Operations | Monitoring | ITSM | DevOps | Cloud

The breakthroughs in AI today aren’t just coming from bigger datasets and more compute; Reinforcement Learning (RL) has quietly become one of the most powerful forces in modern AI development. RL is teaching models to reason and self-correct, enabling capabilities that make AGI feel less like science fiction and more like an inevitable future.

Bad data doesn't announce itself. Datadog Data Observability gives you unified visibility across your entire data stack—from source systems and pipelines to dashboards and AI applications—so you catch silent failures before they cascade. Detect data quality and pipeline issues before stakeholders do, pinpoint root causes with end-to-end lineage, and reduce pipeline costs with job, cluster, and query recommendations.

At, Datadog launched 100+ capabilities to help customers drive autonomy and manage growing AI and security complexity. From new Bits AI, log management, and security capabilities, customers have the visibility and autonomous operations they need to detect, investigate and resolve issues across the development loop and data lifecycle. Tune in to the full keynote to catch the highlights.

In this video, you'll learn how Datadog GPU Monitoring gives ML and platform teams a single view of their GPU fleet, so they can see what's slowing down their AI workloads, fix issues faster, and use the GPUs they already have more efficiently.

"We're doing cutting-edge AI, focused on real translational impact: getting our research over the wall and into production." Ameet Talwalkar, Datadog's Chief Scientist, shares what it took to build the AI Research Lab from the ground up — and what makes DAIR different from traditional research teams. At Datadog, research ships. Recent work from the lab includes Toto 2.0, open-weights time series forecasting models ranked on leading benchmarks, and ARFBench, a new benchmark for evaluating AI on real incident data.

Datadog has always been driven by a broader vision of helping teams understand and operate complex systems. In this session, you’ll hear from Michael Whetten, Product SVP, and Abrar Hussain, Senior Director, Product Management, as they share the latest updates across the Datadog product suite and discuss how that vision continues to shape the platform’s evolution and support the next generation of AI-driven applications.

In the fast-paced world of mobile development, reliability rarely fails with a loud crash; instead, it degrades quietly through micro-regressions that erode user trust and engagement. While most companies track backend health and API latency, they often fly blind regarding the actual screen-level responsiveness that defines the true user experience. When Expedia Group underwent a major technical evolution, the team realized they lacked a consistent baseline to compare performance across platforms, leaving them unable to validate improvements before rollout.

You’re told to “go build agents” without clear guidance on what that actually means, how to do it well, or how to know if it is working. You are not a data scientist. You are a software engineer. In this talk, a Datadog AI product leader Shri Subramanian breaks down what changes when you move from building applications to building AI agents, and why familiar approaches like traditional testing and linear delivery fall short. We will explore how agent development shifts the focus from code alone to data, prompts, and evaluation, and why functional reliability matters just as much as operational reliability.

Join Datadog CPO Yanbing Li and a special guest as they discuss emerging technologies and innovation, how they impact businesses today, and the new opportunities they create for you.

Delivering great products to your customers requires a mix of evolution and consistency. To really land with users your product has to be ready to adapt and scale, prioritizing across a mix of customer and business needs. Join experts in reliability, systems engineering, and DevOps as they share real-world examples, true stories of pitfalls, and astounding impact from the experiments they have run. Learn how experienced practitioners handle failure, adapt to scale, and bridge gaps between teams to improve software performance and customer outcomes.