Business
Datadog Announces GPU Monitoring to Help Businesses Optimize Spend and Performance as They Aim to Scale AI Projects
The launch of Datadog’s GPU Monitoring helps teams plan capacity, troubleshoot issues quickly, prevent costly failures and avoid wasted spendNEW YORK, April 22, 2026 (GLOBE NEWSWIRE) -- Datadog, Inc., (NASDAQ: DDOG), the leading AI-powered observability and security platform, today announced that GPU Monitoring is available to customers everywhere. The new product addresses one of the most prevalent issues facing organizations today as they look for a scalable and effective way to manage expandi
About this update from Datadog, Inc.
The launch of Datadog’s GPU Monitoring helps teams plan capacity, troubleshoot issues quickly, prevent costly failures and avoid wasted spend NEW YORK, April 22, 2026 (GLOBE NEWSWIRE) -- Datadog, Inc., (NASDAQ: DDOG), the leading AI-powered observability and security platform, today announced that GPU Monitoring is available to customers everywhere. The new product addresses one of the most prevalent issues facing organizations today as they look for a scalable and effective way to manage expanding AI costs. “GPU instances account for 14 percent of compute costs—which is a huge issue as companies are struggling to build AI-first technology in scalable and smart ways. While these companies can see their costs climbing, they can’t chargeback GPU spend across business units, see workload context or identify clear next steps for improvement. As a result, it is very challenging to budget and plan in thoughtful ways,” said Yanbing Li, Chief Product Officer at Datadog. The launch of GPU Monitoring marks one of the first times a single solution provides unified visibility across the AI stack—giving customers a single view linking GPU fleet health, cost, and performance directly to the teams relying on them for faster troubleshooting of slow workloads and cost savings. “Smartly managing AI spend becomes a board-level conversation when capacity is misallocated, training and inference workloads stall, and costs escalate. We all know managing GPU costs is a huge problem we need to solve, but most companies are experimenting with solutions and it is still very difficult to get a single view of what is happening across the stack. GPU Monitoring fixes that with efficiency and reliability that we haven’t seen before,” said Li. Today, most GPU tools provide high-level device health metrics, but they don’t surface cross-functional resource contention issues, explain why training and inference workloads fail, or provide visibility into which devices are idle or ineffectively used. This lack of visibility slows down investigations and means that teams overprovision as the safest default—leading to wasted spend. GPU Monitoring streamlines this work by linking fleet telemetry directly to the workloads consuming those resources, and gives platform engineering and machine learning teams a shared view to investigate together, enabling them to: “Datadog GPU Monitoring has made it ...