Margin (Non-GAAP) 76% 80% 81% 77% +5 pts Strong Confluent Platform Margin Key Margin Drivers Economies of Scale in Cloud & Operational Discipline Increasing Mix of DSP Continuing to Operate Above Best-In-Class Gross Margin of 80%+ Note: Refer to the slides in the section titled “GAAP to Non-GAAP Reconciliations” in the Appendix, for a reconciliation of our non-GAAP financial metrics to the most directly comparable GAAP financial measures. 134
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6 pts ● Leveraging lower-cost regions to optimize operations ● Invest in AI and automation to drive efficiency Driving Sustained Operational Efficiency ● Leveraging economies of scale ● Invest in innovation ● Land & expand motion coupled with DSP adoption driving efficiency ● Invest in sales specialization to drive transition to a multi-product platform Non-GAAP R&D % of Revenue FY’21 FY’23 FY’24FY’22 6 pts Non-GAAP S&M % of Revenue -25 pts Non-GAAP G&A % of Revenue -5 pts FY’21 FY’23 FY’24FY’22 -6 pts 29% 27% 24% 23% FY’21 FY’23 FY’24FY’22 67% 61% 48% 42% 15% 14% 11% 10% Note: Refer to the slides in the section titled “GAAP to Non-GAAP Reconciliations” in the Appendix, for a reconciliation of our non-GAAP financial metrics to the most directly comparable GAAP financial measures. 135
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Target Model 136
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Balanced Capital Allocation Strategy Efficient Organic Growth Disciplined M&A Return of Capital to Shareholders 137
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Multiple Drivers for the Next Wave of Growth FY’24 Confluent Platform Revenue Confluent Cloud Revenue (Stream) Confluent Cloud Revenue (DSP) Multiple Drivers Streaming Opportunity Continuing to land new customers in the 150K+ OSK base Data Streaming Platform ~13% CC consumption, growing substantially faster than CC AI Adoption Several dozen AI native companies Partner Ecosystem Strategic partnerships, and expanded GSI, MSP & OEM programs FY’23FY’22FY’21FY’20FY’19FY’18FY’17FY’16 Note: This chart is not to scale and is for illustrative purposes only. Cloud revenue prior to FY’19 and DSP revenue prior to FY’23 were not shown in the chart given immateriality. 138
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40+ pts Expanding Operating and Free Cash Flow Margins FY’21 FY’22 FY’23 FY’24 FY’25 FY’27 Long-term 25%+ (41%) (30%) (7%) 3% 6% 12% - 15% Note: Refer to the slides in the section titled “GAAP to Non-GAAP Reconciliations” in the Appendix, for a reconciliation of our non-GAAP financial metrics to the most directly comparable GAAP financial measures. 1) FY’25 Operating Margin guidance as of Confluent's Q4'24 earnings call dated February 11, 2025 FCF Margin Expansion In line with Non-GAAP OM 12%-15% by FY’27 and 25%+ Long-term Operating Leverage at scale Key Takeaways Non-GAAP Operating Margin (%) 1 139
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Path to GAAP Profitability SBC % of RevenueNet Dilution FY’24 FY’27 Long-Term Long-TermFY’27FY’24 ~25% Mid-Teens 41% ~3% ~2% <2% 140
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TAM $100B+ Market Fueled by Cloud, Data & AI Tailwinds TECH The Industry’s Only Complete Data Streaming Platform TEAM Track Record of Driving Growth and Profitability at Scale 141
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Q&A 142
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T H A N K Y O U 143
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Appendix 144
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Customer Count Starting in Q1’25, we will begin reporting on Core Customers ($20K+ ARR) on a quarterly basis and provide an update on Total Customer Count an annual basis. Core customers are our highest propensity customers and capture 95%+ Company ARR Contract Term Average total contract duration for Confluent Platform approximately 18-22 months. Taxes FY’26/FY’27: Non-GAAP tax $18M-$21M per year; Cash tax $14M-$18M per year As of December 31, 2024, had $1,404M federal, $561M state, $59M foreign NOL carryforwards Interest Income FY’26/FY’27: $60-70M per year CAPEX FY’25-FY’27: 2-3% of total revenue per year; Includes capital expenditures and amounts capitalized for internal-use software costs Other Key Modeling Points 145 145
