#1Datadog Investor Meeting October 27, 2021 DATADOG 1#2Safe Harbor This presentation and accompanying oral presentation contain "forward-looking statements" based on our beliefs and assumptions formed from available information, including statements concerning our strategy and objectives, future operations, operating model, financial and competitive position, industry environment, potential growth and market opportunities, and customer trends. Forward-looking statements include all statements that are not historical facts and can but may not always be identified by terms such as "anticipates," "believes," "could," "seeks," "estimates," "targets," "guidance," "expects," "intends," "may," "plans," "potential," "predicts," "prospects," "projects," "should," "will," "would" or similar expressions and the negatives of those terms. By their nature, these statements are subject to numerous uncertainties and risks, including factors beyond our control, that could cause actual results, performance or achievement to differ materially and adversely from those anticipated or implied in the statements. Please refer to our Quarterly Report on Form 10-Q for the quarter ended June 30, 2021 filed with the SEC on August 6, 2021, and future SEC filings, for a discussion of these risks and uncertainties, which include, among others, unfavorable market conditions or reductions in information technology spending, a decline in new customers, renewals or expansions, operating in competitive markets, failure to effectively develop and expand our sales and marketing strategy, failure to adapt and respond effectively to rapidly changing technology, evolving industry standards, changing regulations, changing customer needs, requirements or preferences, and identifying and successfully integrating strategic investments. It is not possible for us 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 or outcomes to differ materially from those contained in any forward-looking statements. You should not rely upon forward-looking statements as predictions of future events. Although our management believes that the expectations reflected in our statements are reasonable, we cannot guarantee that the events and circumstances described will be achieved or occur. Moreover, neither we, nor any other person, assumes responsibility for the accuracy and completeness of these statements. Recipients are cautioned not to place undue reliance on these forward-looking statements, which speak only as of the date such statements are made and should not be construed as statements of fact. Except to the extent required by federal securities laws, we undertake no obligation to update any information or any forward-looking statements as a result of new information, subsequent events, or any other circumstances after the date hereof, or to reflect the occurrence of unanticipated events. This presentation and the accompanying oral presentation may also contain estimates and other statistical data made by independent parties and by us relating to market size and growth and other data about our industry. This data involves a number of assumptions and limitations, and you are cautioned not to give undue weight to such estimates. In addition, projections, assumptions, and estimates of our future performance and the future performance of the markets in which we compete are necessarily subject to a high degree of uncertainty and risk. Terms such as "ARR," "Net Retention Rate" and "Gross Retention Rate" shall have the meanings set forth in our SEC filings; provided, however, we updated the definition of MRR as of the quarter ended September 30, 2021 to capture usage from subscriptions with committed contractual amounts and applied this change retroactively. 33 This presentation also includes non-GAAP operating income. Non-GAAP financial measures have limitations as analytical tools and you should not consider them in isolation or as a substitute for or superior to their most directly comparable financial measures prepared in accordance with GAAP. There are a number of limitations related to the use of non-GAAP financial measures versus their nearest GAAP equivalents. Other companies, including companies in our industry, may calculate non-GAAP financial measures differently or may use other measures to evaluate their performance, all of which could reduce the usefulness of our non-GAAP financial measures as a tool for comparison. We urge you to review the reconciliation of Datadog's non-GAAP financial measures to the most directly comparable GAAP financial measures, and not to rely on any single financial measure to evaluate our business. See the Appendix for a reconciliation between non-GAAP operating income and revenue. The information in this presentation on new products, features or functionality is intended to outline our general product direction and should not be relied upon in making a purchasing decision and shall not be incorporated into any contract. Such information is not a commitment, promise or legal obligation to deliver any code or functionality. The development, release and timing of any features or functionality described for our products remains at our sole discretion. DATADOG 2#3Agenda Industry drivers and our opportunity Datadog design choices and differentiators Infrastructure Monitoring, our platform, and Datadog for developers APM and Log Management Cloud Security Platform Break (~15 minutes) Customer focus and pricing philosophy Go-to-market Financial takeaways Q&A session Olivier Pomel CEO & Co-founder Alexis Lê-Quốc CTO & Co-founder Ilan Rabinovitch SVP, Product & Community Renaud Boutet SVP, Product Management Pierre Betouin VP, Product Management, Sqreen Co-founder Amit Agarwal Chief Product Officer Adam Blitzer coo David Obstler CFO Olivier Pomel CEO & Co-founder Alexis Lê-Quốc CTO & Co-founder David Obstler CFO Yuka Broderick Head of Investor Relations 3#4Olivier Pomel Co-Founder & CEO#5Dash Announcements PRODUCT CI Visibility Session Replay Funnel Analysis Network Device Monitoring Datadog Apps DATADOG STATUS General Availability General Availability General Availability General Availability General Availability PRODUCT Online Archives Observability Pipelines Application Security Universal Service Monitoring Cloud Cost Management STATUS Limited Availability Private Beta Private Beta Private Beta Private Beta#6What's happening today in IT DATADOG#7Two broad and deep transitions DATADOG Digital Transformation X Cloud Migration 7#8An explosion of 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 Scale in number of computing units Number of nodes Static People Physical hardware Number of people involved X Siloed Ops Cloud instances Dev + Ops Serverless & microservices Containers Business + Dev + Ops Time Dynamic Security + Dev + Ops + Business Time Integrated ∞#9DATADOG Why Datadog? 