Slides
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FEBRUARY 12, 2026
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This presentation and the accompanying oral presentation have been prepared by Datadog, Inc. (“Datadog” or the “company”) for informational purposes only and not for any other purpose. Nothing contained in this presentation is, or should be construed as, a recommendation, promise or representation by the presenter or Datadog or any officer, director, employee, agent or advisor of Datadog. This presentation does not purport to be all-inclusive or to contain all of the information you may desire. Information provided in this presentation speaks only as of the date hereof, unless otherwise indicated. This presentation and accompanying oral presentation contain “forward-looking” statements, as that term is defined under the federal securities laws, including but not limited to statements regarding Datadog’s strategy, product and platform capabilities, our investments in research and development and go-to-market, the growth in and ability to capitalize on long-term market opportunities including the pace and scope of cloud migration, digital transformation and AI deployment, the potential size of the cloud, observability and cloud security markets, and Datadog’s future financial performance in particular the goals presented in the section “Financial Goals” in this presentation. These forward-looking statements are based on Datadog’s current assumptions, expectations and beliefs and are subject to substantial risks, uncertainties, assumptions and changes in circumstances that may cause Datadog’s actual results, performance or achievements to differ materially from those expressed or implied in any forward-looking statement. The risks and uncertainties referred to above include, but are not limited to (1) our recent rapid growth may not be indicative of our future growth; (2) our history of operating losses; (3) our limited operating history; (4) our dependence on existing customers purchasing additional subscriptions and products from us and renewing their subscriptions; (5) our ability to attract new customers; (6) our ability to effectively develop and expand our sales and marketing capabilities; (7) risk of a security breach; (8) risk of interruptions or performance problems associated with our products and platform capabilities; (9) our ability to adapt and respond to rapidly changing technology or customer needs; (10) the competitive markets in which we participate; (11) risks associated with successfully managing our growth; (12) risks associated with changing laws, regulations, and contractual obligations related to data privacy and security and (13) general market, political, economic, and business conditions including concerns about trade policies, tariffs, reduced economic growth and associated decreases in information technology spending. These risks and uncertainties are more fully described in our filings with the Securities and Exchange Commission (SEC), including in the section entitled “Risk Factors” in our Quarterly Report on Form 10-Q for the quarter ended September 30, 2025, filed with the SEC on November 7, 2025. Additional information will be made available in our Annual Report on Form 10-K for the year ended December 31, 2025 and other filings and reports that we may file from time to time with the SEC. Moreover, we operate in a very competitive and rapidly changing environment. New risks emerge from time to time. 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. In light of these risks, uncertainties and assumptions, we cannot guarantee future results, levels of activity, performance, achievements, or events and circumstances reflected in the forward-looking statements will occur. Forward-looking statements represent our beliefs and assumptions only as of the date specified or as of this presentation, as applicable. We disclaim any obligation to update forward-looking statements. Safe Harbor
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Agenda Strategy, innovation, Datadog platform and product Q&A First half Go-to-market, delivering customer value, financial execution Q&A Intermission Second half Sean Walters Adam Blitzer David Obstler Olivier Pomel Yuka Broderick Olivier Pomel Alexis Lê-Quôc Yrieix Garnier Tim Knudsen Michael Whetten Yanbing Li Yuka Broderick
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Olivier Pomel CEO & Co-Founder
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1 What problem we solve and how 2 Our platform expansion 3 How we’re delivering with AI What I’ll cover today 4 Where we’re going as a company
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Cloud migration and digital transformation Gartner Forecast: Public Cloud Services, Worldwide - 2010-2016, 4Q12 Update; 2011-2017, 4Q13 Update; 2012-2018, 4Q14 Update; 2013-2019, 4Q15 Update; 2014-2020, 4Q16 Update; 2015-2021, 4Q17 Update; 2016-2022, 4Q18 Update; 2017-2023, 4Q19 Update; 2018-2024, 4Q20 Update; 2019-2025, 4Q21 Update; 2020-2026, 4Q22 Update; 2021-2027, 4Q23 Update; 2022-2028, 4Q24 Update; 2023-2029, 3Q25 Update. Gartner Market Databook - 4Q12 Update; 4Q13 Update; 4Q14 Update; 4Q15 Update; 4Q16 Update; 4Q17 Update; 4Q18 Update; 4Q19 Update; 4Q20 Update; 4Q21 Update; 4Q22 Update; 4Q23 Update; 4Q24 Update; 4Q25 Update. Cloud spend continues to grow rapidly Cloud Spend Cloud Spend as % of Global IT Spend
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The problem: an explosion of complexity Diversity of technologies in use Frequency of release Scale in number of computing units Dynamic Time Static Number of nodes Physical servers Cloud instances Containers Serverless & microservices TFLOPs Number of people involved Integrated People Time Ops Dev + Ops Security + Dev + Ops + Business AI Agents + Security + Dev + Ops + Business Siloed Business + Dev + Ops
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AI market is expected to grow quickly Gartner Forecast: AI Spending in IT Markets, Worldwide, 2024-2026, September 2025. AI spending on IT Markets
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AI compounds complexity Frequency of releases Diversity of technologies in use Number of people involved Scale in number of computing units Number of Models on Hugging Face Developer productivity Source for number of models: Hugging Face Hub Stats Dashboard, cfahlgren1, 2025.
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Datadog solves complexity
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Datadog non-GAAP R&D spend (1) Datadog R&D headcount Ending 2025 with nearly 4,000 engineers >$1B in 2025 R&D investment Datadog invests in innovation (1) Non-GAAP measure. See Appendix for a reconciliation of these non-GAAP measures to the most directly comparable GAAP measures.