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FY’21 - FY’25 Target Model FY’21 Actual FY’22 Actual FY’23 Actual FY’24 Actual FY’25 Guidance Mid-Term (FY’27) Long-Term Non-GAAP Operating Margin (41%) (30%) (7%) 3% 6% 12% - 15% 25%+ Free Cash Flow Margin (29%) (29%) (16%) 1% 6%* 12% - 15% 25%+ Target Model Summary Note: Refer to the slides in the section titled “GAAP to Non-GAAP Reconciliations” in the Appendix, for a reconciliation of our non-GAAP financial metrics to the most directly comparable GAAP financial measures. Note: FY’25 Free Cash Flow Margin guidance as of Confluent's Q4'24 earnings call dated February 11, 2025, and represents adjusted Free Cash Flow Margin, adjusting for one-time negative impact due to timing of payments related to our non go-to-market employee compensation structure 146
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Definitions Annual Recurring Revenue (ARR): We define ARR as (1) with respect to Confluent Platform customers, the amount of revenue to which our customers are contractually committed over the following 12 months assuming no increases or reductions in their subscriptions, and (2) with respect to Confluent Cloud and WarpStream customers, the amount of revenue that we expect to recognize from such customers over the following 12 months, calculated by annualizing actual consumption of Confluent Cloud and WarpStream in the last three months of the applicable period, assuming no increases or reductions in usage rate. Services arrangements are excluded from the calculation of ARR. Dollar-Based Net Retention Rate: We calculate our dollar-based net retention rate (NRR) as of a period end by starting with the ARR from the cohort of all customers as of 12 months prior to such period end (“Prior Period Value”). We then calculate the ARR from these same customers as of the current period end (“Current Period Value”), and divide the Current Period Value by the Prior Period Value to arrive at our dollar-based NRR. The dollar-based NRR includes the effect, on a dollar-weighted value basis, of our Confluent Platform subscriptions that expand, renew, contract, or attrit. The dollar-based NRR also includes the effect of annualizing actual consumption of Confluent Cloud and WarpStream in the last three months of the applicable period, but excludes ARR from new customers in the current period. Our dollar-based NRR is subject to adjustments for acquisitions, consolidations, spin-offs, and other market activity. Total Customers: Represent the total number of customers at the end of each period. For purposes of determining our customer count, we treat all affiliated entities with the same parent organization as a single customer and include pay-as-you-go customers. Our customer count is subject to adjustments for acquisitions, consolidations, spin-offs, and other market activity. Customers with $100,000 or greater in ARR: Represent the number of customers that contributed $100,000 or more in ARR as of period end. Customers with $1,000,000 or greater in ARR: Represent the number of customers that contributed $1,000,000 or more in ARR as of period end. 147
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GAAP to Non-GAAP Reconciliations (in thousands, except percentages) FY’21 FY’22 FY’23 FY’24 Subscription revenue $347,099 $535,009 $729,112 $922,091 Subscription gross profit on a GAAP basis $252,239 $388,685 $553,108 $713,491 Subscription gross margin on a GAAP basis 72.7% 72.7% 75.9% 77.4% Add: Stock-based compensation-related charges(1) 13,207 23,705 26,487 35,438 Add: Amortization of acquired intangibles - - 564 2,368 Non-GAAP subscription gross profit $265,446 $412,390 $580,159 $751,297 Non-GAAP subscription gross margin 76.5% 77.1% 79.6% 81.5% FY’21 FY’22 FY’23 FY’24 Total revenue $387,864 $585,944 $776,952 $963,642 Research and development (R&D) expense on a GAAP basis $161,925 $264,041 $348,752 $421,237 R&D expense as a % of total revenue on a GAAP basis 41.7% 45.1% 44.9% 43.7% Less: Stock-based compensation-related charges(1) 51,329 104,131 143,846 171,487 Less: Acquisition-related expenses - - 