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Kubecost helps show who is paying for Kubernetes GPU capacity; GPU telemetry helps show whether that capacity is doing useful work. Together, cost allocation and activity data can reveal expensive workloads with low utilization, but neither GPU spend nor a high utilization reading alone proves that a workload is efficient.

What Kubecost can tell you about GPU costs

Kubecost’s open-source cost-allocation lineage is OpenCost, a vendor-neutral project originally developed and open-sourced by Kubecost. OpenCost measures and allocates infrastructure and container costs for monitoring, showback, and chargeback.

In OpenCost’s workload model, GPU cost is based on the greater of the requested and used GPU resources. Costs are calculated at the container level and can be rolled up by pod, namespace, label, cluster, or other dimensions. That gives teams a way to assign GPU spend to workloads and organizational owners even when utilization is less than the amount requested.

Three OpenCost metrics connect GPU capacity to cost and ownership:

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node_gpu_count Available GPU count See how many GPUs are available on a node.
container_gpu_allocation GPU allocation over the last one minute, labeled by container, node, namespace, and pod Connect allocated GPU resources to the workload and its location.

These metrics support cost attribution; allocation is not a measure of GPU activity. A workload can be charged for GPU capacity it has requested or used without that allocation indicating how busy the GPU was during the same period.

Why GPU cost needs activity data

To judge whether allocated capacity is being put to work, pair cost allocation with hardware telemetry. NVIDIA’s Data Center GPU Manager (DCGM) and DCGM Exporter can provide signals such as engine activity, streaming multiprocessor (SM) activity, device-memory activity, PCIe traffic, and NVLink traffic. DCGM Exporter exposes GPU metrics to Prometheus and uses Kubernetes pod-resource information for attribution.

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A typical telemetry stack has a collector, a time-series database, and a visualization layer. In practice, the cost view and telemetry view answer different questions: allocation associates dollars and GPU resources with workloads, while activity metrics describe what the hardware is doing. Attribution labels are important when comparing the two; without a reliable workload mapping, a device-level signal may not identify which team or pod should investigate it.

How to assess GPU efficiency across workloads

Compare cost, resource requests, activity, and workload output over matching time periods. A useful review includes:

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  • GPU cost by owner: group GPU spend by workload, namespace, team label, or another ownership dimension that your organization maintains.
  • Requested versus used resources: look for a persistent gap that could indicate overprovisioning, while accounting for workload phases and scheduling needs.
  • Low-activity intervals: identify when an allocated GPU has little observed activity, then check whether that interval is expected—for example, between workload phases—or represents stranded capacity.
  • Throughput or business output: compare the work completed with the cost and GPU time. Activity is not the same as useful output.

These signals help distinguish several situations. A large request-to-use gap can point to overprovisioning; low activity while capacity remains assigned can suggest stranded capacity; and replicas placed unevenly across nodes can leave some GPUs busy while others are underused. A workload whose cost rises without a corresponding increase in output deserves investigation, but the metrics identify a question to examine rather than establish its cause.

How to interpret SM activity

SM activity is useful as one indicator of how occupied the GPU’s streaming multiprocessors are. NVIDIA’s DCGM profiling documentation states: “A value of 0.8 or greater is necessary, but not sufficient, for effective use of the GPU.” This is an SM-activity heuristic, not a guaranteed efficiency target: a high reading does not prove that application throughput or business output is good. NVIDIA DCGM profiling metrics

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DCGM interval metrics also do not identify a source line, CUDA kernel, or instruction. If cluster-level telemetry shows a workload worth diagnosing, use an application-level developer profiler to investigate where its GPU work is spent.

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What an idle-GPU cost view can—and cannot—show

Cost allocation can show which workload or owner is associated with GPU spend. When combined with activity telemetry, it can help identify periods when costly GPU capacity appears to have low activity. That is a useful way to surface candidates for review, but it is not by itself proof that a GPU is wastefully idle: expected workload phases and the application’s output matter.

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For a meaningful comparison across teams or workloads, examine cost per GPU-hour, the request-to-use gap, time at low activity, workload throughput, and clarity of ownership. Compare telemetry coverage, sampling interval, and attribution labels as well; readings that do not cover the same time window or map cleanly to workloads can lead to misleading conclusions.

What a GPU efficiency improvement should mean

Define improvement in terms of the workload’s output and cost, not utilization alone. For example, a team can compare GPU cost and completed work before and after changing a request, replica placement, or workload configuration. Keep the comparison period and workload conditions comparable, and check that lower GPU use did not reduce throughput or service quality.

There is no universal savings percentage established by the metrics described here. Kubecost/OpenCost allocation and DCGM telemetry help teams find and measure their own opportunities; the result depends on workload behavior, GPU pricing, and operational requirements.

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