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Our history of innovation
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Supporting our customers end-to-end Monitor Operate OptimizeShipTest Improve User Experience Run Support Users Code Drive Business Outcomes Resolve Issues
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Starting with Observability Monitor Operate OptimizeShipTest Run Support Users Code Resolve Issues Observability End-to-end, simplified visibility into tech stack health & performance ● Infrastructure Monitoring ● Network Monitoring ● Cloud Cost Management ● GPU Monitoring ● Application Performance Monitoring ● Continuous Profiler ● LLM Observability ● Database Monitoring ● Log Management ● Observability Pipelines ● AI Agents Console Improve User Experience Drive Business Outcomes
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Extending to Data Observability Monitor Operate OptimizeShipTest Run Support Users Code Resolve Issues Observability Data Observability Prevent, detect, and resolve data availability and quality issues in data warehouses and tools ● Data Streams Monitoring ● Data Jobs Monitoring ● Data Quality Monitoring Improve User Experience Drive Business Outcomes
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Building out Digital Experience / Product Analytics Observability Data ObservabilityData Observability Digital Experience Monitoring Optimize front-end performance and enhance user experiences ● Real User Monitoring ● Synthetic Testing ● Mobile App Testing ● Product Analytics ● Session Replay ● Experimentation ● LLM Experimentation Monitor Operate Optimize Support Users Resolve Issues ShipTest RunCode Improve User Experience Drive Business Outcomes
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Support Users Digital Experience Monitoring Improve User Experience Drive Business Outcomes Shifting left to developers Observability Data ObservabilityData Observability Monitor Operate OptimizeShipTest RunCode Resolve Issues Software Delivery Build, test, secure, and ship quality code faster ● CI Visibility ● Test Optimization ● Continuous Testing ● Error Tracking ● IDE Plugins ● Feature Flags ● Datadog MCP Server ● Bits AI Dev Agent
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Software Delivery Observability Data Observability Digital Experience Monitoring Securing the cloud stack end-to-end Data Observability Security Detect, prioritize, and respond to threats in real-time Monitor Operate OptimizeShipTest Run Support Users Code Resolve Issues Improve User Experience Drive Business Outcomes ● Cloud Security ● Code Security ● Cloud SIEM ● Data Security ● Bits AI Security Agent ● Security: AI Guard
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Software Delivery Observability Data Observability Digital Experience Monitoring Data Observability Closing the loop and taking action Service Management Integrated, streamlined workflows for faster time-to-resolution ● On-Call ● Incident Management ● Event Management ● Resource Catalog ● Internal Developer Platform ● Workflow Automation ● App Builder ● Bits AI SRE Agent Security Monitor Operate OptimizeShipTest Run Support Users Code Resolve Issues Improve User Experience Drive Business Outcomes
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● Infrastructure Monitoring ● Network Monitoring ● Cloud Cost Management ● GPU Monitoring ● Application Performance Monitoring ● Continuous Profiler ● Error Tracking ● LLM Observability ● Database Monitoring ● Log Management ● Observability Pipelines ● AI Agent Console Observe, Secure, Act with Datadog Security Detect, prioritize, and respond to threats in real-time ● Cloud Security ● Code Security ● Cloud SIEM ● Data Security ● Bits AI Security Agent ● Security: AI Guard Service Management Integrated, streamlined workflows for faster time-to-resolution ● On-Call ● Incident Management ● Event Management Software Delivery Build, test, secure, and ship quality code faster ● CI Visibility ● Test Optimization ● Continuous Testing ● IDE Plugins ● Feature Flags ● Datadog MCP Server ● Bits AI Dev Agent Digital Experience Monitoring Optimize front-end performance and enhance user experiences ● Real User Monitoring ● Synthetic Testing ● Mobile App Testing ● Product Analytics ● Session Replay ● Experimentation Data Observability Prevent, detect, and resolve data availability and quality issues in data warehouses and tools ● Data Streams Monitoring ● Data Observability Observability End-to-end, simplified visibility into tech stack health & performance Monitor Operate OptimizeShipTest Run Support Users Code Resolve Issues ● Resource Catalog ● Internal Developer Platform ● Workflow Automation ● App Builder ● Bits AI SRE Agent Improve User Experience Drive Business Outcomes
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A balanced, unified platform December 2025 $ ARR Digital Experience Monitoring (DEM) Application Performance Monitoring (APM) Infrastructure Monitoring Log Management APM & DEM
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Datadog is delivering with AI Datadog for AI End-to-end observability and security across the AI stack • LLM Observability • Data Observability • GPU Monitoring • AI Guard • AI Agents Console AI for Datadog AI-powered capabilities in the Datadog platform • Bits AI SRE Agent • Bits AI Dev Agent • Bits AI Security Analyst • Bits AI Assistant • MCP Server
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AI for Datadog g Security Cloud Service Management Software Delivery Digital Experience MonitoringObservability Monitor Operate OptimizeShipTest Understand Users Run Support Users Code Understand Business Resolve Issues ● Bits AI SRE Agent ● Bits AI Incident Commander ● Voice AI for Mobile ● Bits AI Assistant (Chat) ● Conversational AI for Apps and Workflows ● Bits AI Remediation Agent ● AI Incident Onboarding ● AI Agent Builder ● AI Incident Analytics Insights ● AI Incident Video ● Bits AI Security Analyst ● Anomalous Behavior Detection ● Vulnerability false positive filtering ● AI-native Static Analysis ● Bulk vulnerability remediation ● Sensitive data detection • Bits AI Detection • Natural Language Log Querying • Bits AI Kubernetes Remediation • APM Latency and Error Investigations • Architecture Recommendations • Bits AI FinOps Agent • Business Impact Analysis • Updog.ai • AI Research - Foundation Models • Bits AI Dev Agent • Datadog MCP Server • Bits AI Deployment Agent • CI Autofixing • Flaky Tests Quarantine • Automated Test Generation • Bits AI RUM Analyst • Automated End-to-End Synthetic Test Generation • UX Recommendations • Synthetic A/B Tests
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Datadog for AI • LLM Observability • LLM Playground • GPU Monitoring • Distributed AI Observability • AI Agents Experimentation • Data Observability • AI Gateway Security Cloud Service Management Monitor Operate OptimizeShipTest Understand Users Run Support Users Code Understand Business Resolve Issues • MCP Server • Feature Flags • Bits AI Dev Agent • Bits AI Deployment Agent • LLM Experiments in CI • Deployment Gating • LLM Experimentation • Sentiment Analysis • Synthetic AI Agent Testing • Internal Developer Portal • App Builder • AI Agents Console • Workflow Automation • Case Management • Prompt Injection Protection • Malicious Tool Protection • Data Security • Auth Bypass Prevention • Secrets & Data Leak Redaction • Containment Policies (MCP , Tools, Data) • Discovery & Inventory • AI-SPM • Attack Path Analysis • Compliance & Audit • Automated Remediation Workflows • Supply Chain Detections • AI Integrations • AI Agent Builder • Multi-Agent Orchestration • AI Agent Task Triage • AI-Generated Relational Systems Software Delivery Digital Experience MonitoringObservability
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CLOSING THE LOOP The end-to-end decision, action, and automation platform for our customers
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First, closing the DevOps loop This is what we had before, production systems, if they break we fix them, etc. There’s a second key workflow that is becoming increasingly relevant, thanks to the rise of AI coding, that’s going from development to production. [bring Michael’s developer loop in - make it a circle loop again] The value used to be in build, and now it’s shifting to evaluate and __. And that’s where we are and that’s what we’re doing. And show the two loops side by side. Take actions that will bring system back to health When stress is detected, decide what actions to take Monitor systems for stress
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Now, closing the software development loop Experimentation, Data Warehousing, Analytics Modern CI/CD, Ephemeral Environments, Flags AI Coding
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Now, closing the software development loop What’s difficult: ●Contact with production, with other applications, with other agents, with end-users, with the real world ●Correctness, safety ●Business outcomes Before: 60-80% of the job Today: Order(s) of magnitude faster with AI coding agents
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The most impactful place in AI development Observability 3 Pillars User Experience Developer Experience Monitoring Availability Performance AI-Native Dynamic and Stochastic Spans Dev, Ops, Security, and Business Impact Highly automated Cloud-native Dynamic Spans Dev and Ops Legacy Static Reactive Autonomy Validation Security & Safety ControlAlignment
