Tech Brew Ride Home (BNS): Datadog Founder Olivier Pomel

Host: Morning Brew
Guest: Olivier Pomel, CEO & Co-founder of Datadog
Date: September 20, 2025

Episode Overview

In this rich, candid interview, Olivier Pomel, CEO and co-founder of Datadog, reflects on his journey from tinkering with computers in France to co-founding a $40 billion public company headquartered in New York. Pomel discusses his early tech influences, the unique New York tech ecosystem, lessons learned from operating during downturns, company culture, the birth and scaling of Datadog, and thoughts on the future of observability in the age of AI. Peppered with stories from the dot-com bust, open source projects like VLC, and building for real-world users, Pomel shares insights relevant to founders, operators, and technologists navigating today's startup landscape.

Key Discussion Points & Insights

Childhood & Early Programming Inspiration

• First Computers:

  • Thompson TO7/70 (70k memory) was his first at-home machine through his mom, a teacher. The Commodore Amiga was the "real computer" that made him fall in love with programming.
  • Quote: "The real computer that made me completely fall in love with programming was a Commodore Amiga." [01:00]

• Minitel Memories:

  • Recalls France’s pre-web network, Minitel: "It was the Internet before the Internet. In France, basically every single household had a VT100 terminal... Anything you can imagine would actually happen over the Minitel." [01:37]
  • On the web vs Minitel: "Minitel was text mode... even the adult stuff... required, I think, quite a bit of imagination." The Web’s graphical nature made it "immediately more exciting." [02:27]

Open Source Experience: VLC

• University Project:

  • Wrote the first version of what’s now VLC ("videolan" then), as a campus video streaming project, inspired by graphical programming and Europe’s demoscene.
  • Quote: "I can't take any credit for all of the amazing success that followed... it was generations of students and programmers after me that open sourced it." [03:12]

• Lesson Learned:

  • "It's all about getting the others to do the work and show up and build things that you can't build yourself... Even more true today. At Datadog we have more than 6,000 people contributing and I don't write any code anymore." [04:15]

Move to the US & the Dot-Com Era

• IBM Research & New York:

  • Arrived in NY in 1999 for a co-op at IBM, one of the top US research centers then ("...what I thought would be six months and I'm still here more than 25 years later." [04:40])
  • Lived through the dot-com boom and bust:
    • Quote: "You know the expression, 'party like it’s 1999'? That was exactly like that." [06:07]
    • The downturn: At first, it felt like a brief slowdown, but then, "it became very clear that things were going down pretty fast." [06:30]
    • Post-9/11 added to the uncertainty, especially for immigrants on visas. [07:29]

• Choosing to Stay:

  • Despite downturns and a compressed tech job market, stayed in NY for its energy and promise:
    • "Maybe foolishly at the time, believed that startups would still be around and there would still be a lot to build in technology." [07:29]

Building Career in NY Tech

• Amplify Education:

  • Joined/grew an edtech startup (now Amplify) -- "Building a successful business in public education is really difficult." [08:46]
  • Built a network of engineers and product people.

• Why Not Silicon Valley?

  • Chose NY for diversity, lifestyle ("I sort of need a city with all its diversity to basically thrive"), and an existing talent network. [09:26]
  • Despite starting as an underdog location for infra companies, believes NY proved the better long-term choice for Datadog.

Datadog’s Founding Story

• Co-founder Origins:

  • Met Alexei, his co-founder, first in college (ironically, by disconnecting his internet access as his court-martial sentence), then at IBM, then at later startups. [10:50–11:26]

• Birth of Datadog:

  • Both ran technical teams at Amplify (dev & ops, respectively), experiencing firsthand how dev and ops teams often clashed—even as friends and close collaborators.
  • Quote: "Even though we’re very good friends... development organization hated the operational organization and vice versa... So the starting point for Datadog was: let’s bring everybody on the same page." [12:07]
  • The rise of the cloud made this bridging urgent, and Datadog’s platform was born from those DevOps scars.

• Name Origins:

  • Servers were animal-named (production = dogs; Datadogs were production databases; Datadog17 was a notorious, problematic Oracle database).
  • "That was the name of the pain, like the old world we were trying to leave behind." [15:35]
  • The logo—a cute dog—helped with brand stickiness.

Getting Started in NY: Fundraising & Early Growth

• Bootstrapping Mindset ("Frugal"):

  • Fundraising in NY for infra was tough—few reference points for VCs, east coast fund disinterest, and fewer infra peers.
  • Early money: West Coast “smart money” said no; local angels and Amplify connections said yes. Steve Levitt was an early believer. [23:01]

• Early Lessons from EdTech:

  • Key: Solve the right customer problem. In education, the “user” wasn’t the buyer, leading to potential for false product signals—a discipline Pomel brought to Datadog. [24:00]

• Go-to-Market Strategy:

  • Targeted both developers (as users) and operations (as actual buyers, with authority to deploy and pay).
  • "We were very disciplined about not spending too much, but also having some clear signals about the value of our product through revenue." [27:03]
  • Early feedback: Month-to-month contracts to get real, rapid signals. "When they're on the month to month contract... you know immediately." [29:17]

• Product Positioning:

