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As we gear up for SaaStr Annual 2025 in the SF Bay Area on May 13-15, we wanted to take a look back at some of our most iconic speakers and sessions from over the year, that we can still learn from today. Today, Datadog is at $2.5B+ ARR. It's a suite that most of us use to manage how our software operates. But where was it at $100m in ARR? A huge deal, yes but just getting ready ... to grow even faster. To go much more multi-product, and more enterprise. And Olivier Pomel, CEO and co-founder of Datadog, was kind enough to join us in Paris at the first SaaStr Europa when they were at about $100m ARR. We can look back at how Datadog scaled in this incredible and honest deep-dive. Pomel focused his session on: Making your SaaS startup customer-centric: How event marketing has helped him integrate his engineering and sales teams. The critical role of product managers and ways to utilize them effectively. Lastly, he provides his piece of advice to all founders that are looking to become customer-centric. [embed]https://youtu.be/sNdUX7XpyRI[/embed] Transcript CEO @ Datadog | Olivier Pomel Co-Founder @ Plato | Jean-Baptiste Coger JB: I'm some super privileged and excited today to have Olivier here has. So for those who don't know him, Olivier is the founder and CEO of Datadog. So why don't you tell us a little bit about what Datadog does? Olivier: Sure. Hi everyone. It looks like it's working. So, Datadog what we do is...we do monitoring and we do analytics for application and infrastructure in cloud environments. So basically any company that is running infrastructure, servers, applications and that is running it in cloud environments is a customer for us. And the good news is that just about every single company is busy right now moving from legacy to public and private cloud. JB: Cool. It's a very niche environment. How did you end up in that world? Olivier: So I started the company with a longtime friend and coworker of mine, and by the way we started the company in the US, so despite the French accent and all of that, I've actually lived in the US since 1999, and it was natural for us to start a company in New York. So what happened to us is that we had ... Previous company, I was running the development team and he was running the technical operations team or ops team. And the story there was that we knew each other very well. We had worked together in four different companies before that. We hired everybody on our teams. We scaled them from scratch, basically. We had a strict no jerk policy and yet we ended up a couple of years down the road with developers that hated operations, operations that hated developers, finger pointing, all of the things that you can imagine. Olivier: So the starting point was, why don't we give all of those teams the same viewpoint? How do we get them aligned and understand their problems are the same way? So that's how we got started. We started the company in 2010. We didn't quite understand at the time that the cloud was going to be so big. We thought, hey, it looks cool. Why don't we build it for the companies that are moving into these new cloud environments? Maybe that's a great way to get in. The other thing we didn't quite realize was that bringing dev and ops together, bridging those two teams, was not just a feature of the new world. It was actually one of the key reasons why companies and enterprises would be moving from legacy IT to the cloud. So we ended up right in the center of it. And when you fast forward today, the company is about 700 people. We've been doubling the size of the company every single year and it turns out everybody's moving to the cloud and everybody needs to understand what's happening to their systems and applications. JB: Cool. So living the problem first, that's what the root of Datadog. So we're here to talk about building and selling a product that customers love. So extremely relevant because I think, not only your self-claim, but also acknowledged in the community by being one of those companies that actually are very customer-centric. So let's start with what is, in your opinion, a customer-centric company. What's your definition? Olivier: Right. So the one thing that's counterintuitive is that so everybody wants to be customer-focused. Olivier: The problem is that most companies end up being either sales-driven or engineering-driven and, if you want to be customer-focused, you can't be either of those. So you have basically to keep correcting what's happening within your sales teams and within your engineering teams to make sure everybody is focusing on the customer first. What this means is that if you let everything be sales-driven, you'll end up being very short term. Sales teams are great at figuring out what's going to get a deal done and very often getting the next deal done is actually not what you want to do for the long run for your customers. But on the flip side, if you let your engineering teams run on their own, I mean, you'll end up with organizations where people focus too much, way too much, on their solutions, focus way too much on the long term, what will make sense and be sustainable in the long term, and you end up bridging that gap back to the customer, which is hard. So I would say it's a struggle of every day to make sure that we go back to the customer and start from there. JB: And how did this kind of style of management or company culture started? Was it a conscious choice? Olivier: So the choice for us was ... Actually, we were forced to do it because when we started the company it was really, really hard for us to raise money. We were based in New York, which is not the obvious place to start an infrastructure company. Most of the companies in the US started in that space were in the Bay Area. Neither my co-founder or myself came from Google or Facebook so we didn't have the credentials for helping companies run at very large scale. So basically we didn't have millions and millions of dollars, we didn't have a whole suite of people that were repeating to us that we were geniuses so they could invest in our company. So we basically had to focus on the problem, like we spent really the first couple of years of the company listening to customers and trying to understand what the problem was. JB: So that's how it started. So let's come back over the course of Datadog. You've been around for eight years. Let's start from the beginning. How did it all start? So if you could give more details of these two first two years. We discussed it earlier. There were multiple phases. And how did you manage to be customer-centric in those early days? Olivier: Yep. So the first thing we did is when we started the company, even though, so I'm an engineer and my co-founder is an engineer too, for the first six months of the company, we didn't write a line of code, which took some restraint because when you start a company, you're super excited, you want to get it done, you're thinking of all those beautiful things you can build. And so instead of that, we actually spent all that time talking to potential customers and potentials users. And the thing that we found out there is that ... So first of all, when you don't have anything to sell, everybody is super happy to talk to you. You'll get hours and hours of really fantastic people at fantastic companies and I spend all that time explaining to you what their problems are, what's working, what's not working for them. They'll be extremely candid. Olivier: That changes once you have something to sell because then you're tainted, you have too much of a vested interest and you're trying to push something, so people won't open up so easily. So it's actually very easy to do that super early on. So then after that, what we did is we built the first alpha of our products. We spend six months building the first alpha, got it to deploy it into a small number of customers. We actually noticed that the product was not exactly what it needed to be. It was way too open-ended, way too general at the time. So we had to change that quite a bit. But also one thing that was counterintuitive to us was that we were really limiting ourselves by strictly getting access to our product. Olivier: So we had a closed alpha, we hand-picked the people that would actually be on the alpha, we tried to pick the best companies, the best people at the best companies. And it was actually really, really hard to get a lot of signal from these customers where we're doing that. What we ended up doing after that is we opened it up to a wide beta, and all of a sudden everything changed. Because, as it turns out, it's a lot easier for users to self-select and to go and start using your product than for you to understand for whom you're going to be the right thing at the right time and they're going to have some free bandwidth right now. And all the stars will be aligned so they can get you deployed. JB: All right, so open beta, that was one of the secrets. You're not a strong believer in the MVP, right? Olivier: No, I actually don't think, for what we do, an MVP exists. There might be exceptions, right? But in our case, we sell to enterprises. We sell a product that serves a fairly large purpose like it needs to monitor everything that's happening across the whole infrastructure, the whole application. There's a very large number of features our customers rely on, and none of these features in and of themselves are revolutionary. I mean that's not why we build a product. There are all sorts of differentiation we built in, but you need to have them, otherwise, you cannot be minimally useful. And so the way we see it is it's more of a continuum, you keep adding those features, and then at a certain point you have enough of them that customers can start buying. And the hard part there is to figure out really how you know which …