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A LONG-TERM VISION Enabling Autonomy across Dev, Ops and Security
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Alexis Lê-Quôc CTO & Co-Founder
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Trillions of metrics Trillions of datapoints per hour Billions of application traces Exabytes of logs
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AI Research at Datadog - Toto, our first model Cost ~$750k to train • Initial model cost ~$500k • Improving the model cost ~$250k Trained on >1 trillion datapoints • ~750B datapoints unique to Datadog • Trained on 4-10x more data than other leading time-series models Strong performance on observability benchmarks • Leader on BOOM benchmark • Leader on GIFT-Eval benchmark at time of release Released Toto as an open-weights model • >9 million downloads on Hugging Face • Among the most downloaded time-series forecasting models
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Training for Bits AI SRE Agent Internal evals to use for training Time 1,000’s of evals
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Hill climbing with internal evals Training for Bits AI SRE Agent Time % Accuracy
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Training for Bits AI SRE Agent More diverse sources for evals Time Customers with feedback in eval set
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Datadog’s AI advantage Broad operational context Trillions of datapoints Deep domain expertise AI Research / foundational models
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What if you use frontier AI models? Illustrative Example Cost % Accuracy 100% accuracy $10B$1M $100M$10M $1B Frontier models
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Frontier models are too expensive Illustrative Example Cost % Accuracy $10B$1M $10M $1B Frontier models $100M Small observability models 100% accuracy
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Moving towards autonomous operations Observability 3 Pillars User Experience Developer Experience Monitoring Availability Performance AI-Native Dynamic and Stochastic Spans Dev, Ops, Security, and Business Impact Highly automated Cloud-native Dynamic Spans Dev and Ops Legacy Static Reactive Autonomy Validation Security & Safety ControlAlignment
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Towards autonomous operations World model of system behavior Time Accuracy DATA Source Code Metrics Logs Traces Topology Events Alerts Research Evals Autonomous operations Predictive alerting & preemptive remediation Automated remediation Proactive alerting Customized and adaptive observability
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Yrieix Garnier Vice President, Product
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The Datadog platform Platform
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We built the platform first
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We built the platform first
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Enabling lean and agile product teams
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Breaking down silos with our unified platform
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Our platform progress 2015 2020 2025 Integrations 100+ 400+ 1,000+ Events ingested per hour Millions Billions Trillions Monthly Average Users (MAUs) 1,000s 100,000s Approaching 1M # of products 1 9 25 # of customers 2,060 14,200 32,700 # of $100k+ customers 57 ~1,230 ~4,310 # of $1M+ customers 0 101 603 All data as of December of the given year. Monthly Active Users are users at paying customers, who logged onto the Datadog platform in December of the given year
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AI customer example ~140 MAUsSMB (<1,000 FTEs)AI ~3 yrs as customer # of products Annual Recurring Revenue (ARR) 4 5 5 6 6 7 9 14 14 16
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Evolving the platform Platform Data scaling
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Log Management
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Log Management data scaling Log Management ARR, 2020-2025 ~7x
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Observability logs are a portion of log use cases Operational logs (production) Audit & config logs Transaction logs Network logs (low info density) Security logs QUERY FREQUENCY VOLUME & RETENTION LOG MANAGEMENT
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Expanding for new logs use cases Operational logs (production) Audit & config logs Transaction logs Network logs (low info density) Security logs QUERY FREQUENCY VOLUME & RETENTION FROZEN & ARCHIVE SEARCH LOG MANAGEMENT FLEX LOGS
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Flex Logs average stored events per month Exponential growth of Flex Logs Excludes AI-native customer usage.
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Flex Logs with Cloud SIEM Visualize security activity across systems or entities
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Cloud SIEM momentum Cloud SIEM revenue, 2020-2025 ~18x
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Customer example ~480 MAUsEnterprise (5K+ FTEs)Ecommerce ~7 yrs as customer # of products Annual Recurring Revenue (ARR) Adopted Flex Logs and Cloud SIEM 3 3 3 4 4 4 4 4 7 7 7 7 8 8 9 11 11 11 11 11 11 11 12 15 Adopted Logs, APM and Synthetics
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Evolving the platform Platform Enterprise coverage Data scaling
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On-prem Monitoring Cloud Cloud On Prem • Servers • Network devices • Wireless Access Point • End User Devices • Edge Devices
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Full stack complexity coverage LESS COMPLEXITY MORE COMPLEXITY
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Datadog BYOC (Bring Your Own Cloud) Petabyte Scale Stored Locally Global Visibility
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Tim Knudsen Vice President, Security Product
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The Silo Tax • Overwhelmed by the number of vulnerabilities and misconfigurations • Balance conflicting goals of security and development teams • Overwhelmed by the number of vulnerabilities to triage • Limited bandwidth to work with development teams • Security often slows down release pace • Often don’t have enough context to understand why fixing is a priority Developers Operations Security
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Datadog flattens the tax - Observability + Security + AI
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Software Composition Analysis (SCA) Static Code Analysis (SAST) Interactive Application Security Testing (IAST) Infrastructure as Code (IaC) Scanning Secret Scanning Code Security PROACTIVE REACTIVE Cloud Security AI Guard Sensitive Data Scanner AI & Data Security Cloud SIEM Bits AI Security Analyst Cloud SIEM Cloud Security Cloud Workload Protection App and API Protection Datadog Security Products
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Data-Driven DevOps Collaboration Telemetry Correlation ● Resource Catalog ● Cost Management ● Dashboards ● IDP Integrations Integrations ● Threat Intelligence ● Notebook ● Case Management ● Incident Management ● Workflows SINGLE AGENT FOR OBSERVABILITY AND SECURITY | 1,000+ INTEGRATIONS Bits AI Cloud Security Software Composition Analysis (SCA) Static Code Analysis (SAST) Interactive Application Security Testing (IAST) Infrastructure as Code (IaC) Scanning Secret Scanning Code Security AI Guard Sensitive Data Scanner AI & Data Security Cloud SIEM Bits AI Security Analyst Cloud SIEM Integrated Incident Response Platform Threat Intelligence ● On-Call ● Incident Management ● Automated Workflows Cloud Security Cloud Workload Protection App and API Protection
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Datadog is a proven Security provider 8,500+ Security Customers 1 in 4 Fortune 500 are Datadog Security customers Source: Datadog internal analysis. Data as of December, 2025
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Opportunity to increase Security wallet share 70% Of $1M+ ARR customers use 1 or more Datadog Security products Data as of December, 2025 2% Security ARR as % of total ARR in these $1M+ Security customers
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Annual Recurring Revenue (ARR) Security customer example ~270 MAUsMid-Market (1-5k FTEs)Media ~11 yrs as customer # of products Security ARR % of total (Dec-25): 20% Security ARR All Other ARR 1 1 1 1 1 1 1 1 1 1 1 1 2 3 4 4 4 4 4 4 6 6 7 8 9 9 9 9 9 11 12 12 12 12 12 13 17
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Security customer example 4 5 7 7 7 7 7 7 8 ~20 MAUsSMB (0-1k FTEs)Software ~2 yrs as customer # of products Annual Recurring Revenue (ARR) Security ARR % of total (Dec-25): 38% Security ARR All Other ARR
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Michael Whetten Senior Vice President, Product
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We live in an era of speed