9#10Datadog solves complexity redis System CloudTrail ROOKOUT Microsoft Event Viewer Microsoft Azure IMMUNIO Container Engine HTTP CHECK Google MySQL Microsoft Microsoft SQL Server Ganglia Active MQ Mac OS X REDMINE Sidekiq Flowdock 9 Google Cloud SQL ORACLE Odebian ANSIBLE mongoDB. Atlas Google Compute Engine Cupstart Jenkins elasticsearch Azure Automatio LOUDFLARE split New Relic portworx aqua hadoop HOFS Amazon Buddy Elastic Beanstalk Neutrona Neutral. On Net. Anywhere. Amazon Kinesis Twistlock Adobe Experience Manager PUSHER =GO >Ssh CloudHealth php fpm bonsai Up fluentd Cloud Checkr git bugsnag CRI Hyper-V Gearman Azure Load Balancer MIMCACHED PostgreSQL php C Amazon 53 Microsoft Azure IOT Hub Amazon Redshift Couchbase Microsoft Queue St STORM C Capistrano MNotification Hubs Java Azure >mparticle ob 888 Google Cloud lo LIGHTTPD fly light. catchpoint™ Amazon ALB Microsoft Azure Microsoft Azure Container Instance Google Event Hub Cloud APIs 3 000 Amazon OpsWorks chatwork dockerriak MarkLogic Amaz Microsoft Azure Batch Service CISCO APPLICATION CENTRIC INFRASTRUCTURE (ACH) EMR etcd SERVERLESS vilware vSphere Dy SAAS pingdom OpsGenie A S Amazon Lambda Segment pagerdutyam Microsoft Azure Table Storage journald ME PIVOTAL TRACKER AEROSPIKE www Ruby CONVOX RED HAT OPENSHIFT amazon webservices + SECURITY Strear Analytics Apache Solr Billing HARDWARE Azure Google Cloud Interconnect neo4j fastly. fedora SYSLOG Teams DATABASES MESOS POL Apache Zookeeper Nomad CONTAINERS Google Cloud Dataproc Microsoft Azure Google App Service Environment Cloud Memorystore for Rodis NETWORK STATSD MARATHON DNS 588 Amazon SES Azure Search NOTIFICATIONS RIGOR CAN O StatusPage.io SYSTEM DEPLOYMENT Amazon IOT APPS wy Google Cloud TPU CONSUL Cassandra NGUX Nagios WildFly JBoss by Red Hat python Amazon SOS Google Cloud Pub/SuGremlin VictorOps salesforce Amazon Amazon DynamoDB DocumentDB Google Amazon CodeDeploy slack € Azure Event Grid NETWORKS hadoop MapReduce CLOUDS LaunchDarkly Apache Ambari Google Cloud Platform Amazon ECS DEV TOOLS papertrail CODE Besk splunk> DIRECTORY Signal Sciences WORKFLOW RBL TRACKER OPENMETRICS Microsoft Azure VM crosoft Azure obile AppMicrosoft Azure Logic App Google Cloud Bigtable PLATFORMS træfik Ho on CONFIGURATION Things hadoop Akamai AD Amazon Elastic Load Balancing ELB websphere Application Server cri-o CouchDB Amazon Polly zure nagement nextcloud TLS IBM DB2 Google (53 CLOUDFOUNDRY ino Microsoft Event Viewer Amazon. Route 53 outs Chat Azure Key Vault C₁ Windows Server 2012 GitHub Cockroach DB TO TeamCity IBM MQ Rollbar H zendesk NTP Apache Kafka Microsoft Azure Blob Storage Azure Service Bus Relay Apache Zookeeper Pivotal Container Service logz.io Vault TCP Azure Application Gateway ERLANG Microsoft Azure Service Bus Spark Core OS Amazon SWF Amazon Azure Cognitive Services Auto Scaling Azure Data Factory Azure HDinsight →8 Amazon CloudSearch kubernetes NFSSTAT TCP RTT CHECK Google Cloud Run Microsoft Azure App Services hadoop TokuMX OpenLDAP syslog-ng gunicorn Sentry ENGINE Amazon Google CloudHSM Cloud Firestore SQL Microsoft Azure SQL Elastic Pool Azure Data Lake Store AmazonCloud Composer Storage Gateway Amazon VPC AWS Fargate ||||| Google Cloud Tasks SUPERVISORD PDH CHECK Amazon X Ray Azure Microsoft Azure Azure ustomer insights DB for MySQL Analysis Servic PostgreSQL Amazon API Gateway puppet Amazon Linux Amazon Google Cloud Virtual Network EC2 containerd sQs Azure presto. KONG SQL Microsoft Azure SQL DB Amazon Kinesis Data Firehose K Amazon EKS moxtra SUSE twemproxy APOLLO POWERDNS::: ceph (x)matters Windows SNMP btrfs 8 Windows Centos Windows Services Instrumentatio ERISIGN RabbitMQ logstash ubuntu circleci My 41 Amazon Google Key Management Servi VARNISH CACHE CHE Google Amazonloud Machine Learning Virtual Kubelet CoreDNS servicenowcontainer Service PROCES Azure CosmosDB Cloud Rou Google Honeybadger Ⓒ. Prometheus Amazon Amazon MQ Aurora CHA Azure Data Lake Analytics R Linux Microsoft Azur Document D Amazon WAF Apac Alibaba Cloud E3 Exc Google App Engine Azure sumolo XN HAPR POSTFIX DISK Ding Ta Gearman php fpm#11Datadog breaks down silos Unified platform Simple but not simplistic DATADOG Deployed everywhere, used by everyone Breaking down silos 11#12Our 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 APM (2017) 2017 Logs (2018) 2018 Security Platform (2020) User Experience Monitoring (2019) 2019 2020 2021 2022 One product One platform Used by everyone Deployed everywhere 12#13Our history of innovation Real-Time Unified Data Platform 2010 2011 Infrastructure Monitoring Hosts / Clouds / VMs / Containers / Processes / lot DATADOG 2012 2013 2014 Founded Datadog to break down silos 2015 2016 APM Distributed Tracing 2017 Tracing without Log Management Limits™ Logging without Limits™ Serverless Monitoring Watchdog Alerts Network Performance Monitoring Continuous Profiler 2018 2019 Real User Monitoring Synthetic Error Monitoring Tracking Cloud SIEM Mobile RUM Session Replay Network Device Monitoring Cloud Security Posture Management Deployment Cloud Tracking Workload Security Incident Management Database Monitoring CI Visibility Watchdog Root Cause Analysis Watchdog Insights 2020 2021... Deployed everywhere, used by everyone 13#14DATADOG Our opportunity 14#15At our core, Observability is a very large opportunity $53B in 2025 Gartner Forecast: Enterprise Infrastructure Software, Worldwide, 2019-2025, 3Q21 Update, published September, 2021. DATADOG $38B 2021 $42B 2022 $45B 2023 $49B 2024 $53B 2025 15#16Our security opportunity DATADOG Developers Security DevSecOps Operations 16#17Future opportunities DATADOG Security Real time business intelligence Observability Developer workflows IT Service Management 17#18Alexis Lê-Quốc Co-Founder & CTO#19Public cloud revenue expansion Public cloud market revenue worldwide from 2012 to 2021E (in billion U.S. dollars) DATADOG Revenue in billion U.S. dollars 400 300 200 100 0 26.4 2012 38.6 2013 56.3 2014 75.3 2015 115 2016 154 205 Source: https://www.statista.com/statistics/477702/public-cloud-vendor-revenue-forecast/ 258 313 364 2017 2018E 2019E 2020E 2021E 19#20Increasingly complex software deployments App App Operating System Hardware App Traditional Deployment DATADOG → App App Bin/Library Operating System Virtual Machine App App Bin/Library Operating System Virtual Machine Hypervisor Operating System Hardware