Scale coverage as fast as you ship with Datadog Bits Testing
Engineering ships faster than ever with AI writing more of the code, but test coverage has to keep pace, or users find the broken flows first.
Operations | Monitoring | ITSM | DevOps | Cloud
The Place Where Modern Operations & Technology Come Together
Introducing Scout 1.0: A new SRE model powering Bits AI with frontier performance at a fraction of the cost
Today, we’re introducing Scout 1.0, Datadog’s first AI model specialized for investigating production issues. Scout powers Bits Investigation, our AI SRE. We trained Scout to reduce inference cost so that investigations are cheap enough to run an order of magnitude more often and earlier in an incident’s lifecycle. Key highlights include: Close dialog.
Monitor the performance of critical steps in user journeys with RUM operations
Frontend and mobile applications depend on technical steps that users expect to work in every user journey: loading search results, signing in, saving a payment method, or processing a payment. Datadog Real User Monitoring (RUM) captures detailed events about these interactions, including views, actions, resources, and errors. But examining those events individually does not always provide a direct answer to more fundamental questions: Did a critical application step work, and how long did it take?
How Kubernetes DRA can improve GPU utilization and cut costs
Organizations are spending more on GPUs as they move AI workloads into production, and improving GPU utilization is a key priority. But Kubernetes’s legacy device plugin model can limit those improvements because it constrains how workloads request and consume GPU resources. Teams must configure GPU partitioning before workloads are scheduled. Resource requests cannot distinguish GPUs by capability, and a workload can reserve an entire GPU while using only part of its capacity.
Datadog San Francisco Summit: Keynote
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, Senior Vice President of Product Management, and Cansu Berkem, Director of 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.
Dismantling Datadog's Monolith with Agentic AI - Developer Track
AI can write code, but how does it do with a large, organically built codebase with years of development? Datadog’s monolithic webapp had significant scale and support challenges, so we knew the time had come to break it up. But when the code has been written, revised and tweaked so many times (as we keep adding new features and capabilities), by so many teams, that becomes much harder.
How Datadog defines service criticality at scale
Datadog runs thousands of internal services and has been increasing global footprint year after year. For most of that growth, we treated services uniformly when making deployment and reliability decisions. At our current scale, that approach became expensive. A change destined for production rolls out to data centers sequentially, so deployment time grows with every data center we add.
Accelerating Resolution with Autonomous Incident Response v1
Incident response often spans a patchwork of tools, handoffs, and manual steps. An engineer gets paged, a team investigates the issue, the incident commander coordinates the response, SREs document decisions, and somebody (anybody!) writes the postmortem. Every handoff introduces more context switching, increasing the risk of critical information loss, and ultimately slowing resolution.
Building an AI Assistant That Actually Understands Your Incidents
When production breaks, the clues are everywhere but the story is scattered. RUM sessions, APM traces, metrics, and logs allow Datadog to tell you part of the story. What’s missing is your service ownership quirks, your team's conventions, or the three-year-old workaround buried in a comment that explains why a certain endpoint behaves the way it does.
Democratizing Incident Response with AI at Riot Games - SRE Track
Senior engineers are the pillars of SRE programs—not only do they have the leadership to manage incidents well, they also have the tribal knowledge to understand the technical causes of tricky issues. But when the know-how is confined to a few individuals, it greatly limits the effectiveness of your SRE team.