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

Episode Date: September 20, 2025

Today I’m joined by Olivier Pomel, cofounder/CEO of Datadog. We trace his path from French open-source tinkerer to NYC founder, the dev-vs-ops friction that sparked Datadog, finding product-market fit through integrations, and the choice to stay independent en route to a 2019 IPO and S&P 500. Olivier shares scaling war stories, culture and GTM lessons, and what observability means in an AI era. If you build software—or companies—this one’s packed with playbooks, from hiring to pricing to platform bets that work. Learn more about your ad choices. Visit megaphone.fm/adchoices

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Google Fi Wireless is not subject to data traffic deprioritization during times of high network usage. Olivia, thanks for coming on to talk to us today. Hey. I want to go all the way back, but I want to do it in a nerdy way where I want to ask you the first computer that was yours, either the first computer that you had access to or the first computer where this is my computer and I'm going to start doing stuff on it. Like, I want make and model stuff. Do you remember that?

Yeah. So, you know, I grew up in France. and they used to be that this category of computers that used to be bought by schools in France and my mom was a teacher so we got one at home. It was a Thompson T-O-770 with 70K of memory and I remember it very distinctly. So that was the first computer I had at home and I could actually use as much as I wanted was in my bedroom. But the real computer that made me completely fall in love with programming was a Commodore Amiga

at the time. mentioning growing up in France, were you on Minitel, I'm assuming? Of course, we had a Minitel, you know, I think it's a, for those of you who are listening to us, who are not familiar with the Minitel, it was the Internet before the Internet in France. Basically, every single household had a VT-100 terminal that they could connect to a remote system. And it was used as a phone book replacement initially and then all sorts of services and anything you can imagine. would actually happen avoid the Minitel. It was in the 80s.

Right. As late, I was in France in the late 90s and there were still advertisements for mostly adult stuff, but like Minitel advertisements for numbers to call up or whatever. But do you remember the transition in France? I don't know if I've ever asked anyone this. From when the web comes up and how people in France were like, how is this different than Minitel? Do you remember that transition at all?

Yeah, I think, look, the web was always exciting. in ways the minitel was not because minitel was text mode. And so even the adult stuff on minitel was text mode, which required, I think, quite a bit of imagination. Whereas the web from the very beginning was very graphical. So I think it took off indefinitely of the minotel at the time, even though everyone had a minotel at home. So you did go, you went to college for computer science, correct? Yeah, in France, yeah. And you were involved in the late 90s on a lot of open source stuff like a VLC player. I think you're an author on.

What were some of, what turned you on to open source as like a movement or something to participate in? So what turned me on to computing in general, I think was graphics and programming graphics. So I saw that with my Amiga, as I mentioned earlier on. In Europe, there was this thing called the demo scene.

It was people mostly from Northern Europe would program incredible graphics on fairly low-powered computers. at the time. And I was so impressed by that that I started programming and starting to see what I could do to actually do 3D rendering and things like that. That's how I came to work on VLC or VDoland, as it was called at the time, which was a project at my university to stream video on a campus. I wrote the very first version of VLC, but I should say I can't take any credit for all of the amazing success that followed. It was generations of students, the programmers

after me that open source did, that made it so, so good and so successful since then. Well, what lesson did contributing to that? As you're saying, like it lives on beyond your initial contributions. Is there anything from that that has sort of stuck with you as your career has evolved? Well, I think the lesson is always, it's all about getting the others to do the work and show up and build things that you can't build yourself. So I think it's been true at the time. It's even more true to that data dog. You know, we have. have, I think, more than 6,000 people are contributing, and I don't write any code anymore. So what brought you to the States and your first work at startups?

So I came to New York in 1999, and what brought me to the States with an end-of-study co-op at IBM Research. So, you know, they used to be, I mean, it still actually is a pretty sizable research center in upstate New York from IBM. I think at the time it was by far the best research center in the US. I think now you could argue that that mental goes to

meta or Google or AI lives like OpenAI. But at the time it was a really, really exciting place to be. So I came for what I thought would be six months and I'm still here, you know, more than 25 years later. So it's been a while. So you're coming to New York City at the tail end of the dot-com bubble sort of bursting? Yes, exactly.