Virtualized Deployment → App App App Bin/Library Bin/Library Bin/Library Container Container Container App App App Bin/Library Bin/Library Bin/Library Container Container Container Container Runtime Operating System Hardware Container Deployment ← app1.func34 app1.func35 app1.func36 app1.func31 app1.func32 app1.func33 app1.func28 app1.func29 app1.func30 app1.func25 app1.func26 app1.func27 app1.func22 app1.func23 app1.func24 app1.func19 app1.func20 app1.func21 app1.func16 app1.func17 app1.func18 app1.func13 app1.func14 app1.func15 app1.func10 app1.func11 app1.func12 app1.func7 app1.func8 app1.func9 app1.func4 app1.func5 app1.func6 app1.func1 app1.func2 app1.func3 Function Runtime Operating System Hardware Hardware Serverless Architecture 20#21Increasingly numerous and ephemeral compute units DATADOG Scale in number of computing units Number of nodes Static Physical hardware Serverless & microservices Containers Cloud instances Time Dynamic 21#22Legacy tagging: labeled by unique identifier DATADOG M hu 09:00 09:05 09:10 Am 09:15 09:20 Overwhelming... 09:25 09:30 22#23Next-gen tagging: labeled by category or property Hosts - datacenterus1.staging DATADOG us-east-1d us-east-1c availability zone x Fill by: % CPU utilized avg . Size by us-east-1a un-east-ſe %CPU utilized 19 100 Tag by: Cloud Vendor Availability Zone Environment Service Version Custom Tags: Customer-defined 23#24Unified tagging Apps (click to see metrics) DATADOG aws chef consul goshe kafka ntp system Why use tags? Agent Datadog Agent: v5.7.1 System GNU/Linux - 2 CPU - 4 VCPU- 10.230.198.93 14.69GB - 93.56GB > Metrics (as of 2 mins ago) % CPU utilized 36% - More intuitive - Flexible and scales with hosts or containers Simplified searching and filtering Aggregate metrics on the fly Tags Chef #env:prod #kafka_cluster:cold #role:common-node #role:encrypted-storage #role:hauk-evaluator #role:monitoring-client AWS #account:prod #availability-zone:us-east-la #creator:caleb_datadoghq.com #image:ami-d7d165bc #instance-type:m3.xlarge #kernel:none #name:hauk-evaluator #region:us-east-1 #security-group-name prod-backend #security-group:sg-le51ef77 User Edit Tags Tags automatically added Why it matters: Tagging binds different data types in Datadog, allowing for correlation and calls-to-action among metrics, traces, and logs 24#25trols 1:54:5 1:54:5 1:54:5 1:54:5 1:54:5 1:54:5 1:54:5 1:54:5 1:54:5 1:54:5 1:54:5 1:54:5 1:54:5 1:54:5 1:54:5 7:54-5 GAN CEEP web-store rack.request Shopping CartController#checkout 2.67 s (100.0%) Q Search Analytics Y Retention Filter Metrics Logs (25) Errors (9) Processes Tags Infrastructure Q Trace ID trace_id:2923558922422553706 ↑ DATE SERVICE Feb 12 11:54:57.236 web-store 1-08bafa8285f6bce19 Headers:HTTP_VERSIO HTTP_HOST='rails-sto HTTP_ACCEPT_ENCODI HOST Feb 12 11:54:57.248 One-click pivot between metrics, traces, and logs Checking token for customer=YCLT212F5B7ZPREDX4XHYJAW with session=6NROVLNXTBA54ZKY11907DQ1 and capturing all current sessions the user. Feb 12 11:54:57.370 auth-dotnet i-0e57860b977b5fd67 Found session with token=EFXXRMFPCZXPJBTUXJFNQIJ8 Feb 12 11:54:58.343 auth-dotnet i-0e57860b977b5fd67 Found session with token=9Q7KJY370VPXZGG5V3FB50ZH Feb 12 11:54:58.368 auth-dotnet i-0e57860b977b5fd67 Network Code Hotspots BETA Found expired session with token=CX7CK66ZES6IF2A4RTQD68D9 Feb 12 11:54:58.372 auth-dotnet i-0e57860b977b5fd67 api.payment.c...#26Product innovation ape $ Pragmatism - Custom-develop vs. Open-source Build vs. Buy DATADOG Always building new - Learn new things, divert resources from established products, re-organize teams - Rebuild regularly - avoid a maintenance/legacy mentality 26#27Product innovation More More DATADOG 7 €) Incident Management Real User Monitoring Infrastructure Monitoring O Log Management Database Monitoring Synthetic Monitoring ja |.. APM Network Monitoring 0 Security Monitoring Workload Security fx Serverless 0 CSPM More + More 27#28Ilan Rabinovitch Senior Vice President, Product#29Hosts Filter by availability-zone * Copyright Datadog, Inc. 2021-35.5669838- Master Subscription Agreement - Privacy Policy - Cookie Policy - Datadog Status All Systems Operational Fill by: % CPU utilized ▾ avg Size by: Group northcentralus Metrics (group avg.) % CPU utilized % CPU utilized 0 50 84 hosts 31% * 100 0#30Infrastructure Monitoring extends across technologies Older Technologies IBM WebSphere AIX DATADOG 1 BEE aws Google Cloud A docker kubernetes Newer Technologies Serverless Function-as-a-Service IoT 30#31Infrastructure Monitoring as a starting point in observability Infrastructure Monitoring DATADOG - APM O Log Management O Network Monitoring Cloud Security 31#32Single product to a full platform Annual Recurring Revenue (ARR) Customers with 1 product DATADOG Customers with 4+ products Customers with 2-3 products Q2-18 Q2-19 Q2-20 Q2-21 32#33Platform-level capabilities: cloud-agnostic DATADOG aws PRIVATE CLOUD A Microsoft Azure ON-PREM Google Cloud ON-PREM Alibaba Cloud vmware® 33#34Platform-level capabilities: cloud-agnostic aws - Longest-standing partnership - Often first-to-market in launching monitoring products for new AWS products - Available on the AWS Marketplace DATADOG A Azure - Deep technical integration - Embedded as a first-party service in the Azure portal - Go-to-market partnership opportunities - Available on the Azure marketplace Google Cloud - Available on the GCP Marketplace Strong alliances and GTM partnership - - - PRIVATE CLOUD 450+ integrations Network Device Monitoring Observability Pipelines (Private beta) 34#35PRODUCT CUSTOMERS PRICING SOLUTIONS DOCS Join us at the Dash virtual conference! October 26-27 Azure More than 450 built-in integrations See across all your systems, apps, and services Azure App Service Plan DATADOG ALL ALERTING AUTODISCOVERY AUTOMATION AWS AZURE CACHING CLOUD COLLABORATION COMPLIANCE CONFIGURATION-DEPLOYMENT CONTAINERS COST-MANAGEMENT DATA-STORE DEVELOPER-TOOLS EVENT-MANAGEMENT EXCEPTIONS GOOGLE-CLOUD INCIDENTS IOT ISP ISSUE-TRACKING LANGUAGES LOG-COLLECTION MARKETPLACE MESSAGING METRICS MONITORING NETWORK NOTIFICATION ORACLE ORCHESTRATION OS-SYSTEM PROCESSING PROFILING PROVISIONING SECURITY SNMP Azure Active Directory Azure App Services SOURCE-CONTROL TESTING WEB ABOUT BLOG LOGIN Q Azure Analysis Service Azure Application Gateway GET STARTED FREE Azure API Management Azure Automation Azure App Service Environment Azure Batch Service 35#36Developers are moving towards CI/CD Build Test DATADOG Merge CI/CD benefits Deploy smaller code changes faster for: - Improved customer experience - Higher quality code Fewer unintended consequences Automatically release to repository Automatically deploy to production CI/CD challenges - Poor CI/CD pipeline implementation Poor coordination/communication among teams - Faulty tests 36#37CI Visibility - Observability for CI/CD من Development QA ...and bringing that level of visibility to your tests and pipelines in earlier stage environments DATADOG Shifting left Staging Production Ail Taking APM-like visibility historically only seen in production...