You can listen to this Deep Dive here
Olivier Pomel and Alexis Lê-Quôc, the two co-founders as well as CEO and CTO of Datadog respectively, used to work at Wireless Generation, a NY-based SaaS company focusing on the education sector. Olivier was running a team of Developers there whereas Alexis was leading Operations. As Developers and Operations team have to work together in many cases to accomplish project objectives, Olivier and Alexis got to know each other really well and became friends over time.
Generally speaking, Developers and Operations team used to work in silos and were prone to have somewhat acrimonious relationship. While developers write code and build the applications, operations team is responsible for these in production. Given the natural tendency of blaming each other in these teams when something inevitably breaks, Olivier and Alexis had "no jerk" policy in hiring. Even then, Olivier and Alexis found their teams routinely at odds against each other.
Two things happened almost simultaneously that led Datadog to be at the right place at the right time: a) these silo structure between Developers and Operations started to fall apart as DevOps, the idea of a more cohesive, integrated workflow gained more momentum, and b) the rise of cloud fundamentally changed how developers and operations used to co-ordinate with each other. Datadog founders bet both on these to be secular trends. The founding team realized there's something to build here at the infrastructure layer:
...the starting point for Datadog was that there must be a better way for people to talk to each other (i.e. Developers and Operations). We wanted to build a system that brought the two sides of the house together, all of the data was available to both and they speak the same language and see the same reality. It turns out, it was not just us, it was the whole industry that was going this way.
This DevOps movement was a core part of the cloud migration that was just starting to take place when we started Datadog, in the early 2010s. A lot of the value prop for the cloud was that, instead of having a development team that builds an application and then you spend 6 months filing tickets and placing order forms in order to actually bring that application into production, and you can’t change your mind for three years on the way it’s going to run… with the cloud you can actually decide on the spot and do whatever you want, engineers and developers can do it themselves without needing to ask for permission, or file a form. And then you can change your mind from one day to the next. That really brought the developers and operations teams together, and that was a big part of the DevOps movement.
Building a cloud infrastructure startup in NY, instead of San Francisco, in the post-Global Financial Crisis (GFC) environment was far from easy. Many West Coast investors thought Datadog's decision to be in NY instead of SF was some sort of "mental deficiency", and the company wasn't funded for almost a year. Such difficulty of getting early traction from investors made the founding team to be even more focused on solving the real problems that they knew their potential customers had. Once they shipped their initial product, they got almost an immediate market traction. Fast forward to 2019, just before going to IPO, Datadog allegedly received $8 Bn acquisition offer (more than 20x 2019 revenue) from Cisco. As Olivier later recalled, "...to grow to a large company, one of the hardest thing to do is not to sell along the way."
The decision to not sell the company is looking golden so far despite ~55% correction from its all-time high as Datadog's market cap exceeds $27 Bn today.
Before studying Datadog, I was aware of Datadog's cute logo (see below) and sort of expected that the co-founders are perhaps huge dog lovers. I found it funny that neither of the co-founders have (or ever had) a dog. The story behind the name is "Datadog" quite a nerdy one:
...in our previous company, we used to name the production “servers dogs”. “Data dogs” were production databases and “Data Dog 17” was the horrible, horrible, Oracle database that everyone lived in fear of. Every year it had to double in size and we had to sacrifice goats so the database wouldn’t go down. So it was really the name of fear and pain and so when we started the company we used Data dog 17 as a code name, and we thought we’d find something nicer later. It turns out everyone remembered Datadog so we cut the 17 so it wouldn’t sound like a MySpace handle and we had to buy the domain name, but it turned out it was a good name in the end.
Enough factoids; let's get into the meat of the discussion. Here's the outline for this month's Deep Dive:
Section 1 What is Datadog? I collaborated with Justin from Technically to explain Datadog's platform in a more lucid language.
Section 2 Datadog Economics: I elaborated on how Datadog makes money as well as its operating cost structure on this section.
Section 3 Competitive Dynamics: Competitive dynamics among Datadog, other publicly listed companies in the observability platform space such as Splunk, New Relic, Dynatrace etc., public cloud hyperscalers, open source solutions are the primary focus in this segment.
Section 4 Management and Incentives: Datadog's management and incentive structure is discussed in this section.
Section 5 Valuation and Model Assumptions: Model/implied expectations are discussed here.
Section 6 Final Words: Concluding remarks on Datadog, and disclosure of my overall portfolio.