A Look Back: Olivier Pomel, CEO of Datadog at $100000000 in ARR

Olivier Pomel of Datadog told Newcomer’s Madeline Renbarger that New York City has emerged as a competitive market for AI talent in the past few years, and that it was “easier to understand how to build the right product there than it is in the Bay Area.”

Pomel said his customers, who range from small businesses to mid-market enterprises, are “all still figuring out how to adopt AI” through experimentation and early production stages.

The company is currently rolling out a second version of its chatbot. “We have to see how it behaves in the real world,” he said.

Pomel appreciates the competitive ecosystem that emerged from the AI wave across both coasts. “On one end, you have the open source databases that you run yourself, then you have the proprietary databases that somebody else runs for you,” he noted.  “The market is a pretty healthy split between all of that because there’s good use cases for all of those to exist. Any given company is going to use a mix of those.” 

CEREBRAL VALLEY NEW YORK TOOK PLACE ON JUNE 27, 2024, HOSTED BY NEWCOMER & VOLLEY

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Videos made by https://www.storycastproductions.com/

Olivier Pomel (Datadog) in conversation with Madeline Renbarger

The observability provider was down for more than a day in March. What went wrong, how did the engineering team respond, and what can businesses learn from the incident? Exclusive.

Inside DataDog's $5M Outage (Real-World Engineering Challenges ...