#38Renaud Boutet Senior Vice President, Product#39APM: From distributed tracing... DATADOG TH 2 webserver-query | user.batch_query Sep 20 20:38 PM 717 ms POST /user/batch_query 200 OK 100 ms pylons.request string_query string_query.raw raw_query raw_query.ctxs T...raw_query.conte... postgres.qu... 200 ms SELECT key, 300 ms APM 2017 string_query string_query.raw ctx-pshard-standby | postgres.query /var/pgbouncer O 84.6 ms (11.8% of total time) 400 ms raw_query ctx-pshard-standby postgres.query query.ctxs Ⓒ/var/pgbouncer 84.6 ms (11.8% of total time) 468 ms ms raw_query.conte... postgre get_contexts_sub_query [[org:9543 query_id:e4675c5b33 batch:0]] WITH sub_contexts as ( po... 600 ms string_query string_query.raw raw_query 700 r raw_query.ctxs raw_query.cont... postgres... po... Service ctx-pshard-... user-query webserver-q... cache master-db indexer See trace % Total Time 52.5% 39.2% 7.3% 0.6% 0.3% 0.2% 39#40...To best-of-breed APM Suite E Services Request Flow Map Save As Search Facets Saved Views Q Search facets ✔Showing 138 of 138 COME Status ok Error Env prod staging Service Ins +Ext DI send-email-redis-queue Analytics ad-server-top Dsend-email-mysql-db. CPU-Cores Traces Max 26. auth-dotnet ad-server-graphol product-recommendati... 145 product-recommendati.. ad-server-http-client ne (cervera hande (p.pl 376 581 3.92k DATADOG TH €85 CPU-Cores for ACPU-Corm for hande one request basehttp run handler finish TH Compare: CPU Time A4046, B11- send p write CFU Time: 40 per os over all threads bootstrap threading <ifram bootstrap in threading py run threading py process request thread sock She sacketserver py Titer fomegraph by function or package 31 Aug 20, 5:00 pm-Aug 23, 8:00 pm Q Servicemail-appy X Version 5d39714X Enpred X 12:00 get resp Profiles wrapper Flow Map Q Hide Controls wrapper Flex Profile Comparison Shows A and Baggregated Flame Graph (works best when comparing similar words) 26.86 regis ta proces frkh WE hande hard Turch LOM send-email-m.... petr mer Enviprod x Service product-recommendation x Search by any tag on any span Showing 32 services from distributed traces matching this query: 0 email-api-py w 9.53 reg/s Au 21 AL CA shipping work... 04 errors 32.64 req/s wrappe wrapp Lea Ver wrappe _ca inne Show A&B Combined Side by Side NGAL e email-queue. envers 8.10 ms latency S5 req/s %error esecurel send-email-re.. 3.3 req/s Frame with CPU time per function (Ort » Scroll to zoom) 81%eriors 165.12 req/s ny Na trac nur firm anges) get execute(unseve p inne. (base py was handle(unerver pyl wa unserer CA run with reloader [autoreloaday) inne start jengo lautordaday NA run (autoreload ency nun Joop (autoripy) ticky) 11 errors 0.59 req/s modul(manage cute from command lineLink ay shopist-web-l ad-server-http errors Bey 11.05 req/s TA inne. loop fietsutor WORD snapshot les autoloaday MIX stipes (autoready) Man 13 ad-server af-server-top 39.6 ms latency 11.65 regis 14h en 18.42 ree/s glob(pap) select from pabay erat directories from th 154-P 14.63 g/ &18% 12:30 3.470/ ad-server-ma.. e shipping-que... 15 reg/s O Tue 24 Only in A lo.shopistos De eers <0.01 reg/ A brea/s of shipping-prod... il sa Homey 12.24 reg/s ad-server-redis 27 145 ms ency 6.37 reg/s B web-store 1.5% ras 0.6 req/s 12:30 Beetstraper A: 19.6% CFU Time: 14791ms of 404 process request thread ( இlைån requure; (kakatam.. fuckerserver. handled handle on outb run therdersgy) freshreporse han LIVE 100% of ingested traces 15m Past 15 Minutes 4d Aug 23, 8:00 pm-Aug 27, 2:00 pm Q Servicemail-appy X Version:60cx Eprod anders send preamb wite handler... loshopist and O errors 0.01 req/s auth-dotnet 6.87 req/s 43.39 reals Wel bootstrap (threading.py) Nocal/pythons rating o B used less CPU time on average than A -8.23% product-reco.... 47 te envers 6.23 regis product-reco 17,5% errors 15.5 ms latency 17.25 reg/s wapped (panchay B: 11.3% 410ms of 3.615 11 fames omitted traced get response (patchay) gat responsap Inner exception.py) 12:00 a deprecation pl innerleeption wachay) wapped (pichay cal deprecation pyl inerception) wrapper(trace utsay wapped (pachay call Ideprecation and payment-post... 168m latency 12.46 req/s THỦ ĐÔ of payments go 121 m Lency 6.23 reals es process reque. first reque hitlocks handle thaset handle one. rundunders Imagi wapper trase g Inner (ecep wrapper race.. wapped pat (depre inner (excep wrappersrace.. wapped parc 6.23 reg/s in 10 inventory-apr 218 a lasery 38 regis framesiled call (gley) wrapper (trace us traced et respons inner exceptienpyl wrapper traceu wrapped (patch pl ca(deprecate expl wrapper Drace, ul. wrapped (patch.pl ca(deprecate expl wrapper(trace L wrapped (patch.pl (deprecatio inner excepcienpyl On errors 175 ms Latency 2.52 regis 27 francmant executat pel freinarge.gy hade en s merver 0 < ( da Export More time in Orly in B 1700 Options start, djurgo (austurday) aucorudget Parday DK autoready leep fres Castoreload.p snapshot fles lastered) (pap) A A S 0 14. P រៀប Errors a Q Search Q Search facets cont prod ✓ Service ✔web-u ... shopist-web-ul Version 35.2831289 35.2832435 35.2627500 35.2828800 35.2830998 35.2831783 SameSta OTHERS > Browser > Country APM 1.54 Services Deployments Hide Contras 11 results found DETAILS Showing 1-5 of 9 VERSION 1631908800 1631907000 1631905200 1631903400 1631901600 Traces TypeError pages/department/ Cannot read property 'geeCartStatus' c last seen 3 minutes ago about 1 mon Latency Distribution TypeError https://static.datado Cannot read property replace of und last seen about 2 hours ago about 2 RangeError mps://static.tac Maximum cal stack size exceeded last seen about 1 hour ago 15 days o RangeError https://static.datar Maximum cal stack size exceeded last seen about 1 hour ago 22 days of TypeError https://static.datado Cannot read property value of null last seen about 14 hours ago-about inventory-api http.request. RangeError ttps://static.datac Maximum cal stack size exceeded st seen about 1 hour ago 22 days of TypeError https://static.datado Cannot read property from ts of null Sast seen about 4 hours ago about 41 16318926001631894406 16318952001633880001631899600 Profiles Service Config CRITICAL shopist-web-ul TypeError: Cannot read property "getCartStatus of undefined 59.3k total errors in this issue First seen about 1 month ago TypeError https://static.datato Cannot read property 'byteKeyString" last seen about 4 hours ago 11 days SyntaxError <anonymous in Invalid regular expression //: Unterm View FIRST SEEN 10 minutes ago A MAY BE FAULTY 40 minutes ago 1 hour ago A MAY BEFAULTY 2 hours ago 2 hours ago A MAY BE FAULTY W Wed 22 Viewing sample event 59308 of 59308 ERROR Aug 04 15:42:05.041 (about 1 hour) A TypeError: Cannot real property "petCertitatus of undefined pages/department/_dept/product/product.vue at line 42 37. 