And I actually didn't stay that long at IBM, so I stayed a year and a half. And then I did enjoy the tail end of the dot-com, boom, and then the full extent of the dot-com bust after that. So I leave through it all. Well, explain that to me. So you come here to, hey, there's all this excitement happening in tech, and then all of a sudden, like the literal air goes out of the bubble or whatever. Do you remember like, oh, maybe I've made a mistake? Or what was the feeling like in New York City around the bubble ending?

Well, I should say, first, the feeling in New York City in 1999-2000 was amazing. Like, you know, things were booming. The cultural scene was exploding. You know, the expression, like, party like it's 1999. That was exactly like that. Did you happen to go to any of those pseudo parties? Remember sudo.com? I don't know if you remember that.

No, not pseudo parties. But, you know, I, so it was a very exciting times. The startups I worked at were extremely cool. I learned a lot of lessons there, you know, about what to do, also what not to do, you know, when you build a company. And after that, you know, we did see the market crash. And like everything else, like when it starts crashing, it doesn't look like it. Like, you know, it was initially people were questioning whether, oh, we're just slowing down a little bit, or the economy tech and it needed a bit of time to breathe, you know, et cetera, et cetera. You had all these rationalizations at the time. And then it became very clear that things were going down, you know, pretty fast.

Was it difficult? Like, did you see people, I've talked to people that are like, well, lots of my friends left tech as an industry because they thought it was like a fad that it ended or something like that. Was it difficult to then keep working on projects, or was there like an undercurrent of people's, like, true believers? So I was very much into startups. And so I worked for one startup that was amazing that didn't go so well when the economy started turning. He started working for a few other startups after that. I would say where it became quite dark was, you know, shortly after that day was September 11th. And then, I don't know if you remember, but a month after September 11th, there's a plane. that crashed in Queens also.

Yes, yeah. And I remember those were dark times. I remember at the time, nobody thought technology would be worth anything ever again. And the whole employment market contracted. For people like me, you know, who were on a work visa,

you had to find a new job pretty quickly if you wanted to stay in the US. So all of that, I think, was pretty stressful. I did decide to stay because I found New York City as a city and as an ecosystem so excited. And I, you know, maybe foolishly at the time believe that startups would still be around and there would still be a lot to build in technology. And so I started, I joined another startup in education of all spaces.

And then it ended up being a very successful one. And after that, you know, in 2010, I started that dog. But that's what ended up becoming Amplify, the education startup? Is that? Exactly. Yes. It was education selling to school. So initially, K-2-3 and not K-12.

which was very exciting in many, many different ways, also very difficult in other ways. Like, you went building a successful business in public education is really difficult. Can I ask why not go to Silicon Valley? Why stay in New York? Now, you just said that you love New York, you love the ecosystem. But, like, can you go into detail on that in the sense of, like, what the obvious thing to do, if you need a job, go out to where the tech jobs are maybe more prevalent? Why stay in New York?

What's the difference to New York for you? Well, so first of all, I'm a bit of a city person. So I grew up in the Paris suburbs, spent time in Paris as a student, then moved to New York after that. It was like New York was kind of a dream destination for me at the time. And I sort of need a city with all of its diversity to basically thrive. And New York has plenty of that. It was obvious when we started Datadog that it would be way easier to get the company funded in the Bay Area. But my co-farner and I, my co-founder, by the way, is also French and also came to New York in the late 90s.

We did enjoy leaving the city quite a bit. And also, you know, through that other company I had mentioned earlier, which is now Amplified Education, we also had a great network of engineers and product people that would work with us and for us at Datadog. And for that reason, that's the place we chose to get started at. We can come back to that a bit later. I think in the end it probably made things more difficult at the very beginning, but way easier after that. And so I do not regret making that choice. So let's talk about the founding story of Datadog.

So you meet your co-founder at Amplify, correct? Yeah, we actually met even way before that. You were friends first, right. Yes, we actually work at different startups together. Before that, we actually work at IBM Research together. And before that, we met briefly in college in France, where I was part of the team that ran the Compass Network. And he was, my co-founder was code hacking into the Compass Network. and he was basically court-martialed by the student body and sentenced disconnection,

and I carried out the sentence and basically went to the basement and disconnected his internet. You exiled him from the network. Yes, exactly. That was the whole extent of a relationship in college. And then we found each other again at IBM Research, basically sharing the same office, and that's when we really became friends. But then who ended up at Amplify first or who got the job at Amplify and brought the other So after IBM, so he went to a startup first and then brought me in, and then I went to Amplify first and then brought him in.

