AI at Datadog: Monitoring machines in the age of LLMs | Olivier Pomel, CEO of Datadog

"It's crucial to organize everything so that we could hook together many different data stores under one fairly flexible data model. This allows systems to adapt and grow without the constraints of rigid structures."

"Every application can generate an arbitrary large amount of logs, and that volume escalates much faster than our customers' revenue, which presents significant challenges."

"In founder mode, you, at some point, have to care about the details because it will dictate how you build a company and remain relevant in a fast-moving market."

"I have an email from Paul Graham that says, 'A platform is only as good as its first product.' This indicates the immense pressure that comes with launching an initial offering that needs to be impactful."

"We started expanding into security... by doing security on top of logs, outlining a strategic expansion to address both application and infrastructure needs."

"We started with just metrics and infrastructure. We didn't have tracing, we didn't have logs, showing the company's evolution and diversification in their product offerings."

"And that's where we see ourselves playing a role in the long term... there's different themes for Datadog and AI."

"So for example, when we think about competition, we don't spend a lot of time reading our competitors' press releases or websites. Instead, we talk to our customers and we hear what they have to say about it."

"But I do think that at the end of the day, I do see that as one more evolution in the history of innovation, which is we just do more."

"So to capitalize on AI, you need data... So it needs to be digital."

"But the way we do it is we start small and we've waited a long time to scale up teams or products as we were still searching for basically the core value or what we'd be able to sell in the end."

"Datadog has become one of the most exciting tech companies in public markets, with very impressive stats: almost 29,000 customers around the world, 2.39 billion in revenue, and a 39 billion market cap at the time of recording."

"The promise here is since we have the cleanest data... So when it's not clean, it gets fixed pretty quickly. And we are in the middle of the use cases, like the transactional use cases of, hey, I need to fix something and verify that it works. We are the ideal insertion point to automate a lot of that."

"The problem with AI in general is if you start solely with the promise of automating with AI, you're sort of on the hook to doing it all the time. And the general case for what we do is not something that you can solve with AI. I think right now we'd be lucky to solve 2%, 3%, 5% of the cases with AI."

"We're here to make sure you can solve your issues, and little by little, we'll do more of it for you, up to the point where maybe you have only 1% of the issues to solve yourself."

"Because we're not talking about creating avatars or voices or marketing copy. We're talking about detecting anomalies across mostly structured number information everywhere, which means the best way to describe it is not transformers."

"So the idea is now that this technology, including our own models, has been proven to work really, really well, what can we do with our own data? Can we actually improve forecasting for our own time series, for our own observability time series?"

"We don't do it like Apple. We don't disappear in a basement for three years and then ship a fully formed product that takes the world by storm. Instead, from the earliest days, we work with design partners, we work with customers, and we try to ship something to them as quickly as possible."

"Basically, the system is not sure. It tends to generate more false negatives and false positives."

What is "AI at Datadog: Monitoring machines in the age of LLMs | Olivier Pomel, CEO of Datadog" about?

In this episode featuring Olivier Pomel, CEO of Datadog, the discussion revolves around the company's remarkable growth from a single product to a leading observability platform. Olivier recounts the evolution of Datadog's product development strategy, emphasizing the importance of customer collaboration from the earliest stages. The dialogue then shifts to how AI, particularly Generative AI, has impacted Datadog's approach, enabling the development of innovative products like Watchdog and Toto. The episode also touches on the challenges within the tech industry, including product-market fit and the complexities of scaling AI solutions. Finally, the conversation concludes with insights on the concept of 'founder mode' and the dynamics of managing growth in a fast-paced market.

What are the key takeaways from "AI at Datadog: Monitoring machines in the age of LLMs | Olivier Pomel, CEO of Datadog"?

Datadog's success stems from a proactive, customer-centered approach to product development, diverging from traditional secretive methods. The adoption of AI technologies has transformed Datadog's capabilities, particularly in anomaly detection and operational efficiency. The distinction between launching new products versus scaling existing ones is critical in the tech landscape.

Which podcast is this episode from?

"AI at Datadog: Monitoring machines in the age of LLMs | Olivier Pomel, CEO of Datadog" is an episode from The MAD Podcast with Matt Turck.