19,203 24,750 Non-GAAP R&D expense $110,596 $159,910 $185,703 $225,000 Non-GAAP R&D expense as a % of total revenue 28.5% 27.3% 23.9% 23.3% (1) Represents stock-based compensation expense, employer taxes on employee stock transactions, and amortization of stock-based compensation capitalized in internal-use software. We began excluding amortization of stock-based compensation capitalized in internal-use software from our non-GAAP measures starting with the quarter ended March 31, 2024. The amounts of amortization of stock-based compensation capitalized in internal-use software were immaterial in both current and prior periods. 148
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FY’21 FY’22 FY’23 FY’24 Total revenue $387,864 $585,944 $776,952 $963,642 General and administrative (G&A) expense on a GAAP basis $108,936 $125,710 $137,520 $156,703 G&A expense as a % of total revenue on a GAAP basis 28.1% 21.5% 17.7% 16.3% Less: Stock-based compensation-related charges(1) 35,610 45,122 50,595 60,466 Less: Common stock charitable donation expense 13,290 - - - Less: Acquisition-related expenses - 1,104 1,640 1,702 Non-GAAP G&A expense $60,036 $79,484 $85,285 $94,535 Non-GAAP G&A expense as a % of total revenue 15.5% 13.6% 11.0% 9.8% GAAP to Non-GAAP Reconciliations (in thousands, except percentages) FY’21 FY’22 FY’23 FY’24 Total revenue $387,864 $585,944 $776,952 $963,642 Sales and marketing (S&M) expense on a GAAP basis $319,331 $456,452 $504,929 $547,379 S&M expense as a % of total revenue on a GAAP basis 82.3% 77.9% 65.0% 56.8% Less: Stock-based compensation-related charges(1) 59,772 101,851 128,448 139,929 Less: Acquisition-related expenses - - 4,304 717 Non-GAAP S&M expense $259,559 $354,601 $372,177 $406,733 Non-GAAP S&M expense as a % of total revenue 66.9% 60.5% 47.9% 42.2% (1) Represents stock-based compensation expense, employer taxes on employee stock transactions, and amortization of stock-based compensation capitalized in internal-use software. We began excluding amortization of stock-based compensation capitalized in internal-use software from our non-GAAP measures starting with the quarter ended March 31, 2024. The amounts of amortization of stock-based compensation capitalized in internal-use software were immaterial in both current and prior periods. 149
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GAAP to Non-GAAP Reconciliations (in thousands, except percentages) FY’21 FY’22 FY’23 FY’24 Q1’21 Q4’24 Total revenue $387,864 $585,944 $776,952 $963,642 $77,028 $261,220 Net cash (used in) provided by operating activities ($105,060) ($157,333) ($103,657) $33,460 ($19,989) $35,211 Add: Capitalized internal-use software costs (5,342) (10,334) (17,845) (21,404) (596) (5,420) Add: Capital expenditures (3,600) (4,121) (2,834) (2,567) (643) (669) Free cash flow ($114,002) ($171,788) ($124,336) $9,489 ($21,228) $29,122 Free cash flow margin (29.4%) (29.3%) (16.0%) 1.0% (27.6%) 11.1% FY’21 FY’22 FY’23 FY’24 Q1’21 Q4’24 Total revenue $387,864 $585,944 $776,952 $963,642 $77,028 $261,220 Operating loss on a GAAP basis ($339,620) ($462,674) ($478,773) ($419,147) ($45,144) ($105,784) GAAP operating margin (87.6%) (79.0%) (61.6%) (43.5%) (58.6%) (40.5%) Add: Stock-based compensation-related charges(1) 165,713 284,666 360,864 417,101 13,616 107,579 Add: Common stock charitable donation expense 13,290 - - - - - Add: Amortization of acquired intangibles - - 564 2,368 - 780 Add: Acquisition-related expenses - 1,104 25,147 27,169 - 11,065 Add: Restructuring and other related charges - - 34,854 - - - Non-GAAP operating (loss) income ($160,617) ($176,904) ($57,344) $27,491 ($31,528) $13,640 Non-GAAP operating margin (41.4%) (30.2%) (7.4%) 2.9% (40.9%) 5.2% (1) Represents stock-based compensation expense, employer taxes on employee stock transactions, and amortization of stock-based compensation capitalized in internal-use software. We began excluding amortization of stock-based compensation capitalized in internal-use software from our non-GAAP measures starting with the quarter ended March 31, 2024. The amounts of amortization of stock-based compensation capitalized in internal-use software were immaterial in both current and prior periods. 150