#1Datadog Investor Presentation February 2023 DATADOG#2Safe Harbor 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, growth in and ability to capitalize on long-term market opportunities, gross margins and operating margins including with respect to sales and marketing, research and development expenses, investments and capital expenditures, and Datadog's future financial performance, including its guided revenue on the slide "Rapid Revenue Growth" and the information on the slides entitled "Long term growth drivers are still in early stages, " "At our core, Observability is a very large opportunity," and "Financial Outlook". 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 business depends on our 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 and (12) general market, political, economic, and business conditions including concerns about reduced economic growth and associated decreases in information technology spending as well as the impact that the ongoing COVID-19 pandemic. 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 Annual Report on Form 10-K for the year ended December 31, 2022. 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 of this press release. We disclaim any obligation to update forward-looking statements. DATADOG N#3Datadog is the monitoring and security platform for cloud applications#4A Amazon Machine Learning ElastiCache openstack Good Datadog complexity Tradis System EFS Amazon CodeBuild FirebasCloud Dataflowe D Amazon Cloud Trail ROOKOUT Microsoft Event Viewer Microsoft Azure HTTP CHECK Container Engine Microsoft Google MySQL IMMUNIO Microsoft SQL Server Gangita Active MQ ORACLE Mac OS X REDMINE Sidekiq flowdock mongoDB. Atlas ANSIBLE Google Compute Engine upstart Jenkins elasticsearch Azure Automation LOUDFLARE Odebian split New Relic Elastic Block Store EBS portworx aqua Google Cloud SQL hadoop HOFS Amazon Buddy Elastic Beanstalk Twistlock Neutrona Neutral het. Knyshare. Amazon Kinesis -GO >Ssh CloudHealth php TECHNOLOGIES fluentd bonsai Up git bugsnag Cloud Checkr Azure Load Balancer Hyper-V Gearman php fpm fpm VIV CACHED CRI osoft Azure r PostgreSQL MNotification Hubs * Microsoft Azure IOT Hub Virtual Machine Scale Set Amazon S3 > Java Couchbase Microsoft Queue St STORM C Capistrano ‒‒‒ Amazon Redshift Adobe Experience >mparticle Manager Azure C PUSHER php pagerdutya dh Google catchpoint Cloud lo LIGHTTPD fly light. Microsoft Azure Microsoft Azure Container Instance Google Event Hub Cloud APIs O Amazon ALB Amazon OpsWorks chatwork dockerriak Mark Logic Amaz Microsoft Azure Batch Service CISCO APPLICATION CENTRID INFRASTRUCTURACH etcd SERVERLESS VITTWae vSphere Dy SAAS Ruby EMR pingdom mongoDB www www OpsGenie A S Amazon Lambda Segment journald Microsoft Azure Table Storage AEROSPINE PIVOTAL TRACKER amazon web services RED HAT OPENSHIFT CONVOX + amazon Twebservices Health T SECURITY Azure Stream Analytics Apache Solr Billing HARDWARE Google Cloud Interconnect neo4j fastly fedora rSYSLOG Teams DATABASES MESOS Apache Zookeeper Nomad CONTAINERS Google Cloud Dataproc 600 Amazon Microsoft Azure Google SES App Service Environment Cloud Memorystore for Rodis 0 Azure Search NOTIFICATIONS NETWORK OSTATSD MARATHON RIGOR A O StatusPage.io DNS SYSTEM TRE 505 Groups DEPLOYMENT Amazon IOT APPS Google +Cloud TPU CONSUL Cassandra 449 python Ов NEMX Nagios WdFly G JBoss yes Hot Amazon SOS Google Cloud Pub/SuGremlin VictorOps salesforce Amazon Amazon DynamoDB DocumentDB Google Amazon CodeDeploy slack Azure Event Grid NETWORKS Phadoop IN PREDUCE CLOUDS Launch Darkly Apache Ambari Google Cloud Platform papertrail DEV TOOLS CODE desk splunk> DIRECTORY Signal Sciences WORKFLOW RBL TRACKER OPENMETRICS Microsoft Azure VM PLATFORMS træfik crosoft Azure obile AppMicrosoft Azure Logic App T Google Cloud Bigtable on Ho CONFIGURATION Things hadoop WARN Akamai websphere Application Server cri-o CouchDB Amazon Elastic Load Balancing ELB AD zure nagement Amazon ECS Amazon Polly Rollbar £ TLS TBM DB2 oOo nextcloud CLOUDFOUNDRY Amazon Route 53 Microsoft Event Viewer Google auts