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Complexity slows you down Frequency of releases Diversity of technologies in use Number of people involved Scale in number of computing units Source for number of models: Hugging Face Hub Stats Dashboard, cfahlgren1, 2025. Number of Models on Hugging Face Developer productivity
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Network Compute Infrastructure Platform & Storage Infrastructure Applications & Services Browsers/Devices Users Without Datadog: point products, monitoring gaps
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Network Compute Infrastructure Platform & Storage Infrastructure Applications & Services Browsers/Devices Users Without Datadog: Slow incident response
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~430 MAUsEnterprise (5K+ FTEs)Bank ~2 yrs as customer Based on information provided by the customer and Datadog internal analysis Latency spike impacting all credit transactions >4 hours to detect and resolve issues 300k+ transactions per hour impacted 99.5% uptime Unexpected drop in sales Shipping Cost Calculation failure on Black Friday ~$200k estimated gross revenue loss per day >400k customers with lower conversion rate Payments Platform Investments Platform Digital CommercePartner Portal Without Datadog: negative business impact
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Network Compute Infrastructure Platform & Storage Infrastructure Applications & Services Browsers/Devices Users Datadog: End-to-end, full-stack, unified observability
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Network Compute Infrastructure Platform & Storage Infrastructure Applications & Services Browsers/Devices Users Unified observability: find and fix problems faster
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~430 MAUsEnterprise (5K+ FTEs)Bank ~2 yrs as customer Based on information provided by the customer and Datadog internal analysis Time-to-detect reduced from 45 min to <8 min Detect and resolve issues in <1 hour >$1M / day avoidance of revenue loss Uptime improved to 99.95% Issue identified in <2 hours Anticipated critical business risk before Black Friday +35% click-through improvement on “Buy” button ~$10M GMV impact averted Payments Platform Investments Platform Digital CommercePartner Portal With Datadog: positive business impact
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User-first monitoring DEM APM Network Compute Infrastructure Platform & Storage Infrastructure Applications & Services Browsers/Devices Users
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Digital Experience Monitoring (DEM) connects Observability to User Experience Synthetics Real User Monitoring (RUM) Product Analytics Proactive workflow user experience monitoring Automatically track every action a user takes on an application Use data to drive product design and development decisions Feature Flags & Experimentation Measure the relationship between new features and user outcomes
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DEM expansion Digital Experience Monitoring $ARR Product Analytics Synthetics Real User Monitoring
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DEM and APM together deliver more value APM Only vs APM + DEM customers Average ARR per customer, APM + DEM vs. APM only APM Only APM + DEM APM Only APM includes core APM, Continuous Profiler, and Database Monitoring; DEM includes Synthetics, RUM, and Product Analytics. APM + DEM
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Multi-product adoption for unified observability Average # of products used by total, $100k+, and $1M+ ARR customers Average # of products, Total customers Average # of products, $1M+ customers Average # of products, $100k+ customers
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AI Agents work better on a platform Network Compute Infrastructure Platform & Storage Infrastructure Applications & Services Browsers/Devices Users
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Datadog supports equilibrium Pre-Production Software development loop Production DevOps loop
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DASH 2025 Feature Announcements
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Developer market is expanding rapidly Select AI developer tools, last disclosed ARR All figures are ARR as of the following dates: Cursor - 11/13/25 company blogpost; Anthropic Claude Code - 12/2/25 company blogpost; Lovable - 11/19/25 CEO comment; Repit - 9/10/25 company blogpost; Cognition - 9/8/25 company blogpost
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AI Coding and the 100x engineer?
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DASH 2026 Product and Feature Launches?
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Developers want to move fast (but they break things) Use a Fragmented, manual process Test situationally but not comprehensively Shipping takes longer More risks, incidents
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With Datadog: move fast and break nothing Use a Fragmented, manual process Test situationally but not comprehensively Shipping takes longer More risks, incidents Automated, integrated testing Ship features safely at the speed AI helps build them
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Datadog for modern developers Experimentation, Data Warehousing, Analytics Modern CI/CD, Ephemeral Environments, Flags AI Coding Datadog MCP Server Code Security IDE & Agent User Experimentation Error Tracking LLM Observability Internal Developer Portal Feature Flags CI Visibility Continuous Testing
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Datadog is delivering for AI Datadog for AI End-to-end observability and security across the AI stack • LLM Observability • Data Observability • GPU Monitoring • AI Guard • AI Agents Console AI for Datadog AI-powered capabilities in the Datadog platform • Bits AI SRE Agent • Bits AI Dev Agent • Bits AI Security Analyst • Bits AI Assistant • MCP Server
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AI Observability
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AI Agents Console
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AI Observability momentum Spans sent to AI Observability >10x
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Datadog for AI • LLM Observability • LLM Playground • GPU Monitoring • Distributed AI Observability • AI Agents Experimentation • Data Observability • AI Gateway Security Cloud Service Management Monitor Operate OptimizeShipTest Understand Users Run Support Users Code Understand Business Resolve Issues • MCP Server • Feature Flags • Bits AI Dev Agent • Bits AI Deployment Agent • LLM Experiments in CI • Deployment Gating • LLM Experimentation • Sentiment Analysis • Synthetic AI Agent Testing • Internal Developer Portal • App Builder • AI Agents Console • Workflow Automation • Case Management • Prompt Injection Protection • Malicious Tool Protection • Data Security • Auth Bypass Prevention • Secrets & Data Leak Redaction • Containment Policies (MCP , Tools, Data) • Discovery & Inventory • AI-SPM • Attack Path Analysis • Compliance & Audit • Automated Remediation Workflows • Supply Chain Detections • AI Integrations • AI Agent Builder • Multi-Agent Orchestration • AI Agent Task Triage • AI-Generated Relational Systems Software Delivery Digital Experience MonitoringObservability
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Yanbing Li Chief Product Officer
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What DevOps teams do Take actions that will bring system back to health When stress is detected, decide what actions to take Monitor systems for stress
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Lengthy incidents cause costly productivity loss Illustrative Example Cost of engineering time without Datadog Hours or days Hundreds of engineers TIME # OF PEOPLE CALLED • Loss of employee productivity • Poor customer experience • Bad business outcomes
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Take actions that will bring system back to health When stress is detected, decide what actions to take Monitor systems for stress Faster remediation with Datadog’s unified platform
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Incident resolution with Datadog Illustrative Example Cost of engineering time without Datadog Hours or days Hundreds of engineers TIME # OF PEOPLE CALLED • Faster initial detection • Alerting the correct on-call engineer with context • Faster MTTR (mean time to remediation) Hundreds of engineers Hours or days Cost of engineering time without Datadog Cost of engineering time using Datadog <1 hour Dozens of engineers TIME # OF PEOPLE CALLED
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Reduction in incidents Sev-1 incidents per month Based on information provided by the customer and Datadog internal analysis Production incidents per year ~3,200 MAUsEnterprise (5K+ FTEs)Insurance ~5 yrs as customer Sev-1 incidents per month Sev-1 incidents per monthSev-2 incidents per month Sev-2 incidents per month Sev-1 incidents per year Sev-2 incidents per year Sev-1 incidents per year Sev-2 incidents per year 10x reduction
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Reduction in downtime Datadog Saved: 70 employee years $11M annual labor cost ~3,200 MAUsEnterprise (5K+ FTEs)Insurance ~5 yrs as customer Based on information provided by the customer and Datadog internal analysis Engineer downtime in hours, per year Root Cause Analysis Incident Resolution Root Cause Analysis Incident Resolution 3.5x reduction