That was the deal. And the full story is, so Amplify, we went there. So I was there for about eight years. So I started there in 2001, 2002, and then left in 2010 to start Datadar. And during that period, the company grew from basically a handful of employees to maybe five or six hundred, something like that. And my co-founder, Alexi and myself, actually built all of the technical teams there. So I used to run development, and my co-founder, Alexi used to run operations, so technical operations. And so we sat on both sides of what was known then as the Devonops divide.

And we ended up in this situation where, you know, even though we're very good friends, and we hired everybody on our teams, and we had a no-jerk policy for hiring people on our teams. we did have the development organization that hated the operation of the organization and vice versa, and we spent our whole time dealing with finger pointing and people trying to escalate issues. And so the starting point for Datadog was, you know, let's bring everybody on the same page,

let's get everyone into the same platform, speak the same language and solve problems together. And it turned out that it was actually a critical part of cloud adoption. And we started Datadog right at the time when the cloud was starting to explode. and that's, I would say, it would really make us successful.

You're saying you're on the side of the, two sides of the DevOps divide. So, like, your teams were sort of, like, at cross purposes and maybe, like, rolling their eyes at the other and, like, not liking the other and putting out fires for one or the other. So, like, those scars,

would you say that's, like, sort of the inspiration for data doc? Exactly, exactly. That's what drove us to get started. started with data. And, you know, I mean, there's good reasons for which those teams disagree, right? They are put in different situations, different roles, different goals. And you end up with these, or you ended up at least, I think it's largely gone now in the, in the field.

But you had these consideration of, you know, developers thinking that ops are too slow. And then ops thinking that developers don't know what they're doing, they just throw stuff over the wall, you know. So I think getting everybody to speak the same language, bringing them in one place, I think went a long way towards getting. read of that. And, you know, today, so we've done that for maybe 10 years and the emergence of DevOps between 2010 and 2020. And then we found a lot of the same issues between development teams and security teams, where, you know, there's a lack of empathy for between development

and security and vice versa. People don't respect each other's qualifications and the teams can work at cross-purpose from time to time. And so we also started at the time bridging that gap and adding or releasing a series of security products and Datadog to help bring everybody on the same page. But that's more about the product in who are you building company. Yeah, so was there anything that you remember specifically either a direct, like, disagreement between you and Lexi or between your teams that led to like early product decisions

around what Datadog should be? Oh, the early policy were always that the teams were looking at separate data. They had completely separate windows into the world. And so our pitch for Datadog will bring everything under one roof. You don't have to use different tools. And you don't have to have two completely different perspectives. We can give you the same perspective. A single source of truth, right?

Exactly. So I've read that. I just want to touch on the name real quick, like where the name Datadog comes from and the story of the servers, if you want to recount that again for us. Yes, so here's the shocker. So neither my co-founder or I ever had a dog. So we're not particularly dog people.

We don't have anything against dogs, but we never had dogs. But in that previous company, we used to name servers, you know, production servers were dogs, staging servers, we have birds, you know, development where cats, things like that. And data dogs were the production databases. And Datadoc 17 was a horrible, scary Oracle database that was doubling in size every year and everybody was living in fear of. And so for us, that was the name of the pain, like the old world, the world that we were trying to live behind.

And the code name we used when we started working on the company was Datadog 17. It turns out, everybody remembered Datadog. The name was extremely sticky. So we cut out the 17s, it wouldn't sound too much like a Myspace handle. And we, you know, we actually had our designer, like, designed a really, really cute puppy for the logo. And we're off to the races with that. I just, I just lost you, I think. Maybe you're on mute.

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of like, we're going to strike off on our own. Yeah, you know, so we were starting to think about it quite a bit. And in addition to our day jobs, we started renting a co-working space on weekends. So we could go and work through the ID and maybe we had a couple more other ideas that we were, you know, keeping it. Where was the co-working space? Because this is like before, you know, co-working spaces took over. Yeah, so I think it was a community place called The Hive, and it was downtown. I think it was on Broad Street or Wall Street or around there.

And it was nothing fancy. You know, these were just desks in the middle of the spot. There was nobody there on weekends, really. So we could just go there and spend a little bit of time talking, working, whiteboarding, doing some stuff on the computers, and that was it. So we did that for a few months. until we thought that we had enough to really get started. And we gave notice and started working on this.