Chat Azure 55 Key Vault zendesk Windows Server 2012 GitHub Cockroach DB & To TeamCity NTP Anache Kafka IBM MQ Microsoft Azure Blob Storage gunicorn 01 Tit=ida[o]a OpenLDAP Apache Zookeeper Azure Service Bus Relay Pivotal Container Service Azure Vault Microsoft Azure App Services logz.io TCP Application Gateway ERLANG Mitrusoll Azure Service Bus Amazon Cognitive Services Auto Scaling Spark Azure Amazon SWF Azure HDInsight →04 Core OS Azure Data Factory kubernetes Amazon CloudSearch NFSSTAT TCP RTT CHECK Google Cloud Run Toku MX syslog-ng Amazon Google CloudHSM Cloud Firestore SQL Microsoft Azure SQL Elastic Pool Azure Data Lake Store AmazonCloud Composer Storage Gateway Amazon VPC My Azura Microsoft Azure Customer Insights DB for MySQL AWS Fargate Amazon X Ray PDH CHECK Google Cloud Tasks SUPERVISORD PostgreSQL Amazon Google Cloud Virtual Network EC2 puppet Amazon Linux Amazon API Gateway container Azure presto. KONG Windows Microsoft Azure SQL DB SQL G Amazon Kinesis Data Firehose K Amazon EKS moxtra SUSE twemproxy APOLLO POWERDNS::: ENGINE Sentry ceph (x) matters 8 SNMP btrfs Windows Management CentOS Windows Services Instrumentatio ERISIGN RabbitMQ logstash ubuntu circlecl 30 Amazon Google Key Management Servi Google Amazon loud Machine Learning SQS VARNISH CoreDNS servicenowcontainer Service CACHF Virtual Kubelet Azure Cosmos DB Google Cloud Rou Honeybadger PROCES Prometheus Amazon Amazon MQ Aurora Azure Analysis Servit Azure Data Lake Analytics 49 000 Microsoft Azur Document DI Amazon WAF Apac Alibaba Cloud E Exc Google App Engine Linux Azure CHE sumolo IX N HAPR POSTFIX DISK Ding To webhooks HE 4 1 vns#5Evolving technology paradigms create rising complexity Diversity of technologies in use Number of technologies and tools Frequency Standardized/On-prem Frequency of releases Few vendor suites Waterfall DATADOG Once a year X Once a day Lots of open source and SaaS Time Diverse/Cloud On-demand Time Agile X X Scale in number of computing units Number of nodes Static People Number of people involved Physical hardware Siloed Ops Cloud instances X Dev + Ops Serverless & microservices Containers Business + Dev + Ops Time Dynamic Security + Dev + Ops + Business Time Integrated LO#6Datadog breaks down silos Unified platform Simple but not simplistic DATADOG Deployed everywhere, used by everyone Breaking down silos#7Our history of innovation Founded Datadog to break down silos Real-time Unified Data Platform 2010 2011 Infrastructure Monitoring (2012) DATADOG 2012 2013 2014 2015 2016 2017 Log Management (2018) Application Performance Monitoring (2017) 2018 Cloud Security Platform (2020) Digital Experience Monitoring (2019) 2019 Developer Experience (2021) 2020 2021 2022 One product One platform Used by everyone Deployed everywhere 7#8Our history of innovation Deployed everywhere, used by everyone Founded Datadog to break down silos Real-Time Unified Data Platform 2010 2011 DATADOG Infrastructure Monitoring Hosts / Clouds / VMs / Containers / Processes / lot 2012 2013 2014 2015 2016 APM Serverless Monitoring Metric Management Correlations Log Logging without Limits™ Distributed Watchdog Tracing Alerts 2017 2018 Continuous Profiler Network Performance Monitoring 2019 Sensitive Data Scanner Real User Monitoring Synthetic Error Monitoring Tracking Cloud SIEM Mobile RUM 2020 Session Replay Network Device Monitoring Cloud Deployment Security Tracking Posture Incident Management Cloud Workload Security Application Security Management Database Monitoring CI Visibility Watchdog Insights 2021 Watchdog Log Anomaly Detection Watchdog Root Cause Analysis Datadog Audit Trail Observability Pipelines Management Cloud Service Catalog Continuous Testing Cost Management Cloud Security Management SNMP Traps Universal Service Monitoring 2022 Generally available products only. 8#9The Datadog platform Infrastructure Monitoring Containers fx Serverless Network Performance Monitoring Network Device Monitoring Cloud Cost Management DATADOG Application Performance Monitoring Distributed Tracing Error Tracking </Continuous Profiler