The mission of the Data Science team is to improve observability, efficiency, security, and drive business growth for Datadog customers by augmenting Datadog products with smarter and differentiated solutions. This includes designing and building algorithmically driven features in the Datadog app. We work across a range of applications, primarily focusing on analysis on streaming data such as <a class="css-1rn59kg" href="https://www.datadoghq.com/blog/watchdog/" data-testid="link-with-safety" data-renderer-mark="true">anomaly detection</a>, <a class="css-1rn59kg" href="https://docs.datadoghq.com/logs/explorer/watchdog_insights/" data-testid="link-with-safety" data-renderer-mark="true">error outliers</a> and <a class="css-1rn59kg" href="https://www.datadoghq.com/blog/faulty-deployment-detection/" data-testid="link-with-safety" data-renderer-mark="true">faulty deployment analysis</a>. At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. The Opportunity As an Engineering Manager II in the Data Science org, you will lead and grow a team of Data Scientists and Software Engineers to help build and improve the growing suite of <a class="css-1rn59kg" href="https://www.datadoghq.com/product/platform/watchdog/" data-testid="link-with-safety" data-renderer-mark="true">Watchdog products and features</a>. You will work closely with our engineering and product partners to prioritize and solve high-impact customer problems by augmenting Datadog products with Watchdog features. You’ll be responsible for the strategy and execution of the product while developing and growing a team of talented engineers and data scientists. What You’ll Do: <ul class="ak-ul" data-indent-level="1"> <li> Leadership: Drive the vision and strategy for the teams, ensuring alignment with the overall goals. </li> <li> Team Oversight: Provide guidance and support to the Engineering Managers and ICs within sub-teams, fostering a collaborative and high-performing work environment. </li> <li> Technical Excellence: Ensure the teams maintain high standards of code and model quality, performance, and reliability across all systems and services. </li> <li> Innovation and Improvement: Identify opportunities for innovation and continuous improvement within the systems and models, promoting best practices and new technologies. </li> <li> Talent Management: Lead hiring efforts to attract and retain top talent, ensuring the growth and development of team members through mentoring and professional development opportunities. </li> <li> Cross-functional Collaboration: Work closely with other teams and stakeholders to provide the underlying primitives required to execute the vision of the product </li> </ul> Who You Are: <ul class="ak-ul" data-indent-level="1"> <li> A proven leader with experience in building out Data Science and Machine Learning features and products. </li> <li> You have experience in managing managers, as well as overseeing teams of software engineers and data scientists. </li> <li> Experience in setting and executing innovation strategies at both technical and organizational levels to drive business objectives. </li> <li> Experienced in working and communicating effectively in a fast-paced, high growth, distributed organization. </li> <li> You have significant experience applying data science and machine learning to real business problems. </li> <li> You have partnered with product management and design teams in the past to set the vision and strategy for your projects </li> <li> You have a track record of motivating teams and managers by setting clear and ambitious goals, and providing the support and coaching required to achieve them </li> </ul> Bonus points (any of the following): <ul class="ak-ul" data-indent-level="1"> <li> Strong foundation in machine learning fundamentals, modelling techniques, and the productionization of machine learning models. </li> <li> Experienced in developing large-scale models and deploying them in production systems. </li> </ul> Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply. Benefits and Growth: <ul class="ak-ul" data-indent-level="1"> <li> New hire stock equity (RSUs) and employee stock purchase plan (ESPP) </li> <li> Continuous professional development, product training, and career pathing </li> <li> Intradepartmental mentor and buddy program for in-house networking </li> <li> An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups) </li> <li> Access to Inclusion Talks, our internal panel discussions </li> <li> Free, global mental health benefits for employees and dependents age 6+ </li> <li> Competitive global benefits </li> </ul> Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.<div class="content-conclusion"><hr> About Datadog: Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on <a href="https://www.instagram.com/datadoghq/?hl=en" target="_blank">Instagram</a>, <a href="https://www.linkedin.com/company/datadog" target="_blank">LinkedIn,</a> and <a href="https://learn.datadoghq.com/" target="_blank">Datadog Learning Center. </a> <hr> Equal Opportunity at Datadog: Datadog is an <a class="sc-bkCOcH eLQXok" href="https://www.datadoghq.com/affirmative-action-statement" data-renderer-mark="true">Affirmative Action</a> and <a class="sc-bkCOcH eLQXok" href="https://www.eeoc.gov/sites/default/files/2022-10/EEOC_KnowYourRights_screen_reader_10_20.pdf" data-renderer-mark="true">Equal Opportunity Employer</a> and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our <a href="https://www.datadoghq.com/legal/candidate-legal-notices/">Candidate Legal Notices</a> for your reference. Your Privacy: Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s <a class="c-link" href="https://www.datadoghq.com/legal/applicant-candidate-privacy/" target="_blank" data-stringify-link="https://www.datadoghq.com/legal/applicant-candidate-privacy/" data-sk="tooltip_parent">Applicant and Candidate Privacy Notice</a>.</div>

Engineering Manager II - Data Science - Datadog Careers

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Performance Improvements in the Datadog Agent Metrics Pipeline

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Olivier Vielpeau is a highly experienced professional in the field of computer science. He studied Computer Science Engineering at CentraleSupé...

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