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Reduced business disruption Based on information provided by the customer and Datadog internal analysis Customer transactions impacted per year Customer transactions impacted per year Customer transactions impacted per year ~3,200 MAUsEnterprise (5K+ FTEs)Insurance ~5 yrs as customer 20x reduction
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Bits AI Detection Bits AI Dev Agent Bits AI Remediation Agent Bits AI SRE Bits AI Security Analyst Autonomous and predictive detection Autonomous remediation Autonomous investigation and root cause analysis Accelerated remediation with Bits AI Agents
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109 Bits AI SRE Your 24/7 AI On-Call Engineer
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REdesign this slide: - Change to split side by side design. Light background. - Keep the screen shot on the right side. (just cut to the tree diagram but not the left pane) - On the left side, add a vertical flow chart with the following boxes (vertical flow up to down) - ● Autonomously investigates upon alert ● Gathers data and context from multiple sources ● Reasons like engineers - generates multiple hypotheses and investigates and verifies in parallel ● Shows verified root cause with evidence ● Gets smarter with every investigation Autonomously investigates issues Gathers data and context from multiple sources Reasons like engineers Generates multiple hypotheses, investigates, and verifies in parallel Shows verified root cause with evidence Gets smarter with every investigation Bits AI SRE accelerates root cause analysis
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Bits AI SRE integrates with your data and context
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We ran a few alert investigations during the recent major AWS outage and it correctly identified DNS issues with DynamoDB as the root cause for issues which was nice to see. –JAPANESE VIDEO GAME CUSTOMER From day one, Bits AI SRE started cutting our MTTR [mean-time-to-resolution] by 70%. It felt like adding a senior engineer to our team. We had a total outage and I had no experience in the applications. Bits investigated and found the root cause in 15 minutes. It would have taken me at least 2 hours. Bits AI SRE Agent delivers in moments of high stress –US HEALTHCARE CUSTOMER —BRAZILIAN FOOD DELIVERY CUSTOMER
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BitsAI SRE has become an effective assistant for our SWE, Cloud, and Service Desk teams— quickly diagnosing critical alerts, reducing noise, and improving collaboration. –KENYAN FINANCIAL SERVICES CUSTOMER Bits immediately sped up our crisis room by delivering accurate root causes in under four minutes and directing us to the right team internally. –BRAZILIAN ENERGY CUSTOMER Bits AI helps us cut through the noise by instantly surfacing the right context and correlations across our systems. –US NEXT-GEN TRANSPORT CUSTOMER We found [Bits AI SRE] truly remarkable and see it as a breakthrough in our incident investigation process. –JAPANESE FINANCIAL SERVICES CUSTOMER The way [Bits AI SRE] autonomously formulates and thoroughly verifies hypotheses perfectly mirrors the thought process of a top-tier engineer. –JAPANESE SOFTWARE CUSTOMER With Bits AI SRE being on-call 24/7 for us, MTTR for our services have improved significantly. –SOUTH KOREAN MOBILE APP CUSTOMER Beyond reducing our incident response times, Bits AI SRE has a potential to elevate the overall skill level of our entire engineering organization. —US IT SERVICES CUSTOMER Bits AI SRE has been invaluable in reducing manual work and identifying false leads. –US FINANCIAL SERVICES CUSTOMER Customers ❤ Bits AI SRE
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BITS AI SRE AGENT CUSTOMER ADOPTION >100,000 Investigations run with Bits AI SRE Agent since inception
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BITS AI SRE AGENT CUSTOMER ADOPTION >2,000 Customers in the past month
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Bits AI Security Analyst investigates security threats Design - here’s the link if you need it
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Bits AI Dev Agent generates production ready code fixes
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… Customers are talking to Bits AI everywhere AI agents In Datadog Collaboration tools IDEs IntelliJ Cursor VS Code … Zoom Slack Teams … Bits AI Agents Voice … Chat and more and more and more Anthropic Gemini KiroOpenAI and more
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Datadog MCP Server usage is growing exponentially MCP Server tool calls
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Hundreds of engineers Hours or days Cost of engineering time without Datadog Cost of engineering time using Datadog <1 hour Dozens of engineers With Bits AI Agents Hundreds of engineers Hours or days <1 hour Dozens of engineers Using Bits AI Agents Minutes <10 engineers Illustrative Example TIME # OF PEOPLE CALLED • Faster initial detection • Alerting the correct on-call engineer with context • Faster MTTR (mean time to remediation)
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Closing the loops: DevOps to Software development Pre-Production Software development loop Production DevOps loop
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Hundreds of engineers Hours or days <1 hour Dozens of engineers Using Bits AI Agents Minutes <10 engineers A future vision - autonomy across Dev, Ops and Security Illustrative Example Hundreds of engineers Hours or days <1 hour Dozens of engineers Minutes <10 engineers TIME # OF PEOPLE CALLED • Validate the code works and is secure • Autonomously detect and predict anomalies • Prevent incidents before they occur with autonomous remediation • Intelligently adapt observability posture • Autonomous utilization and cost optimizations, code improvements, and security fixes
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A LONG-TERM VISION Enabling Autonomy across Dev, Ops and Security
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Sean Walters Chief Revenue Officer
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Our go-to-market motion
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Our go-to-market strategy Self serve •Indicates interest in Datadog •Funnel to Commercial team
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Self serve •Indicates interest in Datadog •Funnel to Commercial team Commercial sales team •Focused on new logos •Inside sales Our go-to-market strategy
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Commercial customers grow larger 72% of $100k+ ARR customers are Commercial As of December, 2025. 50% of $1M+ ARR customers are Commercial 24% of top 25 customers by $ ARR are Commercial
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Enterprise sales team •More complex customers •Customer lifecycle management Self serve •Indicates interest in Datadog •Funnel to Commercial team Commercial sales team •Focused on new logos •Inside sales Our go-to-market strategy
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Enterprise account executives, 2020-2025 Strategic Enterprise Major Accounts Key Accounts Enterprise sales team
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Addressing the largest customers Source: Datadog internal analysis, Bloomberg data. Datadog Fortune 500 logo penetration Median Datadog spend per Fortune 500 customer: ~$450K
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Enterprise land deals - average land ARR
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Enterprise customers expand Enterprise average land ARR & Enterprise average ARR, 2020-2025 Avg Land ARR Avg ARR 2020 2021 2022 2023 2024 2025
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Datadog’s Services and Technical Support Technical Account Managers (TAMs) • Experts in cloud monitoring and Datadog best practices • Technical guidance for deploying and using Datadog Implementation Services • Tailored implementation plan for migration and deployment Technical Enablement Services • Customized training programs • Ongoing learning resources Premier Support • Dedicated team of support engineers • Individualized support resources
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Go-to-market investment areas
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Resellers/partners Hyperscalers System Integrators Channel & Alliances
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C&As Channel-influenced $ ARR, % of total ARR 15% of total ARR 14% of total ARR 10% of total ARR ARR $s 7% of total ARR 10% of total ARR 4% of total ARR
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Security Specialist Sales Security Sales Engineers Direct GTM TIME Security Channel & Resellers Security GTM investments
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Example customer ARR by category, Dec-2025 Security Adoption Security 23% of total ARR All Other 1Q24 2Q24 4Q24 1Q25 3Q25 Cloud Security (CSPM) Cloud Security (App & API Protection) Cloud Security (Workload Protection) Code Security Cloud SIEM Ongoing rollout, replacement of on-prem SIEM Security customer example ~150 MAUsEnterprise (5K+ FTEs)Travel ~4 yrs as customer