We then moved into our first real office was another co-working space, which was, I think it still exists called Green Desk in Dumbow. Guess what? I have still a desk in Dabo. Yes, green desk still exists. Oh, wow. I think it was the proto we work, right? I think it was...

Yeah, no, absolutely. Yes. Founded by Adam Newman also. Yes. And his partners still own it as far as I can tell. I've tried to interview them a couple times, but I don't think they want to talk about that. Anyway, sorry. Yeah.

So, and that's exactly where we started. And, you know, we were off to the races. By off to the races, however, so this is what, 2010-ish, you're saying, right? Yeah. Great financial crisis is happening. What, again, and coming back to this argument of why do it in New York, like Infra in New York as opposed to out in San Francisco, was it hard raising money? Was it hard convincing people in New York City?

This was a good idea. Explain to me like that moment of trying to fundraise. So yes, it was really hard to raise money. I think, I mean, for one thing, you can't exclude the fact it was probably because I was not very good at it, like I had never done it before. We split the work with my co-founder, and I was the one who was tasked with raising money, unfortunately, and it was not easy. One problem, as I think you alluded to, was that in New York, there was not a lot going on, by the way, of infrastructure. There were a few, like, super, super early companies, like, I think MongoDB

was already around. But by and large, like, what you found in New York was more, you know, e-commerce or, you know, things that were fashion-related and travel-related. But, you know, like Etsy was coming up at that point and, yeah, Kickstarter and stuff like that. Yeah, media, maybe, maybe a little bit of fintech also. But in infrastructure, not much. And so when we talk to VCs, so we talked to some VCs on the West Coast, who at the time were not really interested in investing on the East Coast. I think that changed quite a bit now, but at the time it was a case. And when we talk to VCs in New York,

maybe they liked us, maybe they liked the idea, but they quickly realized that they didn't understand the competitive set we're going against, or we'd have a competitor slide, you know. And first of all, there were many companies out there because what we did was known for being a crowded space, but also the VCs in New York didn't really understand who those competitors were, so it was a big turnoff.

I should mention also that neither Macauphin or I, had direct experience in the field that we were stunning. Like we didn't run systems management companies before. We also didn't work at the hyperscaler. Like we didn't come out of Google, for example. So all that to say, the smart money on the West Coast didn't invest early on. We got some of the New York VCs to invest a little bit. And mostly we relied on the angels that had invested.

in our previous company, so Amplify Education. To begrudgingly invest in Datadog, I think many of them didn't really understand exactly what they were signing up for because they didn't understand all space so much either. But no, thankfully, you know, we made the rounds after that easier. Do you want to shout out anyone that was a true believer early on that you're like, thank you.

You know, angel hands. Well, I mean, look, there was one angel that, that was actually lived on the West Coast but had invested quite a bit in Amplify Education. His name is Steve Leavitt. I don't think he invests anymore. But it was the first

to sign a big check for us that was really good. Our former bosses that Amplify invested too, which were pretty good, pretty validating as well and important for all the others who came after that.

And we got a couple of funds in New York to invest too, in a contour, which is a fund in New York. We got Arrit with a small check. These were the first funds to say yes, which was also helpful to us.

I will say, you know, the bulk of the investors that have been with us afterwards and have been on the board came at the following round. They came at the A round, not at the seed round, we raised initially. So while getting from the seed to the A, you were always known, I believe, as being very frugal. So getting the product out there, what was your strategy for going to market and trying to find product market fit? Yeah. So going back to what I was saying, so we were not very good at fundraising. And so quickly, I mean, I internalize that if our secret source would not go, it's not going to be that we raise way more money than everything than everybody else

and we can brute force the problem. I thought that we had to do two things. One is make sure we solve the right problem, a real problem for our customers. And that's something we had learned to do in education before. You know, in education, what's really interesting is that your users are not your buyers, and so it's very easy to get the wrong signal about what the market needs. Or it's very easy also to build a lot of functionality that your users are not going to use at all. So we were very disciplined from that era about building, you know, useful things,

things that our customers actually need it. So we are very scared of not building the right things, and we spend most of our time talking to everybody who would want to talk to us on the customer side to make sure we built the right product. And you mean developers specifically? Were you targeting developers first and then the people that would be the decision-makers second? Yeah, we were targeting the users, like the developers.

