GPU Monitoring for AI Workloads | Datadog
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See GPU Capacity, Health, and Cost in one place

Monitor shared GPU fleets so platform and ML teams can prevent stalled workloads, catch unhealthy devices early, and reduce wasted spend.

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Why Datadog?

Improve GPU Allocation

Plan capacity with a unified view of GPU usage, spend, and demand across cloud, on-prem, and neocloud environments

Pinpoint GPU Bottlenecks

Resolve stalled or slow workloads faster by connecting GPU performance, workload context, and team ownership in one view

Prevent Hardware Disruptions

Detect thermal throttling, ECC/XID errors, and other hardware issues early with built-in alerts and prescriptive next steps

Reduce Wasted GPU Spend

Break down idle GPU costs by team, workload, or service, then reclaim, reassign, or right-size capacity with targeted guidance

1,000+ Turn-Key Integrations, Including

Product Benefits

Improve GPU Planning Across Teams and Clusters

  • See fleet size, usage, and spend across hyperscalers, on-prem, and neocloud providers in one place
  • Break down GPU usage by project, service, or any tag so teams can allocate capacity more fairly
  • Distinguish true shortages from idle or poorly assigned GPUs before buying more hardware
  • Forecast GPU demand earlier so teams can avoid long procurement cycles and plan spend more predictably
  • Act on optimization guidance, such as reclaiming GPUs tied up by zombie processes, to get more from existing capacity
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Unblock Stalled GPU Workloads

  • Troubleshoot stalled workloads with shared context for both platform and ML teams instead of switching between siloed tools
  • Pinpoint why workloads are slowing down, whether the issue starts with pods stuck in initialization or unhealthy hardware
  • Detect resource contention early with alerts on workloads or clusters that have unmet GPU requests
  • Surface teams that are overreserving and underusing GPUs so high-priority workloads can get the right capacity sooner
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Prevent Hardware Issues from Disrupting AI Delivery

  • Connect heat, power, and hardware errors with workload context so teams can understand impact faster
  • Detect thermal throttling early with built-in alerts before failures spread across the cluster
  • Monitor ECC and XID errors proactively with prescriptive next steps that help teams act quickly
  • Drill into the affected host, GPU, workload, and owner so teams can fix the right issue sooner and protect launch timelines.
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Reduce GPU Waste and Control Spend

  • Break down total and idle GPU cost by any tag over any timeframe to see where spend is concentrated
  • Identify the least efficient teams and workloads to support internal chargebacks and better allocation decisions
  • Make cost optimization part of daily operations by giving teams clear reporting on GPU usage and spend
  • Reclaim, reassign, or right-size capacity with out-of-the-box recommendations tied to the owners behind wasted GPUs
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Real results from Datadog customers

12B Log events each day, managed cost-effectively
EA DICE
<2 min Mean time to resolution (MTTR)
CITIZENS BANK
50% Cost savings on cloud resources
TRAVELSUPERMARKET

Loved & Trusted by Thousands

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