Database Monitoring Universal Service Monitoring 糕 Digital Experience Log Monitoring Synthetics Real User Monitoring Session Replay Management Observability Pipelines Sensitive Data Scanner Audit Trails Log Forwarding Security UNIFIED METRICS, LOGS, TRACES Cloud Security Management Application Security Management Cloud SIEM 600+ INTEGRATIONS Developer Experience Watchdog Al Insights Impact Analysis Root Cause Analysis Anomaly Detection Alerts Correlation Optimizations SCI Visibility Continuous Testing Shared Platform Services Collaboration Dashboards Mobile ● Agents • Notebook • Workflows ● Open Telemetry Service Catalog 9 9#10Long-term growth drivers are still in early stages DATADOG DOG Cloud spend continues to grow rapidly Cloud Spend ($B's) $1,000 B $900 B $800 B $700 B $600 B $500 B $400 B $300 B $200 B $100 B $0 B 2014 2015 Cloud Spend as % of Total IT Spend 2016 2017 2018 2019 2020 2021 Cloud Spend ($B's) 2022E 2023E 2024E 2025E 2026E 20% 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% Cloud Spend as % of Global IT spend 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, 3Q22 Update. Gartner Market Databook - 4Q12 Update; 4Q13 Update; 4Q14 Update; 4Q15 Update; 4Q16 Update; 4Q17 Update; 4Q18 Update; 4Q19 Update; 4Q20 Update; 4Q21 Update; 3Q22 Update. 10#11At our core, Observability is a very large opportunity $62B in 2026 Gartner Forecast: Enterprise Infrastructure Software, Worldwide, 2020-2026, 2Q22 Update. Published June, 2022. IT Operations Market. DATADOG DOG Datadog Observability TAM ($B's) $41B 2022 $45B 2023 $51B 2024 $56B 2025 $62B 2026 11#12INDUSTRY RECOGNITION We were named a Leader in the 2022 Gartner Magic Quadrant for Application Performance Monitoring and Observability DATADOG Figure 1: Magic Quadrant for Application Performance Monitoring and Observability ABILITY TO EXECUTE ManageEngine CHALLENGERS Amazon Web Services Cisco (AppDynamics) Riverbed (Aternity) SolarWinds NICHE PLAYERS Broadcom COMPLETENESS OF VISION Source: Gartner (June 2022) Microsoft Sumo Logic Alibaba Cloud Oracle Honeycomb VMware (TO) Logz.io LEADERS New Relic IBM (Instana) Splunk Elastic VISIONARIES As of June 2022 Datadog Dynatrace © Gartner, Inc This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from DataDog. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. 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. Gartner, Magic Quadrant for Application Performance Monitoring, Padraig Byrne, Gregg Siegfried, Mrudula Bangera, June 7, 2022.#13We are a Leader in the Forrester Wave™ Artificial Intelligence for IT Operations, Q4 2022 Ranked highest for: - Product vision - Market presence - Sensory/telemetry collection and retention - Data insights and visualizations DATADOG Challengers Stronger current offering Weaker current offering Contenders Weaker strategy Micro Focus Strong Performers LogicMonitor O Elastic Splunk ( New Relic OpsRamp Digitate O Market presence Dynatrace Leaders Datadog ScienceLogic -Zenoss Stronger strategy The Forrester Wave™ is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave are trademarks of Forrester Research, Inc. The Forrester Wave is a graphical representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgement at the time and are subject to change#14Why Datadog for cloud security DATADOG DOG Break down silos between DevOps Land Security oct гос CO |000 Datadog has the richest, deepest data No additional friction or performance penalty to instrument, no data tax 14#15Datadog Security products Observability DATADOG ADOG Infrastructure Monitoring Application Performance Monitoring Log Management Co Security Cloud Security Management Application Security Management Cloud SIEM 15#16Financial overview#17Datadog today (1) FINANCIAL $1.68B +63% FY22 revenue FY22 Y/Y growth 19% FY22 non-GAAP operating margin 21% FY22 free cash flow margin 130%+ Dollar-based net retention rate $1.9B Cash, cash equivalents, restricted cash, and marketable securities CUSTOMERS ~23,200 Total ~2,780 $100K+ ARR PLATFORM ADOPTION 81% Customers using 2+ products 42% Customers using 