And developers or people who manage the developers, you know, who would be able to sign the first checks. Right, that's what I'm saying. Because I've seen you say the strategy was, like meet developers where they are, but the developers maybe don't have the authority to sign the checks, as you're saying. So how did you kind of weigh that balancing? So there's a very important distinction here, which is there's developers and then there's operations. Operations typically sign checks because they buy infrastructure, like they buy servers at a time, they buy the cloud,

they're paying bills for things, whereas developers are used to building more than buying things. So very early on, we made sure that we were adopted by developers and operations, and also we were talking to people in operations who had the permissions to deploy us on the systems and also who knew who to sign up for infrastructure and pay to be a for the infrastructure, which was very important. It's an issue, by the way, many companies that build products for developers have, which is it's really hard to monetize developers only on, and it's really hard to build good businesses, you know, that way.

So as I was saying earlier, I just want to come back to that. So we internalize one thing, which is building the right product, talking to customers, which, by the way, was easier to do in New York than maybe in the Bay Area because New York is the real world. Like, you know, it's not a tech company world. Or at least it was not at the time. And so getting real signal about what the market needed was actually maybe easier than it would have been on the West Coast. The second thing we internalized was that we might need to be. profitable very quickly in case we couldn't raise more money. And so we were very, very disciplined

about not spending too much, but also having some clear signals about the value of our product through revenue. And that's something that stays with us today. We have this very clear feedback loops where when we build new product, we start charging for it pretty quickly so that we get very hard to ignore feedback about whether or not it's working. If customers start paying for it, it means it's not working. When you don't charge for things or when you bundle too much, you can lie to yourself pretty easily about what's working and what's not. Yeah, I correct me if I'm wrong, but I saw, I think it was on Logan's pod that you became a $40 billion company while only burning like $25 million in capital. Do you, if people are out

there listening saying, yeah, that's almost like bootstrapping sort of thing. Like, that's the way to go. Is there, was there something unique for how you were able to do that without brute forcing it, like you're saying, where if maybe you just got lucky and there are other situations where brute forcing is the only way to go? So, I mean, I don't know. I don't think I'm very good at brute forcing in general, so maybe others can do it. I think in our case, we, I mean, one is we try to grow responsibly. Maybe we could have gone faster. Look, it's possible. There's another world where instead of being a $40 billion company, we're an $80 billion company because we spent more and it's possible, who knows. In our case, though, we were very careful about

having building a company that was efficient. So we were efficient in the, who we built a product, were efficient in who we went to market, you know, try to not overbuild the sales organizations, for example. Some of that were lessons that we've learned through the dot-com boom and bust. You know, when we saw companies spending lavishly on things that didn't matter, that's, these are things that stayed with us. And maybe we see some of that again today. It's a little bit bubbly right now with AI. So we were very careful about all of that. We'll come back to that. But you're saying you learn those lessons from the Docon bubble of not spending on things that don't matter. If you could pick one KPI that told you were on the right track, do you remember what that might have been?

the main KPIs we looked at were retention on the on the product side and mostly revenue retention so basically our customers that were paying us last month till paying us this month and how much are they paying us versus last month that's the the main thing we've been looking at and by the way to get a very clear signal on that very early on we decided that we would get most customers not on a three-year contract not even on a one-year contract but on a month-to-month contract And as a result, we get the signal very, very, very quickly. The feedback loop closes very, very quickly. You know, when you sell for three years, it's very easy also to ignore the fact that

some customers are not really deplored and not seeing value because you imagine you still have time to prove yourself. Whereas, you know, when they're on a month, a 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. So we wanted to optimize for that. you mentioned that even investors didn't understand the product even if they wrote a check to you. And some of your early challenges were going into organizations and explaining how bridging this divide is important.

Did you learn any lessons in terms of like it's clear to you this is a product that makes tons of sense, but we have to educate the customer? what would you say to people that are dealing with products and startups that it's like it's not blindingly obvious to people that they're selling to are trying to raise money from that's a correct question and actually we struggled a lot with that initially because we positioned the product as a data platform that you can use to share data between devon ops you know for collaboratively working through problems and when we started releasing that to our users initially we got a lot of usage, a lot of love on social media, but everybody forgot to come back and nobody paid for it.