38. 39. 40. 41. 42. 43. 44, 46, 47 Session ! Deploy may be faulty: Version 1631908800 is active and contains 1 previously A This version contains previously unseen errors Requests by Version - The 23 > /node_modules/vue/dist/vue.runtime.esm.js atline 1854 > /node modules/vue/dist/vue.runtime.asm.js at line 2179 /node_modules/vue/dia/vue.runtime.asm.js at line 6917 Showing 1-1 of 1 TIME DETECTED Sep 17, 2:02 pm Metrics A MAY BEFAULTY Deployment Tracking 1631988808 First Seen: 10 minutes ago Last Seen: ACTIVE 160ccc04-013-9506314e2 ce-b723-4640-b6fc02071b972 const sessionID window.localStorage.getitent"session_id"11 // Attempt to pull information about the user's shopping cart from the API. //CART DATA is initially populated when the user first Loads the page. If this product inCart=undefined) ( window.CART STATUS.getCartStatuslami_product", { productId}); 134 // anly talk to the API 1f the user has a current session iffuser. hasSession "true) [ fatch(https://apt.example.com/add_itas.json", ( Comparing 1631908880 to 1631907000 400 Version Metrics at 25 Error Types Total Requests by Version Latency by Version WIS Endpoints 1631088001631007000 Oversions 12:00 16319088001631907000 Operversions ERROR TYPE InventoryError1631906800 last sean: 1 minutes ago 13:00 11:30 14:00 dh Sep 17, 10:09 am-Sep 17, 2:09 pm 12:15 ENDPOINT PUT inventory Error Rate by Version 11:00 12:00 Last occurrence 3 minutes ago Mon 3 + View in Context of RUM - «Q X Create Notebook Declare Incident HTTP STATUS CODE 500 14:00 do 30 M 40#41Distributed Tracing web-store-mongo 0% errors 14.6 ms avg latency 0.16 req/s DATADOG 0.16 req/s auth-dotnet 0% errors 4.99 s avg latency 0.01 req/s 0.08 req/s auth-dotnet-postgr... 0% errors 16.3 ms avg latency 0.08 req/s 0.01 req/s web-store 0% errors 12.7 ms avg latency 0.19 req/s product-recomm... 0% errors 35.4 s avg latency <0.01 req/s product-recommen... 0% errors 524 ms avg latency <0.01 req/s model-storage 0% errors 729 ms avg latency <0.01 req/s ad-server-http-client 0% errors 2.47 s avg latency <0.01 req/s ad-server 0% errors 1.85 s avg latency 0.02 req/s ad-server-mongodb 0.02 req/s 0% errors 204 ms avg latency <0.01 req/s ad-server-redis 0% errors 7.77 ms avg latency <0.01 req/s 41#42Expanded to a broad APM suite Client Side DATADOG ... Web Browser Mobile Synthetic Monitoring (2019) Real User Monitoring (2020) Error Tracking (2020) Session Replay (2021) ← Server Side O O O ... Application Server Databases Cloud Infrastructure APM & Distributed Tracing (2017) Tracing without Limits™ (2019) Synthetic Monitoring (2019) Continuous Profiler (2020) Deployment Tracking (2020) Error Tracking (2021) Database Monitoring (2021) 42#43Expanded to a broad APM suite Client Side ... DATADOG Web Browser Correlate traces with logs Mobile Synthetic Monitoring Real User Monitoring Error Tracking Session Replay ← Server Side O Log Management O O ... Application Server Databases Cloud Infrastructure APM & Distributed Tracing Tracing without Limits™ Synthetic Monitoring Continuous Profiler Deployment Tracking Error Tracking Database Monitoring Infrastructure Monitoring Correlate traces with host, container, & runtime metrics 43#44Strong uptake of APM APM Annual Recurring Revenue DATADOG Q2-18 Q2-19 Q2-20 Q2-21 Continuous Profiler Real User Monitoring Synthetics Core APM 44#45Watchdog: The Datadog Al Engine Anomalies ✔% reduce MTTR Watchdog Insights - Contextual - Surfaces signals Rare Events DATADOG 2 - Outliers °°° Correlations M Watchdog Alerts reduce MTTD Proactive Surfaces symptoms Clusters * ş.. Dependencies - Watchdog RCA reduce MTTR - - Connect the dots Surfaces root causes 45#46Watchdog Alerts III Kubernetes Starting on February 1st, Latency of the Controller Manager's workqueue processing unit was up for about 14 hours name:plain1a-k8s_plain1a-k8s-control-plane-c5-4xla... was impacted ENV prod SERVICE k8s-control-plane +1 DATADOG QUEUE deployment KUBE_CLUSTER parent1 Latency of the Controller Manager's workqueue processing unit 3.1k (average) 10K 8K 6K 4K 2K OK Jan 31, 23:30 - Feb 1, 13:45 12:00 1 February 12:00 Tue 2 46#47Watchdog Insights 50 Insights 5 View all LOG ERROR OUTLIER env:staging 40.0% of total errors <0.1% of total logs Views EO Logs Save + a source: python 2.5K OK 15:58 Showing 467 of 655 Q Search facets > CORE > MONGO >WEB ACCESS > RUM > LAMBDA > MONITORS > ANALYTICS > MCNULTY > VPC > ALERTING-VALIDATOR >BROWSER METRICS > GEO[P > BILLING AUDITING > CONSUL > COMPLIANCE RESOURCE RAW QUERY > EVENT >DATADOG >DATADOG 15:57 > FRONTEND OMPUTANCE POLICY > FLOW LOGS 15:58 Add + DATADOG 15:59 Hide Controls Insights 16:00 LOG ERROR OUTLIER env: staging View all 50,721 results found 16:01 40.0% of total errors <0.1% of total logs ROLE DATE Sep 22 16:10:14.008 kube-node Sep 22 16:10:14.000 kube-node Sep 22 16:10:12.008 kube-node Sep 22 16:10:09.008 kube-node Sep 22 16:18:88.000 kube-node Sep 22 16:18:08.000 kube-node Sep 22 16:18:03.000 kube-node Sep 22 16:10:83.008 kube-nade Sep 22 16:10:03.000 kube-node Sep 22 16:10:03.000 kube-node Sep 22 16:10:03.000 Sep 22 16:10:03.000 kube-node Sep 22 16:10:03.008 kube-node Sep 22 16:10:03,000 Sep 22 16:18:83.008 kube-node. Sep 22 16:18:82.098 kube-node. Sep 22 16:18:02,098 mindy-bhandle. Sep 22 16:10:02.000 common Sep 22 16:10:02.008 kube-node Sep 22 16:10:02.008 kube-node 16:02 PROFILING ANALYSIS 16:03 Ⓒ product-recommendation NAME PROFILING ANALYSIS HIB Deadlock involving 2 threads detected This could impact performance smoke-testing Hot sre_sre-default web_mcnulty-synthetics sre sre-default web support-admin mindy-bhandle 16:00 web_mcnulty-synthetics sre are-default smoke-testing sre sre-default product-recommendation Deadlock involving 2 threads detected This could impact performance 15m Past 15 Minutes apm-firehose-store 1-041712c616dd5cda6 web-canary_support-admin 1-0dce91df88368328e consul-sync-external-ips. 1-08eebf4bd0ff54d48 consul-sync-external-ips-16. 1-0da43f0f8364862e7 consul-sync-external-ips-16. 