I worked in venture for 4 years and it was no secret that Datadog was one of the best private companies. Datadog's S-1 made me realize what an amazing business Datadog truly is! In today's post, we summarize Datadog's S-1 and benchmark the business against other competitors. At Public Comps, we believe metrics don't matter in a vacuum so we benchmark Datadog's metrics against its competitors and SaaS peers to contextualize why Datadog is such a phenomenal business.
- I am not a personal investor in Datadog and am not a financial advisor. These are my views and my views only and this post is not meant to be investment advice.
- We borrow some of the same graphs that Alex Clayton uses in his S-1 breakdowns because they're smart and important.
Prior to Datadog, Olivier led the development team and Alexis led the technical operations team at Wireless Generation. The database they used at Wireless Generation was called "Datadog" -- hence the name of the company. Alexis and Olivier realized that there was a lot of friction between the developers who were writing code and the operations team that were making sure the code ran without problem: there wasn't a common tool or set of metrics that both the developers and operations team could look at and collaborate on to figure out if the infrastructure and applications were working well. Was it the application code or was it the test or infrastructure that was to blame when the websites slowed for users?
Datadog is cloud-native software product that allows IT teams, developers, and business teams to collaborate, monitor and analyze the health and performance of cloud infrastructure and applications to make sure their digital products are working properly.
Additionally, Datadog claims to be the first company to allow end-to-end monitoring and analytics by providing products across infrastructure & application performance monitoring, log management, user experience monitoring, and network performance monitoring. The company focuses on providing a unified view of a company's entire tech stack in one product vs metrics living in different dashboards or data silos.
Easy to Integrate: At its core, Datadog integrates seamlessly to the entire tech stack ranging from cloud vendors (AWS, Azure, Google Cloud Platform, Alibaba Cloud), databases (Postgres, MySQL, MongoDB), automation tools/source control (CircleCI, Bitbucket, Gitlab, Github), other monitoring services (New Relic), containers (Docker, Kubernetes, etc), bug tracking (Jira), etc. With the integrations, Datadog is able to pull and surface all the metrics that matter for the different services that make cloud applications and infrastructure work. The value for customers is they're able integrate data sources or vendors in minutes versus requiring engineering resources that could take hours to add, say, MongoDB or Kubernetes as services to track.
Dashboard of Metrics: Datadog allows teams to put the core metrics that matter for their infrastructure & application on a single dashboard. Instead of having to dig into specific metrics that different vendors spit out, Datadog allows IT & developers to see what's going on at all times. The company emphasizes an easy and simple way to drag and drop metrics onto a dashboard but because of their log management product, Datadog is also powerful enough for developers to drill into specific logs to debug why a specific service became so slow.
Cloud Agnostic: It's worth noting that while AWS, Google Cloud Platform, Azure offer some sort of cloud monitoring solutions (e.g AWS has AWS Cloud Watch), Datadog is cloud agnostic and works across cloud vendors.
Alerts: Datadog integrates with 3rd party issue & incidence trackers like Pagerduty and communication platforms like Slack. As a result, Datadog can automatically notify a developer or IT team if there's a performance problem and allow users to trigger or resolve problems within Pagerduty or Servicenow.
Datadog charges a subscription fee for its various products. Note: majority of Datadog's revenue is subscription software sales so revenue run rate ~ annual recurring revenue (ARR)
Datadog has a highly efficient go-to-market model given their self-service + free trial model which allows any customer to integrate Datadog into various data sources for 14 days. Additionally, Datadog has an inside sales team + enterprise sales force. As we'll see in the next section, the efficient GTM and land and expand nature of its cloud offering leads to best-in-class payback period and net dollar retention.
Business Performance & Financials
We focus on the 5 metrics that matter most for SaaS companies: ARR, ARR Growth Rate, Retention, Payback Period, and Capital Efficiency
Revenue: At $330m ARR, Datadog is on already on par with Splunk's cloud product which just reached $300m ARR. The business added $52.7m in its most recent quarter and in the last 4 quarters its added $150m ARR which is quite impressive. Further, Datadog is seeing an increase in net new ARR in the last 4 quarters which is always a positive sign of continued growth.
Growth Rate: At 82% year-over-year growth, Datadog is the 4th Fastest-Growing Public SaaS company + fastest growing DevOps company.
Payback Period: Datadog has the lowest payback period (9 months) driven by the self-service model + high-velocity sales motion. And the most recent payback period wasn't just an anomaly: if you take the median payback period in the last 8 quarters, its roughly 10 months.
Retention: 146% net dollar retention is highest among public SaaS. Customers almost never churn (90-95% gross dollar retention according to their filing) and pay more when adding more workloads as customers migrate or add more applications to the cloud.
Capital Efficiency: Datadog spent $30m of cash to get to $330m ARR. That's ridiculous capital efficiency ($1 spent for every $10 ARR). In comparison, Slack spent nearly ~$560m in capital to get to $539m ARR which is good and roughly $1 spent for every $1 ARR. Datadog is almost 10x more efficient than Slack!
Large and Growing Market: Company estimates its market size is $35b. Further, Datadog cites an IDC research that infrastructure and platform-as-a-service spend will nearly triple from $60b in 2018 to $173b in 2022. It doesn't take a huge leap of faith to believe that companies will continue to spend on migrating from on-premise legacy systems to the cloud and will require adopting monitoring solutions like Datadog and its competitors.
Largest consumer companies using Datadog: What was particularly interesting was that some of the tech companies with the largest user bases use Datadog: Coinbase (20m users), Evernote (200m users), Airbnb (150m users), etc.
Datadog claims it competes against on-premise infrastructure monitoring tools that don't work quite well in a world of distributed systems and micro-services. Company also competes with homegrown solutions that leverage open source solutions like Graphite and Nagios but the downside of these solutions are that engineers are required to integrate with data sources.
Other cloud focused competitors include Kibana (Elastic), Grafana (open source), and SignalFx (acquired by Splunk for ~$1b). Lastly, in their S-1, Datadog claims it also competes with the cloud vendor's own cloud infrastructure & application monitoring solutions like AWS Cloud Watch.
Application Performance Monitoring: its interesting to note that Datadog just got into the Application Performance Monitoring space in 2017 (~2 years ago).
Log Management: Splunk's recent acquisition of SignalFx puts Splunk directly competitive with Datadog across all three product verticals. Its interesting that both are ~$300m ARR growing 80% YoY.
Its worth noting that Datadog is growing much quicker than most of its public market SaaS competitors particularly Elastic, Appdynamics (acquired by Cisco), Dynatrace, Splunk, New Relic and Solar Winds.
Since Datadog is a fast growth public SaaS company, it'll likely get valued on a Next-Twelve-Month ARR multiple.
The fastest growing SaaS (90%+ YoY) companies like Zoom, Crowdstrike trade for 20x+ NTM ARR and other fast growth companies (40-80% YoY growth) like Zscaler, Alteryx, Twilio, Slack, MongoDB trade between 13-18x NTM ARR.
Using 13-18x NTM ARR as a rough ball park and assuming Datadog grows 85% (see growth persistence) of its currently growth rate of 82% so roughly ~70% YoY, Datadog should be ~$560m ARR 12 months from now which implies $7.28b - $10b enterprise value.