And so clearly, the message was not getting across. What we've done at the time is we decided to ground the future that we're building into the past that our customers were already used to. And so we decided to call the first product not just a data platform, but an infrastructure monitoring product. even though it didn't really look like the existing financial monitoring product, had a number of differences, number of things didn't do yet, things like that. It was close enough, and it was something our customers knew they needed to have,

and it had a budget line, it fit into an existing category. And so it allowed all customers to basically start using the product, come back, explain to their bosses where they needed to pay for that, and that allowed us to be successful. That's something we've kept doing after that. when we've brought new products to market, very often those products correspond to the future state of the world, as we imagine is going to be, like, you know, in three, four, five years. But we always ground these products into existing categories that our customers are used to

and they know how to evaluate and there's a fixed competitive set, you know, so it makes everything easy. Let me ask a scaling question because essentially, again, what your product is is observability and dealing with insane data volumes. So is there something that you learned about, again, being frugal, but not allowing the product to become unreliable in people's eyes and just scaling in a responsible way? Yeah, I mean, look, 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, basically. So we kept, you know, scaling the infrastructure, optimizing the code and everything else. We had the good fortune of being adopted by the largest growing internet properties at the time and we needed to scale with them. And so they forced us to figure things out and get everything in order.

So yes, it was hard. Like that would say, even though our long-term plan was always to build a platform that would bring together many different categories in the one roof, it took us seven years from the start of the company to start working on our second product inside that platform just because we're so consumed with scaling and keeping the lights on for the existing product,

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Canva, the thing that makes anything a thing. You don't know this, but around the time I launched my podcast eight years ago was around the time you were going public, and that's why I know the three pillars of observability, because I read ads for y'all back in those days. I did not know that there was a little bit of drama around going public in 2019 that Cisco, I think, came in and dangled some money at you, and you said no, and you went ahead and went public, and that worked out. I was talking to somebody about this yesterday, the debate in a broad sense between take the acquisition versus try to be an independent company and keep going.

In retrospect, what lesson did you learn from making that decision? Yeah. You know, we had a number of acquisition offers over the years, you know, so the first one in the tens of millions and the last one in the tens of billions, let's call it this way. and we every time that happened my co-phone and I actually spent some time considering it

I think it's always a good time when that happens to try and think back about what you've been doing what you're going to be doing next and what you're doing for what reasons I think in all of those cases like the framework like 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 you know, 100% of our time, 150% of our time. And then do we think there is another 5x to 10x growth from where we are today? And we want to basically deliver that. And in all situations, that's what we thought. And I think, you know, you could have argued that, you know, before going public,

it was harder to imagine the 5x to 10x, but for us it was very clear. And I think, you know, when you look at where we are as a business, we're probably at 10x where we were in terms of revenue, like shortly before in public. So I think we were there, you know. And that's why, by the way, we're still here, like microphone or not, you're still, no, 15 years later, we're still very, very active driving the business and building what's coming next. Well, because it sounds like you still enjoy it, which helps.

But, you know, there are some founders that regret going public. Like, what changed, like, on your day-to-day level once you were a public CEO versus a non-public CEO? And is there anything that you do regret about that? Or did you just roll with the punches and evolve? You know, I would say not much changed on the day-to-day, because most of what we do as a business, you know, it's like hiring the right people,

solving the right problem for the right customers, getting the order, like all of that's the same. Like there's no difference. We were also running a fairly disciplined organization before we went public. You know, it's not like, you know, we went public and a little of a sudden we had to figure out how we become profitable. We were profitable before we went public. And we always had all of that discipline inside the companies.

That was not a problem. I would say there's more of a like a rhythm to being a public company. Like you have the quarterly rhythm. You have maybe one week, two weeks, every quarter that sort of disappear into planning the earnings, doing a follow up with the investors, make sure you have the right messaging, et cetera, et cetera, et cetera. in a perfect world, would I want to use that time

for building products instead? Probably, yes. But I think if I look back at what happened when we're private, I did also spend quite a bit of time with investors because I had to get investors updated. I had to get new investors interested in the company. So, you know, all in all, it was not all that different.

I would say today, the situation might be a bit different from what it was five, six years ago. I think now it looks like, has clearly an opportunity for the very best private companies to remain private for a very long time. And I think it gives them a bit of flexibility in terms of how much they want to invest. Like I think it's harder to invest heavily in a public company, or at least to change your investment profile than it is in a private company.