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Historically meaningful presence Recent expansion Expanding GTM teams worldwide
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LATAM region Latin America revenues 86% CAGR
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Go-to-market focus on AI Datadog for AI End-to-end observability and security across the AI stack • LLM Observability • Data Observability • GPU Monitoring (preview) • AI Guard (preview) • AI Agents Console (preview) AI for Datadog AI-powered capabilities in the Datadog platform • Bits AI SRE Agent • Bits AI Dev Agent (preview) • Bits AI Security Analyst (preview) • MCP Server (preview) • Bits AI Assistant (preview)
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Executing on our GTM investments TCV Bookings, 2020-2025
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Adam Blitzer COO
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Customers choose Datadog and grow with us Datadog market share %, 2016-2025E 2016-2025 Market Share %s derived using Datadog 2016-2025 revenue and Health & Performance market size figures from Gartner Market Share: All Software Markets, 2016-2025
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Unified platform, single pane of glass More buying power through technical and pricing innovations More buying power through technical and pricing innovations Speed of innovation in the age of AI Delivering value to customers
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Single pane of glass across multiple products
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Single pane of glass brings developer productivity ~900 MAUsEnterprise (5K+ FTEs)Semiconductors ~7 yrs as customer Without Datadog 1-2With Datadog 12-13 8 Total hours per year 9 25 20 Major incidents per year FTEs per incident Hours per incident ~4,000 -83% -60% -50% -97%% reduction ~150 Additional 124,000 hours saved from productivity during incidents among broader engineering org This is based on the BVA assuming 15,000 engineers at 5% loss of productivity during the hours of the outage. They have a sensitivity table with productivity between 1 and 10%, and we can toggle the % loss of productivity and # of engineers if you want. Based on information provided by the customer and Datadog internal analysis
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Consolidating on one platform yields buying power Customer example: annualized costs ~2,500MAUsEnterprise (5K+ FTEs)Retail ~6 yrs as customer Based on information provided by the customer and Datadog internal analysis Observability costs Observability management Observability costs Observability management Developer productivity Avoidance of revenue loss Monitor management Alerting management
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Unified platform is critical in the age of AI
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More buying power through technical and pricing innovations Unified platform, single pane of glass More buying power through technical and pricing innovations Speed of innovation in the age of AI Delivering value to customers
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Datadog delivers value at scale Illustrative example. Volume-based pricing Discounts for volume, term Usage Cost Optimization of usage Innovation-driven efficiencies
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Customers scale with business growth ~140 MAUsSMB (<1K FTEs)AI ~2 yrs as customer # of products 8 9 9 9 9 9 9 10 Customer example - Annual Recurring Revenue (ARR)
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Customers scale as they adopt more products # of products 1 1 1 1 1 1 2 2 4 4 6 6 7 7 7 7 7 11 11 12 12 13 15 15 15 16 16 16 16 18 18 21 Annual Recurring Revenue (ARR) ~1,700 MAUs~8 yrs as customer Mid-Market (1-5K FTEs)Entertainment
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Indexed Logs revenue Unit price Indexed Logs volume Innovation-driven value ~380 MAUsEnterprise (5K+ FTEs)Technology ~8 yrs as customer Adopted Flex Logs ~75x ~4x
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Database Rightsizing Increased node density Kubernetes reduction in test/dev Efficiencies in code base Cloud instance reduction Kubernetes reduction in QA Customers save on cloud costs ~600 MAUsEnterprise (5K+ FTEs)Software ~10 yrs as customer Customer example: Cloud Infra savings with Cloud Cost Mgmt. Based on information provided by the customer and Datadog internal analysis
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Speed of innovation in the age of AI Unified platform, single pane of glass More buying power through technical and pricing innovations Delivering value to customers
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Datadog invests in innovation (1) Non-GAAP measures. See Appendix for a reconciliation of these non-GAAP measures to the most directly comparable GAAP measures Non-GAAP Research & Development expenditure vs. observability peers (1) Datadog Datadog Datadog Datadog Datadog Datadog Company 1 Company 1 Company 1 Company 1 Company 1 Company 1 Company 2 Company 2 Company 2 Company 2 Company 2 Company 2 Company 3 Company 3 Company 3 Company 3
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Our history of innovation
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19 AI-native customers with $1M+ Datadog ARR 70% of the top 20 AI native companies are Datadog customers ~650 AI-native customers Source: Datadog internal analysis. Top AI native companies ranked by latest publicly disclosed valuation. Datadog for AI native customers
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David Obstler CFO
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What you’ve heard today Solving our customers’ complex problems Delivering AI for Datadog and Datadog for AI Broadening our go-to-market Unified platform that delivers customer value Enabling Autonomy across Dev, Ops, and Security
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How we grow
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Land-and-expand business model Annual cohort growth ($ ARR) 2025 Multiple of first-year growth 2024: 1.4x 2023: 1.6x 2022: 1.8x 2021: 2.3x 2020: 3.9x 2019: 9.7x 2018: 7.4x 2017: 6.8x 2016: 11.7x 2015 & prior
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Existing customer ARR added New customer ARR added Prior Year ARR Land-and-expand business model
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Drivers of revenue growth % of ARR added by type New customer contribution Expansion of existing products Cross-sell of new products Existing customer contribution
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Strong execution delivers robust revenue growth As of December, 2025. Revenue per customer calculated from reported revenues and average customers during the period. Datadog customers Revenue per customer Datadog revenue 18% CAGR 68% CAGR 17% CAGR 42% CAGR
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Our growth opportunities
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Our growth drivers 1 Secular tailwind of digital transformation and cloud migration
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Cloud migration and digital transformation Gartner Forecast: Public Cloud Services, Worldwide - 2010-2016, 4Q12 Update; 2011-2017, 4Q13 Update; 2012-2018, 4Q14 Update; 2013-2019, 4Q15 Update; 2014-2020, 4Q16 Update; 2015-2021, 4Q17 Update; 2016-2022, 4Q18 Update; 2017-2023, 4Q19 Update; 2018-2024, 4Q20 Update; 2019-2025, 4Q21 Update; 2020-2026, 4Q22 Update; 2021-2027, 4Q23 Update; 2022-2028, 4Q24 Update; 2023-2029, 4Q25 Update. Gartner Market Databook - 4Q12 Update; 4Q13 Update; 4Q14 Update; 4Q15 Update; 4Q16 Update; 4Q17 Update; 4Q18 Update; 4Q19 Update; 4Q20 Update; 4Q21 Update; 4Q22 Update; 4Q23 Update; 4Q24 Update; 4Q25 Update. Cloud spend continues to grow rapidly Cloud Spend Cloud Spend as % of Global IT Spend
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Our growth drivers 1 Secular tailwind of digital transformation and cloud migration 2 Deployment of GenAI and agentic applications driving cloud usage
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AI - another large market opportunity Gartner Forecast: AI Spending in IT Markets, Worldwide, 2024-2026, September 2025. AI spending on IT Markets
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AI Observability momentum Spans sent to AI Observability >10x
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% of total UARR from customers using AI integrations We support our customers’ path to AI # of customers using AI integrations 2023 2024 2025 0% 20% 40% 60% 80%
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We support AI native customers’ business success AI native customer revenue 11% of revenue 5% of revenue 2% of revenue1% of revenue1% of revenue
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Our growth drivers 1 Secular tailwind of digital transformation and cloud migration 3 Growing and retaining customers 2 Deployment of GenAI and agentic applications driving cloud usage
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New logo opportunities >470,000 Global account opportunities ~32,700 Datadog customers Datadog’s logo penetration is 7% Source: Datadog internal analysis, HG Insights.
