Operations | Monitoring | ITSM | DevOps | Cloud

Automate Slack Support with Datadog Workflow Automation + Bits AI

Automate Slack support questions with Datadog Workflow Automation and Bits AI Agent Builder. Datadog Workflow Automation can now trigger directly from messages posted in Slack. In this quick demo, Yuta Yoshida, Product Manager at Datadog, shows how to build an automated support workflow that: → Detects new support questions in a Slack channel→ Sends questions to a custom agent built with Bits AI Agent Builder→ Replies directly in the Slack thread with an answer and relevant documentation.

Build better SLOs with RUM and synthetic SLIs

Well-designed service level objectives (SLOs) help you track the benchmarks your app needs to meet to deliver a successful user experience (UX). To track performance against these SLOs, many SRE teams are turning to real user monitoring (RUM) and synthetic monitoring for their service level indicators (SLIs), as these forms of data provide more direct measurements of UX than backend performance metrics.

Modal scales the next generation of AI with unified observability from Datadog

Modal is an AI infrastructure company rebuilding the cloud for AI, giving developers easy access to the compute needed to build and scale AI applications without managing the underlying complexity. Operating across more than 20 cloud providers and launching approximately 26 million containers daily, Modal relies on Datadog to unify observability across its infrastructure, applications, networking, and CI—helping its engineers detect 99% of incidents within five minutes, resolve 96% of issues before customer impact, and confidently scale one of the world’s most complex AI platforms.

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.