1-82f83d3aa1d54bb16 consul-sync-external-ips-16 i-03123fb9f185fe82d i-053ea9b2ae2e64869 1-0fa13bdafd224f322 1-06f8365e3cf3008c0 i-0af9a6ad15dfb89c2 1-0985f8314faf0df65 1-0fa13bdafd224f322 1-061836503cf300800 1-0985f8314faf0df65 1-053ea9b2ac2e64869 consul-sync-external-ips-16. 1-02287842d3f5cb5ce 1-8b24d5427cba1823b 16:06 RUM LATENCY OUTLIER view.url_path_group:/product/? HOST 16:08 consul-sync-external-ips-16. 1-88eebf4bd0ff54d48 consul-sync-external-ips-16. 1-022a7842d3f5cb5ce 16:09 Export ch LOG PATTERN ANOMALY Jun 24, 15:45-19:45 Q vault-2xlb.c.datadog-enclav... [Triggered] [ASN] Test 2 test 16:10 product-recommendation ERROR Options C CONTENT [Recovered on (host:1-041712c616dd5cd=6}] [Triggered] [ASN] Test 2 test Events from the Cronjob sre/consul-sync-ex.. Events from the Pod sre/consul-sync-extern.. Events from the Pod sre/consul-sync-extern... Events from the Job sre/consul-sync-extern.. Events from the Pod sre/consul-sync-extern.. [Triggered on (host:1-0fa13bdafd224f322}]] - Events from the Pod sre/consul-sync-extern... [Triggered on (host:1-0af9a6ad15dfb09c2)] - [Triggered] [ASN] Test 2 test [Triggered on (host:1-0fa13bdafd224f322)].. Events from the Pod sre/consul-sync-extern.. [Triggered] [ASN] Test 2 test Events from the Pod sre/consul-sync-extern... Events from the Pod sre/consul-sync-extern.. [Recovered] [ASN] Test 2 test Events from the Job sre/consul-sync-extern.... Events from the Job sre/consul-sync-extern.. RUM LATENCY OUTLIER view.url_path_group:/product/? p90 p90 LOG PATTERN ANOMALY 819) product-recommendation ERROR Jun 24, 15:45-19:45 Augmented Troubleshooting with insights - Driven by current search context - Cross-product insights CO C 47#48Watchdog Root Cause Analysis >Views Watchdog Save ✓ Story Category ✔APM Infrastructure ✓ Story Type APM Latency APM Error Rate AWS ELB Cloud Network Health V APM ✓ APM Environment shop.ist ✓ APM Service ad-server ✔ad-server-http-client ✓shipping-queue-redis web-store 3 DATADOG 2 1 1 product-recommendati... 1 1 1 2 1 1 1 3 An increase in latency on ad-server-http-client caused increased latency on 1 resource on ad-server The GET /ads endpoint was driving the increase in latency ENV shop.ist An increase in latency on product-recommendation-db caused an increase in errors on 1 resource on product- recommendation-db The INSERT INTO purchases (id... query was driving the increase in error rate ENV shop.ist A version change on web-store caused an increase in errors on 3 services The Shopping CartController #che... endpoint was driving the increase in latency 40 30 % Error Rate 104 total (0.8 err/s) 20 10 0 ROOT CAUSE ad-server-http- client 1 of 2 endpoints 12:00 18:00 ROOT CAUSE web-store 3 of 34 endpoints 1mo Nov 10, 06:30 07:15 Tue 10 06:00 Oct 17, 5:07 am - Nov 16, 4:07 am 12:00 ad-server 1 of 8 endpoints 18:00 Wed 11 06:00 48#49Watchdog: The Datadog Al Engine Anomalies ✔% reduce MTTR Watchdog Insights - Contextual - Surfaces signals Rare Events DATADOG 2 - Outliers °°° Correlations M Watchdog Alerts reduce MTTD Proactive Surfaces symptoms Clusters * ş.. Dependencies - Watchdog RCA reduce MTTR - - Connect the dots Surfaces root causes 49#50Log Management DATADOG#51Servers Databases Web Browers [ Mobile Servers Serverless Functions What Is Log Management? Cloud Services LOG MANAGEMENT SOLUTION Search Monitor Report and Analyze Machine Learning Archives & Audits 290 Dashboards 51#52Designed from the start to be part of the platform Infrastructure Monitoring DATADOG FO Log Management .:| APM Seamless correlation & troubleshooting: - Common tagging structure across all 3 pillars - Metric-trace-log correlation Lower MTTD and faster MTTR with more collaboration and less context switching 52#53Log problem statement 1 (Very) high log volumes Often makes it cost prohibitive DATADOG 2 Large variation of log volumes Generates cost uncertainty 3 Large variation in log value Makes up-front filtering strategies fail 53#54Traditional Log Management Index everything at high cost, or throw out data and lose visibility TORRENT OF DATA DATADOG TYPICAL LOG MANAGEMENT SOLUTION Ingest & Index Everything Captures everything, but becomes prohibitively expensive OR Index Limited Data TORRENT OF DATA TYPICAL LOG MANAGEMENT SOLUTION Exclude Logs Cost effective, but impacts visibility 54#55Logging without Limits™ No cost vs. visibility trade-offs DATADOG Ingest everything cost-effectively Process, archive, and generate metrics on all logs. Metrics Archive TORRENT OF DATA 303 LOG MANAGEMENT Index only what's valuable Search Analytics Alerts Dashboards Metrics/Trace/Log Correlation 55#56Strong uptake of Log Management Log Management Annual Recurring Revenue DATADOG Q2-18 Q2-19 Q2-20 Q2-21 56#57Large companies tend to have larger problems Extra large volumes (>1 petabyte/day) Means network clogging and substantial additional costs DATADOG Sensitive data Must be scrubbed before leaving the premise of the company Migrations to new tools Typically a painful, costly, disruptive multi-month process 57#58Vector & Observability Pipelines for scaled data needs Serverless Containers Agents APIs Networks LOGS Clouds METRICS Hosts þiļ loT TRACES Apps Ingest data from any source Managed through Datadog Interface YOUR INFRASTRUCTURE REDUCE TRANSFORM ROUTE 17 Aggregate, enrich and transform the data CLOUD MONITORING SECURITY aws DATADOG FIREEYE™ Azure New Relic. splunk> Google Cloud Grafana Carbon Black. Route to multiple destinations 58#59Pierre Betouin Vice President, Product Management#60Traditional security DATADOG 1 POINT SOLUTION 1 + | POINT SOLUTION 0 Firewall | 묘 0 POINT SOLUTION POINT SOLUTION 1 60#61DevOps observability and security visibility are siloed Observability Rich data insights without network visibility DATADOG Traditional Security Network data without runtime context 61#62A unified platform for Dev, Ops, and Security teams DATADOG Datadog Cloud Security 62#63DevOps and Security teams are not aligned DATADOG :::: DevOps Different goals Different tools CDC COC Jan Security Visibility to different data 63#64Break down silos between DevOps and Security CDC DATADOG Security DevOps Enable DevOps to be the first line on security COC Security DevOps CDC Security Align around the same, rich sources of data 64#65Why Datadog for cloud security? DATADOG 얹 100 Break down silos between DevSec and Ops Datadog has the richest, deepest data No additional friction or performance penalty to instrument 65#66Observability as a platform for security DATADOG O Observability Infrastructure Monitoring APM Log Management y Cloud Security Cloud Security Posture Management Cloud Workload Security Application Security BETA Cloud SIEM 66#67Full-stack security for production