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Strong customer growth Total customers $1M+ customers $100k+ customers 18% CAGR 29% CAGR 43% CAGR $10M+ customers 63% CAGR
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Diversity of customers Dec-25 $ARR by industryDec-25 $ARR by region North America LATAM APAC EMEA Software AI Other Tech Media and Entertainment Prof. Services & Insurance Fintech & Payments Financial Services Other Industrials Healthcare Travel Consumer
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Source: Datadog internal analysis, Bloomberg data. Penetration of top 10 companies by vertical 8 of top 10 Telecommunications 8 of top 10 Industrials & Machinery 8 of top 10 Transportation & Logistics 9 of top 10 E-commerce 10 of top 10 Internet Services & Infrastructure 10 of top 10 Entertainment 10 of top 10 Payment & Transaction Processors 10 of top 10 Professional Services
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Gross Revenue Retention Percentage See Appendix for information regarding gross revenue retention. Total company: 97%+ SMB/mid-market: 96%+ Enterprise: 98%+
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Our Growth Drivers 1 Secular tailwind of digital transformation and cloud migration 3 Growing and retaining customers 4 Expanding products / use cases for customers 2 Deployment of GenAI and agentic applications driving cloud usage
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Multi-Product Adoption Multi-product adoption 2+ products 6+ products 4+ products 8+ products 10+ products
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3-Pillar ARR December 2025 $ ARR Digital Experience Monitoring (DEM) Application Performance Monitoring (APM) Infrastructure Monitoring Log Management APM & DEM
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~53% of our customers don’t have all 3 pillars yet 3-pillar customers use all 3 of core Infrastructure Monitoring, core APM, and Log Management. As of Dec-2025. 3-pillar customers Average revenue per customer: 3-pillar vs non-3-pillar Average revenue per customer is >15x higher for 3-pillar customers 3-pillar customers ~53% of our customers don’t use all 3 pillars
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# of products adopted Average ARR per customer taking multiple products# of customers taking multiple products # of products adopted Customers who use more products get more value
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Customers who use more products churn less Trailing twelve-month dollar-based gross retention rate by # of products adopted # of products adopted
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Our growth drivers 1 Secular tailwind of digital transformation and cloud migration 3 Growing and retaining customers 4 Expanding products / use cases for customers 5 Adding new markets beyond Observability 2 Deployment of GenAI and agentic applications driving cloud usage
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Large and Growing Observability TAM Gartner Forecast: Enterprise Infrastructure Software, Worldwide - 2020-2026, 4Q22 Update; 2021-2027, 4Q23 Update; 2022-2028, 4Q24 Update; 2023-2029; 4Q25 Update. Gartner Health & Performance Analytics category, 2020-2029E Our Observability market is $28B in 2026E
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We are adding multiple markets Gartner Forecast: Enterprise Infrastructure Software, Worldwide - 2015-2021, 4Q17 Update; 2016-2022, 4Q18 Update; 2017-2023, 4Q19 Update; 2018-2024, 4Q20 Update; 2019-2025, 4Q21 Update; 2020-2026, 4Q22 Update; 2021-2027, 4Q23 Update; 2022-2028, 4Q24 Update; 2023-2029; 4Q25 Update. Gartner Forecast: Enterprise Application Software, Worldwide - 2015-2021, 4Q17 Update; 2016-2022, 4Q18 Update; 2017-2023, 4Q19 Update; 2018-2024, 4Q20 Update; 2019-2025, 4Q21 Update; 2020-2026, 4Q22 Update; 2021-2027, 4Q23 Update; 2022-2028, 4Q24 Update; 2023-2029; 4Q25 Update. Gartner Forecast: Information Security, Worldwide - 2015-2021, 4Q17 Update; 2016-2022, 4Q18 Update; 2017-2023, 4Q19 Update; 2018-2024, 4Q20 Update; 2019-2025, 4Q21 Update; 2020-2026, 4Q22 Update; 2021-2027, 4Q23 Update; 2022-2028, 4Q24 Update; 2023-2029; 4Q25 Update. Datadog market opportunity by major product area Observability Infrastructure Monitoring, APM, Log Management, Synthetics, RUM, Network Monitoring, LLM Observability, AI Agents Console, and more Security Cloud Security, Code Security, Cloud SIEM, Data Security, Bits AI Security Agent, and more Software Delivery CI Visibility, Test Optimization, Continuous Testing, Bits AI Dev Agent, Datadog MCP Server, and more Service Management OnCall, Incident Management, Workflow Automation, Bits AI SRE Agent, and more Product Analytics
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Security Product Business Expansion Security product suite $ARR, 2020-2025 Security Product Suite includes Cloud Security, Code Security, AI & Data Security, and Cloud SIEM
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Our margins and profitability
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Datadog non-GAAP gross margin % Deploying new features/capabilities Efficiency projects / scale (1) Non-GAAP measures. See Appendix for a reconciliation of these non-GAAP measures to the most directly comparable GAAP measures
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Datadog non-GAAP R&D spend(1) Datadog R&D headcount Ending 2025 with nearly 4,000 engineers >$1B in 2025 R&D investment Datadog invests in innovation (1) Non-GAAP measures. See Appendix for a reconciliation of these non-GAAP measures to the most directly comparable GAAP measures
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Efficient GTM investment TTM 3Q25 Non-GAAP S&M as % of sales, % Y/Y TTM revenue growth (1) % Y/Y TTM revenue growth Non-GAAP S&M as % of sales Datadog Company 1 Company 2 (1) Non-GAAP measures. See Appendix for a reconciliation of these non-GAAP measures to the most directly comparable GAAP measures
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Datadog non-GAAP opex as % of sales R&D as % of sales S&M as % of sales G&A as % of sales (1) Non-GAAP measures. See Appendix for a reconciliation of these non-GAAP measures to the most directly comparable GAAP measures
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Datadog non-GAAP operating margin % Investing for growth Revenue growth outstrips investment growth (1) Non-GAAP measures. See Appendix for a reconciliation of these non-GAAP measures to the most directly comparable GAAP measures
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Strong financial performance Revenue Non-GAAP operating profit and margin % (1) Datadog free cash flow and margin % (1) 42% CAGR % Operating margin 11% 16% 19% 23% 25% 22% % Free cash flow margin 14% 24% 21% 28% 29% 27% 65% CAGR 62% CAGR (1) Non-GAAP measures. See Appendix for a reconciliation of these non-GAAP measures to the most directly comparable GAAP measures
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Financial goals This section contains various forward-looking statements regarding financial goals. See Safe Harbor for important information regarding forward-looking statements
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Non-GAAP % (1) 2020 2021 2022 2023 2024 2025 Long-term goal Gross Margin 79% 78% 80% 82% 82% 81% R&D 29% 30% 30% 30% 28% 30% S&M 31% 25% 25% 24% 24% 23% G&A 8% 7% 6% 6% 5% 5% Operating Margin 11% 16% 19% 23% 25% 22% 25%+ Free Cash Flow Margin 14% 24% 21% 28% 29% 27% Long-term margins vs. goals (1) Non-GAAP measures. See Appendix for a reconciliation of these non-GAAP measures to the most directly comparable GAAP measures