And maybe that's something that might be interesting to some founders today. very broadly in this AI new world where observability still matters and things like that. What's the frontier problem that most obsesses you right now that maybe Data Dogg really wants to work on? I mean, it's automating everything with AI. A lot of what we do, what we used to do was we wake up people in the night when, something needs attention, so they need to fix it. It would be so much better if the system fixed itself and didn't wake you up in the night. Then maybe in the morning after you get a text message that says, hey, there was initially

last night. I fixed it. You should check it out. And so these are the things working towards today. It's also fascinating to see how much programming is changing with AI because we're either are relying on models that don't need to be programmed that are emergent or we're writing the code itself. looks like old code, like imperative code, but we're writing it with agents.

And I think all of that completely changes the role of not only the developer, but also all of the systems downstreams on the developers, with the observability being one of the most important in that new world. So I think there's quite a bit that needs to be built there, and that's what is exciting to us right now. Last couple of questions. If people are watching the video, they can see the Empire State Building in the background. you all are still in New York.

So the first half of this would be, what is it about the New York tech ecosystem? You've sort of touched on it a bit that you think makes it unique and that can make it go toe to toe with not only Silicon Valley, but any tech ecosystem in the world. So I think what makes it unique

is that there's an incredibly large density of real world customers in New York. And so you can plug into those customers, get good signal about audits they need very, very easily. In a way that is more difficult in the Bay Area, because in the Bay Area, you have a higher density of tech companies, new companies. And so the problems you hear when you talk to new companies might be imagined or might be temporary as opposed to being the harder, deeper problems that are that the older companies, bigger companies are facing. So that's one thing. The other thing is, you know, it's a great place for, you know, talent from all over the world to go. It's a great place also to have companies, to see companies that have a foot in the U.S.

and a foot in Europe, for example. You know, that's our case. You know, we have quite a bit of engineering in Europe as well. And that's a great place for us to run the business. Overall, I would say the ecosystem has developed very positively in the past 15 years. You know, so when we started, we mentioned like there was no or very few infarsecreture companies. Now there's a number of scaled companies in New York and infrastructure. There's MongoDB, which is quite big. There's us. There's a number of others. There are large offices from

meta, from Google, with a lot of infrastructure-minded people, a lot of really, very good engineers. And there's a very large number of startups that are emerging as well. So the ecosystem is much, much, much deeper than it used to be. The funding also used to be very hard to come by, especially for non-traditional businesses for New York. Now, you can say that the investors from anywhere on the West Coast, anywhere in the U.S., really, or in the world, are willing to invest in New York as well. And so that's not a problem anymore. So overall, I would say the ecosystem is deeper, wider, more resilient, and it's been good for everyone. Final one is actually the inverse of New York.

If someone listening to us right now is about to start a company in Europe specifically, what would you say? say about being a founder in Europe these days versus do you still feel the need to come to the US if you're being serious about founding? Like just your thoughts on the European startup scene. Yeah. So I talk maybe about the French ecosystem because I know it better than the others. I will say that when I when I left for France for the US, there was not much going on that was exciting technology in France. When I started. When I started. data dog in 2010, it would have been impossible to start something similar in France, really get it financed. It was way too hard. I would say today you can start. There's plenty of financing.

There's plenty of people who want to work in startups who are qualified. There's been already a few generations of a few cycles of startup that have become successful, that have exited, that have reinjected people in the ecosystem. So all of that is real and there's a great talent pool there. I would say, though, that and I said that to all of the European companies I personally invested in, as soon as you have a little bit of product market fit, you need to

invest heavily in the US and you need to move half of your founding team to New York, maybe, and go to market specifically in the US. Because the US market is bigger, it's bigger, it's easier.

Customers are going to move faster. They're going to spend more and there's more of them. And so as a result, you know, you spend for the same amount of a calorie spent, you're going to get much more traction, much more revenue in the U.S. And just growing in Europe would put you at a disadvantage compared to the company that can grow in the U.S. Last thing, congratulations on joining the S&P 500. And, hey, like going public and staying independent has worked out for you all.

Well, thank you. Thank you. It's wild to think that we're on the SNP 500 now, you're thinking back to our humble beginnings in 2010. Some follow the noise. Bloomberg follows the money, whether it's the funds fueling AI or crypto's trillion dollar swings. There's a money side to every story. Get the money side of the story. Subscribe now at Bloomberg.com.