Datadog CEO Olivier Pomel on AI Security, Trust, and the Future of Observability

**SPEAKER_1** (0:00)
If I look back at the cloud migration, the one thing that the companies that defined the cloud at the time, so mostly Amazon, did extremely well was that they handled the security aspects extremely well. That was a big fear of everyone. I am going to share my data or my compute with just about everybody else. Won't they be able to see what I have? And they've been extremely strong about it from day one. I think it would be good for the same to happen with everything that relates to AI models today. So we can take those main fears off the table and let everybody else build with confidence.

**SPEAKER_2** (0:34)
You're listening to The AI Native Dev brought to you by Tesla.

**SPEAKER_3** (0:48)
Hello, everyone, welcome back to the AI Native Dev. Today, we have a very special guest that I'm really excited to have on. We have Olivier Pomel, who is the co-founder and CEO of Datadog. If you happen to have not heard of it, which would be very odd at the audience listening to this podcast. It's a pretty amazing company, a whole growth story. We're not gonna, we have so many things to talk about. We're not gonna cover that too much, but it was founded in 2010 Now today, it's valued as the market moves, but I think well-known for about $35 billion. It has over $3 billion in revenue, or you can make that in 2025, 29,000 customers, all sorts of massive numbers. And I think importantly, it is the leader, the pretty obvious leader in the world of observability, really formed and shaped the category on it with a long series of products that help explore new domains and new areas in a very efficient fashion. So, Olivier, thanks a lot for coming onto the show. Tell me if I, did I misrepresent or miss anything important in that quick intro?

**SPEAKER_1** (1:44)
It's all good. That's definitely me, definitely Datadog and thank you for having me. Super happy to be here.

**SPEAKER_3** (1:49)
Cool, cool. Olivier, I think there's a million things we'll talk about, but really we're going to probably sink into AI and observability. I think few people are more qualified than you to share both a perspective of where we are and where we're headed. Maybe we just start off a little bit with just some taxonomy here, right, and some definition. So when you think about observability, like how do you define this world of observability and what it contains?

**SPEAKER_1** (2:12)
Yeah. So observability, by the way, it's the worst category name to pronounce. And I do that in many, many hundreds of times. So first of all, I'm not in love with the word because it's, it seems somewhat reductive and passive, whereas a lot of what happens is very much not passive. But the observability is the modern version of what used to be called the monitoring before. And I think what it represents is obviously the idea of understanding everything you need to understand to see how an app is performing, what is actually happening inside of it, whether it's doing what it's supposed to be doing. It's an evolution of what used to be different categories. In the, like five, ten years ago, we used to have infrastructure monitoring, we used to have application performance monitoring, used to have log management, used to have network monitoring. These were all like little microcosms of little categories. And I think in part, thanks to the work we've done, those have converged into a bigger category, which is called observability. That's very much end-to-end. It goes from what runs on the servers when it goes over the network, but also what are the end users of these applications doing and what is it doing for the business?

**SPEAKER_3** (3:17)
Yeah, definitely have been a part of that journey with a variety of solutions during that time. It was interesting to see it coalesce today. That scope is expected, it's table stakes when you think about building an observability solution. And I like the point around it not being as passive as it implies. We're going to dig into that a little bit when we talk about AI and agents. So that's the definition of observability. And I guess at a high level, what do you see as the key opportunities for AI within this world? We're going to start a bit meta and then we'll drill in.

**SPEAKER_1** (3:48)
Yes. So it turns out there are big opportunities at every single layer of the stack with AI. The first one, I would say, I would call it maybe the most straightforward and boring one, is that there's just more applications being built with AI, and those applications look a little bit different in terms of the stack they use. So more GPUs, for example, a lot more data, things like that. And we see that today already, we see companies that are building AI, that are consuming a lot more infrastructure, building with a lot more data. So that's the level zero of the opportunity there.