4+ products 18% Customers using 6+ products 17 PRODUCTS IN AN INTEGRATED DATA PLATFORM Infrastructure Monitoring APM Real User Monitoring Log Management Network Monitoring Database Monitoring Incident Management CI Visibility Cloud Security Management Synthetic Monitoring Continuous Profiler Observability Pipelines Cloud SIEM Application Security Management Universal Service Monitoring PEOPLE ~4,800 at 32 locations (2) Sensitive Data Scanner Cloud Cost Management We were named a Leader in the 2022 Gartner Magic Quadrant for Application Performance Monitoring (1) All data as of Dec 31, 2022. Non-GAAP operating margin free cash flow margin are non-GAAP measures. See Appendix for a reconciliation to financial results prepared in accordance with GAAP. (2) Employee headcount as of December 31, 2022. DATADOG Gartner 17#18Our growth drivers DATADOG DOG 1 Secular tailwind of digital transformation and cloud migration 2 3 Increased penetration of cloud and next-gen DevOps customers Expanding products / use cases for customers 4 Adding new markets beyond observability 18#19Rapid revenue growth Annual revenue $M's 101 198 66% CAGR 363 603 1,029 Guided (¹) 2,070-2,090 1,675 Quarterly revenue $M's 70 83 96 114 131 140 155 178 199 234 270 326 363 406 FY17 FY18 FY19 FY20 FY21 FY22 FY23 (1) Guided revenues are forward-looking statements and speak as of February 16, 2023. See Safe Harbor for important information about these assumptions and forward-looking statements. DATADOG 437 Guided (¹) 466-470 469 1Q19 2019 3Q19 4019 1020 2020 3Q20 4Q20 1021 2021 3Q21 4021 1022 2022 3Q22 4Q22 1023 19#20Strong customer growth Total customers 5,403 FY17 DATADOG 7,676 FY18 10,536 FY19 ~14,200 FY20 ~18,800 FY21 ~23,200 FY22 20#21Strong customer growth # of customers with ARR $1M+ 11 FY17 27 FY18 DATADOG 54 FY19 101 FY20 216 FY21 317 FY22 # of customers with ARR $100K+ % of total ARR 76% 78% 60% 236 FY17 69% 438 FY18 837 FY19 1,228 FY20 83% ~2,010 FY21 85% ~2,780 FY22 21#22Platform strategy is resonating with customers % of customers using 2+ products 72% 75% 75% 77% 78% 81% DATADOG 79% 80% 81% 4020 1021 2021 3Q21 4021 1022 2022 3Q22 4Q22 % of customers using 4+ products 22% 25% 28% 31% 33% 35% 37% 40% 42% 4020 1021 2021 3021 4021 1022 2022 3Q22 4Q22 % of customers using 6+ products 16% 14% 12% ÏÏÏ 4020 1021 2021 3Q21 4021 1022 2022 3022 4Q22 3% 4% 6% 8% 18% 10% 22#23Strong retention and upsell DATADOG Mid-High 90%S Dollar-based gross retention rate 130%+ Dollar-based net retention rate 23#24Financial summary Revenue % Y/Y growth Gross margin(¹) Research & development margin(¹) Sales & marketing margin(¹) General & administrative margin(¹) Operating margin(¹) Free cash flow margin(¹) Free cash flow (1) FY17 $101M 109% 77% DATADOG 23% 43% 10% 1% 6% $6M FY18 FY19 $198M $363M 97% 83% 77% 76% 27% 44% 9% (3)% (3)% $(5)M 29% 39% 9% (1)% 0% $1M FY20 FY21 $603M $1,029M 66% 79% 29% 31% 8% 11% 14% $83M (1) Non-GAAP measures. See Appendix for a reconciliation of these non-GAAP measures to the most directly comparable GAAP measures. 70% 78% 30% 25% 7% 16% 24% $251M FY22 $1,675M 63% 80% 30% 25% 6% 19% 21% $354M 24#25Financial outlook (as of February 16, 2023)(¹) Revenue Non-GAAP operating income (2) Non-GAAP EPS(2) Weighted average diluted shares (1) (2) 1Q23 DATADOG FY23 $466-470M $2,070-2,090M $68-72M $300-320M $0.22-0.24 $1.02-1.09 Approx. 348M Approx. 351M Financial outlook are forward-looking statements. See Safe Harbor for important information about these assumptions and forward-looking statements. Non-GAAP measures. See Appendix for a reconciliation of these non-GAAP measures to the most directly comparable GAAP measures. 25#26Appendix#27Non-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 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. Customers as of December 31, 2022 exclude customers from a recent acquisition, which did not contribute meaningful revenue during the fiscal year. 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. 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. 