cloud environments DATADOG INFRASTRUCTURE & CLOUD CSPM CWS CLOUD SECURITY PLATFORM CLOUD SIEM Cloud Security Posture Management Cloud Workload Security Security Monitoring APPLICATIONS BETA AppSec Application Security 67#68Datadog Investor Meeting October 27, 2021 DATADOG 68#69Amit Agarwal Chief Product Officer#70Focus on the customer DATADOG Infrastructure monitoring 7 NPM CUSTOMERS 93 UX monitoring H APM ¡Q Logs 70#71KEY VALUE TO CUSTOMER Ease of implementation and use Deploy within minutes, for quick time-to-value Alerting and dashboarding capabilities available to all users, regardless of role DATADOG Quickly integrate all technologies, whether cloud/next-gen technologies or on-premise No-code: No query language expertise required Easy-to-use UI: Minimal onboarding time for users No need for professional services or extensive training 71#72KEY VALUE TO CUSTOMER Each product competes DATADOG 7 Infrastructure Real User Monitoring Incident Management Ⓒ Synthetic Monitoring 19 Log Management APM & Continuous Profiler of Network Monitoring Security Monitoring CSPM Database Monitoring fx Serverless Workload Security S CI Visibility 72#73KEY VALUE TO CUSTOMER Every product is deeply integrated MULTIPLE DATA SOURCES 450 SOURCES LOGS TRACES ill METRICS ACTIVITY </> METADATA DATADOG PLATFORM SERVICES Analytics ML Insights Visualizations Collaboration Automation Alerting Mobile App PRODUCTS / USE CASES Infrastructure Serverless Log Management APM Continuous Profiler Error Tracking Synthetics RUM NPM Network Device Security Compliance Incident Management SINGLE PANE OF GLASS... W ...ACROSS TEAMS DEV IT OPS SECURITY SUPPORT BUSINESS 73#74CUSTOMER EXAMPLE Global shipping company DATADOG Annual Recurring Revenue (ARR) $2M $1M # of products utilized 3Q18 1 4Q18 1 1Q19 1 2019 3Q19 1 4Q19 1 1 1Q20 2Q20 3Q20 4 4Q20 1Q21 6 7 7 7 2021 7 74#75CUSTOMER EXAMPLE Global payroll and HR services company DATADOG Annual Recurring Revenue (ARR) $1M # of products utilized 4Q16 1017 1 1 2017 1 IT 4Q17 3Q17 1 1 1018 2018 1 1 4Q18 1 1 1 3Q18 1Q19 2Q19 2 11 3Q19 3 4Q19 3 1Q20 2020 3 7 3Q20 7 4Q20 7 1Q21 2Q21 7 7 75#76Customer benefits Better visibility on infrastructure usage Better optimized infrastructure costs Better customer experience Lower downtime (faster remediation of errors) Faster product innovation DATADOG See legal disclosures for definition of dollar-based net retention rate. More productive engineers 130%+ Dollar-based net retention rate for 16 consecutive quarters 76#77TTM gross retention rate 100% 95% 90% Q1-18 Q2-18 Q3-18 Q4-18 Q1-19 Q2-19 Q3-19 Q4-19 Q1-20 Q2-20 Q3-20 Q4-20 Q1-21 DATADOG See legal disclosures for definition of dollar-based gross retention rate. Q2-21 77#78Key characteristics of Datadog's pricing model#79Our pricing is transparent Infrastructure Pro $15 Enreprise $23 DATADOG FO Log Management Ingest Retain or Rehydrate $0.10 $1.70 APM $31 5 APM & Continuous Profiling APM & Continuous Profiler $40 datadoghq.com/pricing Synthetic Monitoring API Test $5 Browser Test $12 7 Real User Monitoring Real User Monitori $15 79#80Our products are priced for usage and value PRODUCT Infrastructure APM Logs Synthetics Real User Monitoring Serverless Database Monitoring DATADOG PRICING per host per host per ingested GB/ per indexed event per test run per session per invocation per host PRODUCT Network Monitoring Incident Management Security Monitoring Cloud Security Posture Management Cloud Workload Security CI Visibility PRICING per host / per device per user per GB of analyzed logs per host per host per user 80#81Our products are priced to give customers control DATADOG TORRENT OF DATA 3 Ingest Everything Choose: - What to retain - How long to retain it 81#82Datadog democratizes data One platform - deployed everywhere, used by everyone APM Services Views Search 200 Requests 7.89k total (8.8 req/s) Min Q Search facets: Requests ✓ CORE 11:30 Showing 9 of 163 ✓ Duration Status Ok Error > Env Ons ✓ Service a Filter values ✔web-store Live Search 100% of ingested trac ad-server Analytics 8. Map Q Envidev Max ad-server-http-client Traces + Add 11:35 101s Tx 7.89k TX 7.89k TX . Profiles 11:40 Hide Controls Watchdog Insights ↓DATE Sep 28 11:43:43.1 Sep 28 11:43:42.6 Sep 28 11:43:41.3 DATADOG Sep 28 11:43:48.4 Sep 28 11:43:40.1 Sep 28 11:43:40.1 Sep 28 11:43:40.1 Sep 28 11:43:39.7 Sep 28 11:43:39.7 Sep 28 11:43:39.6 Sep 28 11:43:39.6 Sep 28 11:43:39.8 Sep 28 11:43:38.6 Sep 28 11:43:38.4 Sep 28 11:43:38.2 web-store Shopping CartController#checkout Sep 28 11:42:37.426 7.51 s p94 LA POST /checkout 500 INTERNAL SERVER ERROR Flame Graph Span List (78) rack.request Shopping CartController#checkout mang E Full Trace Tags Infrastructure 1,5 cart ( id value F checkout ( 2,5 web-store rack.request> 0 Shopping CartController#checkout Developer sample view checkout version cloud provider asp.... action controller ( instance #ShoppingCartController: 0x00087f643c0631d0> expiry date. last four_digits A Payment ServiceUnavailableError: Payment service reported 503 Unavailable. I 331 35 Metrics Logs 21 Errors 5 615337f2cee3060013e40e5b 45 2.2 gep 94/27 0723 rails.action controller Shopping CartController#checkout mo.. 5,s 65 http... 1 Processes Network Code Hotspots 7,5 7,513 Open Full Page X Service ad-server Hide Legend Select 28 web-store-mongo 20 web-store 71 auth-dotnet-post... 6 product-recomme... 4 product-recomme... 2 ad-server-http-cli.... 2 payments-go 2 payment-postgres 2 Y 7.51 s p94 LA (100% of total duration) X Q Posture Management Home: Signals Findings Rules CIS - AWS CIS a CIS-Azure CIS 90% of all weighted findings passed -1% vs. 30 days ago Explore all resources CIS- Kubernetes CIS Security posture score Rules evaluation PASS 11 Rules evaluation PASS 0 Explore rules PASS 32 Explore rules Rules evaluation FAIL 8 00 FAIL 37 FAIL Posture score per account 473437055159 (168 resources) 88% 291fba3f-e0a5-47bc-a299-3bdab2a50a05 (161 r 93% 363525035937 (121 resources) 172597598159 (25 resources) 91% 91% Top 5 requirements by rule failures Networking. IAM Logging Storage Manage accounts Top 5 requirements by rule failures Database Services App-Service Storage-Account Networking Explore requirements [ Other-Security-Considerations Security user sample view Explore requirements Top 5 requirements by rule failures Kubelet API-Server General-Policies 1- 0- 0 12 -2% -0.52% -1% X -0.85% 16 Top 5 high-severity rule failures 6,409 PIDs cgroup limit is used 372 Host's network namespace is not shared 281 Docker socket is not mounted inside any containers 30 CloudTrail multi-region is enabled 24 --rotate-certificates argument is not set to false