Datadog IPO & S-1 Teardown. Datadog S1 Teardown
By: Jon Ma
Tags: Datadog, S1, Metrics, IPO, SaaS

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I worked in venture for 4 years at Insight Partners ($20 billion AUM software focused fund) and Signalfire ($800m AUM) where I focused on investing in growth-stage SaaS companies.

It was no secret that Datadog was one of the best private SaaS companies. Datadog’s S-1 made me realize what a truly amazing business it is.

In this post, we summarize Datadog’s S-1. Our philosophy at PublicComps.com (SaaS metrics for Public Companies) is that numbers don’t mean anything by themselves. So in this blog post, we benchmark Datadog’s metrics against its competitors and SaaS peers to contextualize why Datadog is such a phenomenal business.

Datadog benchmarked against the Public SaaS companies we track.

Problem

Prior to Datadog, the co-founders Olivier and Alexis led the development team and the technical operations team respectively at Wireless Generation. The database the two had to work with and maintain 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 technical operations team who were making sure the code ran properly. There wasn’t a common set of tools or metrics that both the developers and operations team could look at to monitor the health of the same system they were all using. Was it the code or was it the test or infrastructure that was to blame when the websites slowed for users?

Additionally, in 2010 when Oliver and Alexis started Datadog, companies were just starting to move move from legacy on-premise IT to public & private cloud products like AWS. Because companies were starting to deploy updates to their websites in days instead of months, teams needed a way to observe their cloud infrastructure in real-time (“observability”). To complicate things further, there was a shift from monolithic code bases to distributed systems that leveraged micro services and containers which made monitoring the thousands of servers and hosts very painful for the IT teams and developers.

Product Overview

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 solutions.

Timeline of Datadog’s various product lines from its S-1

2010 : Founded in 2010

: Founded in 2010 2012 : Launched Datadog Infrastructure Monitoring

: Launched Datadog Infrastructure Monitoring 2014 : Began monitoring containers

: Began monitoring containers 2016 : Began monitoring serverless environments

: Began monitoring serverless environments 2017 : Launched Datadog APM

: Launched Datadog APM 2018 : Launched Datadog Log Management and Analytics

: Launched Datadog Log Management and Analytics 2019 : Launched Datadog Synthetics

: Launched Datadog Synthetics 2019: Announced beta availability of Datadog Network Performance Monitoring and Real User Monitoring

Key Product Highlights

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 350+ 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 that they’re able to 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 onto a single dashboard. Instead of having to dig into specific metrics that different vendors spit out, Datadog allows users to see what’s going on at all times with their entire tech stack. The company provides an easy and simple way to drag and drop metrics onto a dashboard. 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.

Collaboration: The team at Datadog really emphasize the importance of breaking the traditional silos that Alexis and Olivier dealt with at Wireless Generation between the IT operations and developers. Because Datadog is a single dashboard that integrates across the entire tech stack, developers, IT, business users can investigate collaboratively and have a joint understanding of the health of their application & infrastructure. When there’s a spike in, say, query latency, someone on the IT team can make a comment and notify a developer and share what the IT team is seeing.

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 response tools like Pagerduty and communication platforms like Slack. In the spirit of collaboration, Datadog can automatically notify a developer or IT team on Slack if there’s a performance problem and allow users to trigger or resolve problems within Pagerduty or Servicenow.

Business Model

Datadog charges a subscription fee for its various products on a monthly or annual contract basis. Majority of Datadog’s revenue is subscription software sales so revenue run rate ~ annual recurring revenue (ARR). It’s worth noting that because Datadog doesn’t charge by seat, the company incentivizes companies to add more IT members, developers or business users to the product which drives adoption within organizations.

Go-To-Market (GTM)

Datadog has a highly efficient go-to-market model. In its S-1, Datadog talks a lot its land-and-expand business model focusing on a product that’s easy to adopt (hence the many out-of-the-box integrations Datadog offers) and with a short time to value. Not surprisingly, Datadog has a self-service and free trial model which allows any customer to integrate Datadog into various data sources for 14 days without requiring a credit card — reducing the friction of signing up and getting value right away.

Additionally, Datadog has an inside sales team and enterprise sales force. The customer success team is in charge of renewals and upsells. As we’ll see in the next section, the efficient GTM and land-and-expand nature of its cloud offering has lead to best-in-class payback period and net dollar retention.

Business Performance & Financials

In analyzing Datadog, we focus on the metrics that matter most for SaaS companies: Revenue, Revenue Growth Rate, Gross Margin, Retention, Payback Period, and Profitability/Capital Efficiency. Check out my article “Top 5 SaaS Metrics that VCs Look At” to understand the how & why behind the metrics.

Revenue: At $332m ARR, Datadog is definitely on the smaller side relative to all Public SaaS companies (e.g Salesforce is $16b annual revenue run rate) but DDOG in the same $300–500m ARR range as other high growth SaaS companies like Crowdstrike, Zoom, Elastic, Alteryx, MongoDb.

Datadog 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 ($52.7m net new ARR June ’19 up from $33.8m Mar ’19) which is always a positive sign of continued growth.