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Capital allocation goals Ensure our leadership has flexibility and capacity to invest Generate healthy amounts of FCF Maintain our thoughtful and disciplined acquisition strategy
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Share dilution Target net dilution related to RSUs/PSUs awarded (1) (1) Defined as % of weighted average shares outstanding granted as equity awards (options, RSUs, PSUs, etc.) during the period, net of forfeitures and cancellations. 2.5-3.0%
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Appendix
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Non-GAAP financial measures and other information The statistical data, estimates and forecasts referenced in this presentation and the accompanying oral presentation are based on independent industry publications or other publicly available information, as well as information based on our internal sources. While we believe the industry and market data included in this presentation and the accompanying oral presentation are reliable and are based on reasonable assumptions, these data involve many assumptions and limitations, and you are cautioned not to give undue weight to these estimates. We have not independently verified the accuracy or completeness of the data contained in these industry publications and other publicly available information. We define the number of customers as the number of accounts with a unique account identifier for which we have an active subscription in the period indicated. Users of our free trials or tier are not included in our customer count. A single organization with multiple divisions, segments or subsidiaries is generally counted as a single customer. However, in some cases where they have separate billing terms, we may count separate divisions, segments or subsidiaries as multiple customers. Customers as of December 31, 2022 exclude customers from a then-recent acquisition, which did not contribute meaningful revenue during the fiscal year. Other terms such as annual recurring revenue or ARR and dollar-based net revenue retention rate shall have the meanings set forth in our Annual Report on Form 10-K. Dollar-based gross retention rate is calculated by first calculating the point-in-time gross retention as the previous year ARR minus ARR attrition over the last 12 months, divided by the previous year ARR. The ARR attrition for each month is calculated by identifying any customer that has changed their account type to a “free tier,” requested a downgrade through customer support or sent a formal termination notice to us during that month, and aggregating the dollars of ARR generated by each such customer in the prior month. We then calculate the dollar-based gross retention rate as the weighted average of the trailing 12-month point-in-time gross retention rates. We believe dollar-based gross retention rate demonstrates the stickiness of the product category we operate in, and of our platform in particular.
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Non-GAAP financial measures and other information Datadog discloses the following non-GAAP financial measures in this presentation and the accompanying oral presentation: non-GAAP gross profit, non-GAAP gross margin, non-GAAP operating expenses (sales and marketing, research and development, general and administrative), non-GAAP operating income (loss), non-GAAP operating margin, non-GAAP net income (loss), non-GAAP net income (loss) per diluted share, non-GAAP net income (loss) per basic share, free cash flow and free cash flow margin. Datadog uses each of these non-GAAP financial measures internally to understand and compare operating results across accounting periods, for internal budgeting and forecasting purposes, for short- and long-term operating plans, and to evaluate Datadog’s financial performance. Datadog believes they are useful to investors, as a supplement to GAAP measures, in evaluating its operational performance, as further discussed below. Datadog’s non-GAAP financial measures may not provide information that is directly comparable to that provided by other companies in its industry, as other companies in its industry may calculate non-GAAP financial results differently, particularly related to non-recurring and unusual items. In addition, there are limitations in using non-GAAP financial measures because the non-GAAP financial measures are not prepared in accordance with GAAP and may be different from non-GAAP financial measures used by other companies and exclude expenses that may have a material impact on Datadog’s reported financial results. Non-GAAP financial measures should not be considered in isolation from, or as a substitute for, financial information prepared in accordance with GAAP . A reconciliation of the historical non-GAAP financial measures to their most directly comparable GAAP measures has been provided in this Appendix. Datadog defines non-GAAP gross profit, non-GAAP gross margin, non-GAAP operating expenses (sales and marketing, research and development, general and administrative), non-GAAP operating income (loss), non-GAAP operating margin and non-GAAP net income (loss) as the respective GAAP balances, adjusted for, as applicable: (1) stock-based compensation expense; (2) the amortization of acquired intangibles; (3) employer payroll taxes on employee stock transactions; (4) M&A transaction costs; (5) amortization of issuance costs; and (6) an assumed provision for income taxes based on our long-term projected tax rate. Non-GAAP financial measures prior to April 1, 2025 have not been adjusted for M&A transaction costs, as such costs were not material to our results of operations in such prior periods. Our estimated long-term projected tax rate is subject to change for a variety of reasons, including the rapidly evolving global tax environment, significant changes in Datadog's geographic earnings mix, or other changes to our strategy or business operations. We will re-evaluate our long-term projected tax rate as appropriate. Datadog defines free cash flow as net cash provided by operating activities, minus capital expenditures and minus capitalized software development costs, if any. Investors are encouraged to review the reconciliation of these historical non-GAAP financial measures to their most directly comparable GAAP financial measures. Datadog has not reconciled its expectations as to non-GAAP margins to their most directly comparable GAAP measure as a result of uncertainty regarding, and the potential variability of, reconciling items such as stock-based compensation and employer payroll taxes on equity incentive plans. Accordingly, reconciliation is not available without unreasonable effort, although it is important to note that these factors could be material to Datadog’s results computed in accordance with GAAP .
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GAAP to Non-GAAP reconciliation Gross profit margin ($000’s, annual)
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GAAP to Non-GAAP reconciliation Operating expenses and operating profit ($000’s, annual)
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GAAP to Non-GAAP reconciliation Gross profit margin, operating expenses and operating profit ($000’s, quarterly)
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GAAP to Non-GAAP reconciliation Gross profit margin, operating expenses and operating profit ($000’s, quarterly)
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Free cash flow bridge Free cash flow ($000’s)