  • Framing as “infrastructure monitoring” (an established budget line/category), instead of a vague new "shared data platform," was key:
  • "We grounded the future that we're building into the past that our customers were already used to." [30:51]

Scaling and Product

• Scaling Pains:

  • For the first 6–7 years, infrastructure scale and reliability took all resources. Not until year seven could they add a second product.
  • "We kept scaling the infrastructure, optimizing the code... so they forced us to figure things out and get everything in order." [32:50]

• Efficiency and Metrics:

  • Relied mainly on revenue retention KPIs: month-to-month revenue growth and the ability to get near-instant feedback on product fit. [29:17]

Exit Alternatives & Going Public

• Acquisition Offers:

  • Received offers spanning tens of millions to tens of billions; decision: keep going if they still enjoyed it and could see another 5x–10x growth ahead. [35:55]
  • "The framework we've had to look at this was: do we like what we're doing? Do we want to spend... at least five more years doing it?" [36:21]

• Public Company Life:

  • Little changed day-to-day: already disciplined about profit and operation before IPO.
  • More structured rhythm: quarterly earnings, more investor interaction, but similar overall time commitment. Public/private strategic choice is different now, more founders can stay private longer. [37:44]

The Current & Future Frontier

• AI and Observability:
  • Now focused on automating everything: "Would be so much better if the systems fixed themselves and didn't wake you up in the night... These are the things we're working towards today." [39:43]
  • Changing role of developers and systems—new architectures driven by AI are reshaping product needs in observability.

New York Ecosystem & Advice to European Founders

• Strengths of NY Tech:

  • Access to real-world customers (finance, healthcare, media, etc.) makes it easier to get practical input and market traction.
  • "Problems you hear [in the Bay Area]... might be imagined or might be temporary as opposed to being the harder, deeper problems that the older companies, bigger companies are facing." [41:12]
  • Funding and talent depth have grown tremendously in ~15 years—big infra companies, more investors, better-developed ecosystem.

• Advice for European Founders:

  • Huge transformation in France and European startup scenes—financing and talent are now available.
  • But, to realize full potential, founders should "move half your founding team to New York" and focus on the US market once initial fit is found. "For the same amount of calories spent, you're going to get much more traction, much more revenue in the US." [43:31]

• Reflecting on Datadog’s Success:

  • Pride in independence, public company status, and joining the S&P 500:
  • "It's wild to think that we're in the S&P 500 now... thinking back to our humble beginnings in 2010." [45:16]

Notable Quotes & Memorable Moments

• "The real computer that made me completely fall in love with programming was a Commodore Amiga." —Olivier Pomel [01:00]
• "It’s all about getting the others to do the work and show up and build things that you can't build yourself… Even more true today." [04:15]
• "Maybe foolishly at the time, believed that startups would still be around and there would still be a lot to build in technology." [07:29]
• "The starting point for Datadog was: let’s bring everybody on the same page.” [12:07]
• "That was the name of the pain, like the old world we were trying to leave behind." (on the name Datadog) [15:35]
• "In New York, it's the real world. It's not a tech company world... getting real signal about what the market needed was maybe easier than it would have been on the West Coast." [26:33]
• "If customers stop paying for it, it means it's not working... when you don't charge for things... you can lie to yourself pretty easily about what's working and what's not." [27:25]
• "When they're on the month-to-month contract and they take the credit card off, you know immediately that the product's not good enough or you didn't do something right by them, and you need to fix that." [29:17]
• "We grounded the future that we're building into the past that our customers were already used to." [30:51]
• "The first, I would say six, seven years of the company were us trying to keep up with the growth as best as we could." [32:50]
• "We had a number of acquisition offers over the years... and every time that happened, my co-founder and I actually spent some time considering it... In all those cases, the framework we've had was: do we like what we're doing? Do we want to spend at least five more years doing it?" [35:55–36:21]
• "Would be so much better if the system fixed itself and didn't wake you up at night." (On the promise of AI for observability) [39:43]
• "As soon as you have a little bit of product market fit, you need to invest heavily in the US." (Advice to European founders) [43:31]

Important Segment Timestamps

• [01:00] – First computers, Amiga, early programming
• [03:12] – Open source & VLC, lessons from community
• [04:40] – Move to US; IBM Research
• [06:07–07:29] – NYC during dot-com boom/bust, decision to stay in tech
• [10:50–13:12] – Origin story of Datadog and co-founder relationship
• [15:35] – Name & logo origin (Datadog17 painful Oracle database)
• [20:29–24:00] – Fundraising challenges, angels, early strategy
• [25:05–27:35] – Product-market fit, discipline from edtech, focusing on buyers
• [29:17] – KPIs for product fit & fast feedback
• [32:50] – Scaling frugally, growth challenges
• [35:55–37:19] – Acquisition offers vs. IPO, deciding to stay independent
• [39:43] – AI's impact on observability
• [41:12] – NY tech ecosystem advantages
• [43:31] – Advice for European founders
• [45:16] – S&P 500 milestone

Tone & Style

Olivier is candid, reflective, and at times wry, sharing stories of both triumph and struggle. The conversation is fast-paced, pragmatic, and rooted in building real-world solutions, punctuated with concrete lessons from tech’s last two decades.

This summary should provide you with a comprehensive understanding of the episode’s substance and spirit—even if you haven’t listened.