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. Non-GAAP Financial Measures 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) non-cash benefit related to tax adjustment; (4) employer payroll taxes on employee stock transactions; and (5) amortization of issuance costs. Datadog defines free cash flow as net cash provided by operating activities, minus capital expenditures and minus capitalized software development costs. Investors are encouraged to review the reconciliation of these historical non-GAAP financial measures to their most directly comparable GAAP financial measures. DATADOG 27#28GAAP to Non-GAAP reconciliation Gross profit margin ($000's) Revenue GAAP gross profit GAAP gross margin Add: Share-based compensation expense included in cost of revenue Amortization of acquired intangibles Employer payroll taxes on employee stock transactions Non-GAAP gross profit Non-GAAP gross margin DATADOG FY17 $100,761 $77,347 77% 112 484 $77,943 77% FY18 FY19 $198,077 $362,780 $151,548 $273,831 77% 75% 287 511 - $152,346 77% 582 752 $275,165 76% FY20 FY21 FY22 $603,466 $1,028,784 $1,675,100 $473,269 $794,539 $1,328,357 78% 77% 79 % 1,794 943 187 $476,193 79 % 4,565 3,792 345 $803,241 78% 10,827 6,750 266 $1,346,200 80 % 28#29GAAP to Non-GAAP reconciliation Operating expenses and operating profit ($000's) Revenue RESEARCH & DEVELOPMENT GAAP R&D expense GAAP R&D expense as a % of revenue Less: Share-based compensation expense Less: Employer payroll taxes on employee stock transactions Add: Other Non-GAAP adj. (¹) Non-GAAP R&D expense Non-GAAP R&D expense as a % of revenue SALES & MARKETING GAAP S&M expense GAAP S&M expense as a % of revenue Less: Share-based compensation expense Less: Amortization of acquired intangibles Less: Employer payroll taxes on employee stock transactions Add: Other Non-GAAP adj. (¹) Non-GAAP S&M expense Non-GAAP S&M expense as a % of revenue GENERAL & ADMINISTRATIVE GAAP G&A expense GAAP G&A expense as a % of revenue Less: Share-based compensation expense Less: Employer payroll taxes on employee stock transactions Add: Other Non-GAAP adj. (¹) Non-GAAP G&A expense Non-GAAP G&A expense as a % of revenue Reconciliation of operating loss and operating margin GAAP operating loss Add: Stock-based compensation expense Add: Amortization of acquired intangibles Add: Employer payroll taxes on employee stock transactions Less: Other Non-GAAP adj. (¹) Non-GAAP operating income (loss) GAAP operating margin Non-GAAP operating margin (1) Non-cash benefit related to the release of a non-income tax liability DATADOG FY17 $100,761 $24,734 25% 1,160 $23,574 23% $44,213 44% 977 $43,236 43% $11,356 11% 819 $10,537 10% $(2,956) 3,068 484 $596 (3)% 1% FY18 $198,077 $55,176 28% 1,641 $53,535 27% $88,849 45 % 1,910 $86,939 44% $18,556 9% 1,406 $17,150 9% $(11,033) 5,244 511 $(5,278) (6)% (3)% FY19 $362,780 $111,425 31% 7,972 1,157 (2,344) $104,640 29% $146,657 40% 5,538 284 (397) $141,232 39 % $35,889 10% 4,942 19 (2,266) $33,194 9% $(20,140) 19,034 752 1,460 (5,007) $(3,901) (6)% (1)% FY20 $603,466 $210,626 35% 38,008 2,836 (2,729) $172,511 29% $213,660 35% 20,467 3,756 (449) $189,886 31% $62,756 10% 14,105 839 (2,383) $50,195 8% $(13,773) 74,374 943 7,618 (5,561) $63,601 (2)% 11 % FY21 $1,028,784 $419,769 41% 101,942 8,143 $309,684 30 % $299,497 29% 35,035 600 6,349 $257,513 25% $94,429 9% 22,195 1,248 $70,986 7% $(19,156) 163,737 4,392 16,085 $165,058 (2)% 16% FY22 $1,675,100 $752,351 45% 237,120 10,384 $504,847 30% $495,288 30 % 76,735 825 2,766 $414,962 25% $139,413 8% 38,472 830 $100,111 6% $(58,695) 363,154 7,575 14,246 $326,280 (4)% 19% 29#30Free Cash Flow bridge Free Cash Flow ($000's) Revenue Cash flow from operations Capex Capitalized software developmental costs Free cash flow Free cash flow margin DATADOG FY17 FY18 FY20 $198,077 $362,780 $603,466 $1,028,784 $1,675,100 $13,832 $10,829 $24,234 $109,091 $286,545 $418,407 (9,662) (13,315) (5,415) (35,261) (6,176) (10,128) (20,468) $100,761 (2,351) (5,452) $6,029 $(5,009) 6% (3)% FY19 $791 $83,208 0% 14 % FY21 (9,956) (26,069) FY22 24 % (29,628) $250,520 $353,518 21% 30