Resource types with the most fail findings aws security_group aws network acl aws s3 bucket aws_vpc aws_lam_server_certificate Explore issues Explore resource types Resource types with the most fail findings azure sql server azure_postgresql server azure_app_service azure storage account. azure_security_contact kubernetesNode kubernetesCluster Explore resource types Resource types with the most fail findings kubernetes_worker_node 0 55 23 23 7 5 307 40 246 175 120 75 25 20 15 311 356 25 0 82#83Adam Blitzer Chief Operating Officer#84What we do: land-and-expand Initial land - Free trials - Easy to adopt - Short time to value New product adoption Customers adopt additional products from our unified platform 130%+ Dollar based net retention rate In each of the last sixteen quarters Frictionless usage expansion Customers monitor more workloads, applications, and user experiences More users use the platform to get insights meaningful to their role DATADOG See legal disclosures for definition of dollar-based net retention rate. 84#85CUSTOMER EXAMPLE Multinational financial info services company DATADOG Annual Recurring Revenue (ARR) $2M $1M # of products utilized 3Q18 1 4Q18 1 1019 3Q18 3Q19 4Q19 1Q20 2020 3020 1Q21 1 1 4 4 5 6 6 6 6 4Q20 2Q21 7 85#86CUSTOMER EXAMPLE Major US grocery chain. DATADOG Annual Recurring Revenue (ARR) $500K # of products utilized 3Q18 4Q18 1 1 1019 1 3Q18 3Q19 1 1 4Q19 2 1Q20 4 2020 6 3Q20 6 4Q20 1Q21 6 6 2021 6 86#87Our go-to-market strategy DATADOG Enterprise sales team - High value - Longer sales cycles Commercial sales team High velocity - Inside sales Self serve - Month-to-month billing 87#88Our support evolution 2016 Solutions Engineering DATADOG 2017 Sale Engineers Solutions Engineering 2018 Enablement Mid-market Sales Engineers Sales Engineers Solutions Engineering 2019 Technical Acct Mgmt Enablement Mid-market Sales Engineers Sales Engineers Solutions Engineering 2021 Professional Services Technical Acct Mgmt Enablement Mid-market Sales Engineers Sales Engineers Solutions Engineering Future ... ●●● ... ... ... : ... ... ... 88#89Executing on strong customer growth # of Customers 20,000 15,000 10,000 5,000 2016 DATADOG 2017 Customers <$100K ARR 2018 2019 2020 Customers >$100K ARR 2Q21 % of ARR 100% 75% 50% 25% 0% 2016 2017 2018 % of ARR from <$100k Customers 2019 2020 2Q21 % of ARR from >$100k Customers 89#90David Obstler Chief Financial Officer#91Key takeaways Product-led company; continuous innovation Strong customer growth; rapid expansion of large customers DATADOG Frictionless, customer-led selling motion High revenue growth with operating efficiency 91#92Product innovation Real-Time Unified Data Platform 2010 2011 Infrastructure Monitoring Hosts / Clouds / VMs / Containers / Processes / lot DATADOG 2012 2013 2014 Founded Datadog to break down silos 2015 2016 APM 2017 Tracing Log without Management Limits™ Logging without Limits™ Distributed Watchdog Tracing Alerts Serverless Monitoring 2018 Continuous Profiler 2019 Incident Management Real User Monitoring Synthetic Error Monitoring Tracking Network Performance Monitoring Cloud SIEM Mobile RUM Session Replay Network Device Monitoring Deployment Cloud Tracking Cloud Security Posture Management Workload Security Database Monitoring CI Visibility Watchdog Root Cause Analysis Watchdog Insights 2020 2021... Deployed everywhere, used by everyone 92#93Aggressive investment in R&D DATADOG TTM R&D as % of sales 40% 30% 20% 10% 0% DDOG Competitor 1 Competitor 2 Competitor 3 Competitor 4 93#94Strong upsell and retention DATADOG Mid-90%s Dollar-based gross retention rate 130%+ Dollar-based net retention rate for 16 consecutive quarters 94#95Platform strategy is resonating with customers % of Customers using 2+ Products 15% 25% 32% 40% DATADOG 50% 58% 63% 68% 71% 72% 75% 75% 3Q18 4Q18 1019 2019 3019 4Q19 1020 2020 3020 4020 1021 2021 % of Customers using 4+ Products 7% 3Q18 4Q18 1019 2019 3Q19 10% 20% 12% 22% 25% 15% il 4Q19 1020 2020 3020 4020 1021 2021 28% 95#96Strong customer growth DATADOG Total customers 3,785 FY16 5,403 FY17 7,676 FY18 10,536 FY19 14,200 FY20 16,400 2Q21 96#97Strong customer growth # of Customers with ARR $1M+ 2 FY16 11 DATADOG FY17 27 FY18 54 FY19 101 FY20 145 2021 # of Customers with ARR $100K+ 126 FY16 236 FY17 438 FY18 837 FY19 1,228 FY20 1,570 2021 Average ARR:~$500k We updated the definition of MRR as of the quarter ended September 30, 2021 to capture usage from subscriptions with committed contractual amounts and applied this change retroactively. 97#98Best-in-class sales efficiency DATADOG CAC payback, months, last reported quarter 50 40 30 20 10 O DDOG Competitor 1 Competitor 2 ¡ Competitor 3 Competitor 4 CAC payback is the prior quarter S&M expense, divided by gross-margin adjusted change in revenue, quarter over quarter. 98#99DATADOG 99 1Q18 2018 3018 4018 1019 2019 3019 4019 1Q20 2020 3Q20 4Q20 1Q21 2021 1018 2018 3Q18 4Q18 1019 2019 3Q19 4Q19 1Q20 2020 3Q20 4Q20 1Q21 2021 -10 50 100 10 150 20 200 30 250 40 Revenue ($M's) Non-GAAP Operating Income ($M's) Strong revenue and profit growth#100Q&A Session Olivier Pomel CEO & Co-founder Alexis Lê-Quốc CTO & Co-founder David Obstler CFO Yuka Broderick Head of Investor Relations#101Appendix # of Customers with ARR $100K+ 933 1020 984 2020 1,082 3Q20 1,228 4Q20 1,406 1Q21 1,570 2021 We updated the definition of MRR as of the quarter ended September 30, 2021 to capture usage from subscriptions with committed contractual amounts and applied this change retroactively. DATADOG 101#102Appendix Non-GAAP operating profit ($000's) Revenue GAAP operating income (loss) GAAP operating margin Add: Share-based compensation expense Amortization of acquired intangibles Non-cash benefit related to tax adjustment Plus: Employer payroll taxes related to employee stock transactions Non-GAAP operating income (loss) Non-GAAP operating margin DATADOG 1Q18 $39,715 $153 0% 794 112 $1,059 3% 2Q18 $45,678 $123 0% 949 108 3Q18 3% $51,074 $(4,635) (9)% 1,308 112 4Q18 1Q19 2Q19 $61,610 $70,050 $83,222 $(6,674) $(9,662) (11)% (14)% 2,193 179 $1,180 $(3,215) $(4,302) (6)% (7)% 2,445 175 12 $(3,994) (4)% 2,894 177 (5,007) 460 $(7,030) $(5,470) (10)% (7)% 3Q19 $95,864 $(4,218) (4)% 4,677 179 88 $726 1% 4Q19 1Q20 $113,644 $131,248 $(2,266) $3,778 (2)% 3% 9,018 221 901 $7,874 7% 12,060 247 246 $16,331 12% 2Q20 3Q20 4Q20 1Q21 2Q21 $140,012 $154,675 $177,531 $198,549 $233,549 $654 $(9,267) (6)% 0% 16,834 147 (5,561) 3,264 20,716 11% 274 2,086 $15,338 $13,809 9% $(8,938) $(12,830) $(9,886) (5)% (6)% (4)% 24,764 28,861 275 2,021 355 10% 3,169 $18,122 $19,555 10% 34,515 1,071 5,167 $30,867 13% 102