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Choose an observability platform by the failure domains it can connect, not by the number of GPU charts it advertises. Require NVIDIA DCGM Exporter or an equivalent collector for accelerator telemetry, then prove that the platform can correlate GPU, node, high-speed fabric, Kubernetes or Slurm, workload, logs, traces and model-serving latency. A composable Prometheus/Grafana and OpenTelemetry stack maximizes control and portability; managed services reduce operating work but differ in workload, cost and AI-specific views. The right choice is the platform that turns an event into four answers: which component failed, which workloads are affected, who owns it and what to do next.
What should an observability platform cover in a GPU and AI environment?
A GPU counter is only one signal in a chain that includes the accelerator, host, network fabric, scheduler, training or batch job, and inference service. An observability platform is suitable when it joins those signals on a common timeline and preserves enough identity to attribute symptoms to a team, tenant, job, pod or model.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteNVIDIA’s 2026 decision framework recommends mapping required domains to tools and covering them with the fewest tools that provide a usable triage path. This avoids buying a large collection of exporters that each expose data but leave operators to correlate it manually.
GPU and host domains
- Accelerator: utilization, memory, power, temperature, clocks, performance states, NVLink health, XID errors and ECC events.
- Node: CPU, memory, disks, PCIe, driver state, operating-system events and baseboard or firmware health.
- Fabric: InfiniBand or Ethernet counters, link errors, congestion and topology-aware health for the paths that move training data and distributed gradients.
Orchestration and workload domains
- Cluster: Kubernetes control-plane and node state, device-plugin allocation, pending pods, quotas and scheduler decisions; in HPC environments, Slurm job and partition state.
- Workload: job, pod, team, tenant, experiment and model identifiers, plus requested versus actually used accelerators.
- Inference: request rate, queue time, time to first token, tokens per second, error rate, model and version, and vector-database or retrieval dependencies where applicable.
Logs explain discrete events, metrics show trends and traces show request flow. Correlation requires shared timestamps, resource IDs and trace IDs; without those joins, an operator may see a hot GPU and a slow request but cannot establish whether they are causally related.
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How do I compare platforms for GPU telemetry and accelerator coverage?
Start with collection depth and coverage of the accelerators you actually run. NVIDIA recommends dcgm-exporter for gathering GPU telemetry in Kubernetes. GPU Operator deploys DCGM and DCGM Exporter by default, which makes that path practical for clusters already using the operator.
What DCGM and DCGM Exporter provide
DCGM is strong at NVIDIA accelerator health and performance data. Its useful signals include utilization, memory use, power draw, temperature, clock behavior, performance states, NVLink status, XID errors and ECC errors. DCGM Exporter exposes those metrics in a Prometheus-compatible format so Prometheus can scrape them and Grafana can visualize them.
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Complementary collectors and systems
Different domains may require different native sources. NVIDIA identifies NVSM for system management, Redfish or IPMI for out-of-band hardware, UFM or NetQ for fabric operations, BCM for cluster management, Run:ai for scheduling and utilization workflows, and NIM for inference-oriented signals. Add one only when a required failure domain is otherwise invisible and there is a dashboard, owner and runbook for its data.
Can one platform show GPU, Kubernetes, network and model latency together?
It can, provided the platform preserves identity across telemetry types and your instrumentation supplies the identities. A single pane of glass is useful only when a GPU event can be followed to a node, link, pod or job, and then to an inference request or model version.
Identity to require in a pilot
- GPU UUID, host name, rack or availability-zone label and driver version.
- Kubernetes cluster, namespace, node, pod, container and device-allocation identifiers.
- Slurm cluster, partition, job ID and user or project where HPC scheduling is used.
- Training run, experiment, team, tenant, model name and model version.
- Trace ID, span ID and request ID for inference calls.
OpenTelemetry is the practical interoperability layer for application metrics, logs and traces. NVIDIA’s AI data-center architecture emphasizes joining signals through timestamps, resource IDs and trace IDs so operators can attribute an incident to the correct tenant or service. Check that your selected platform can ingest OpenTelemetry without flattening those attributes or imposing cardinality limits that make them unusable.
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What a useful triage view looks like
A first-response dashboard should place GPU utilization, memory pressure, power and temperature beside node CPU and memory, fabric errors, pod scheduling state, job progress and inference latency. Selecting a time range should reveal related logs and traces rather than forcing an operator to copy timestamps between products. Include a link from every alert to the owning service and its remediation playbook.
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Should I use DCGM, Prometheus and Grafana or a managed service?
Use a composable open stack when portability, schema control and on-premises operation matter most. Choose a managed platform when reducing collection, storage and upgrade work is worth its service cost and data-residency trade-offs. Neither approach is automatically better; the decisive test is whether it closes your failure-domain and ownership gaps.
Composable Prometheus, Grafana and OpenTelemetry stack
The common pattern is DCGM Exporter for GPUs, node and Kubernetes exporters for infrastructure, Prometheus for scraping and alert evaluation, Grafana for dashboards, and OpenTelemetry Collector for application telemetry. This arrangement lets you keep data in your environment, tune labels and retention, and replace individual components.
- Strengths: portability, broad ecosystem, direct PromQL access, control over retention and deployment location, and no dependence on one vendor’s agent.
- Costs: your team owns high availability, upgrades, capacity planning, access control, cardinality management, dashboard quality and on-call support.
- Risk: separate systems can become silos unless you deliberately standardize labels, timestamps, trace propagation and a unified triage dashboard.
Datadog managed GPU monitoring
Datadog’s GPU setup uses its Agent on GPU hosts and requires NVIDIA drivers plus the Kubernetes device plugin or GPU Operator prerequisites. It can collect GPU monitoring without DCGM. Its Explorer links devices, hosts, clusters, pods, training runs and costs, and its monitors cover temperature, throttling, unmet requests, XID and ECC errors, bursty workloads and idle devices.
This model suits teams that want managed workload, health and cost views and already operate the Agent broadly. Validate the Agent’s network path, data-residency requirements, label cardinality and the effort needed to represent non-Kubernetes schedulers before deployment.
Grafana Cloud AI Observability
Grafana Cloud’s AI Observability integration provides six prebuilt dashboards: Agent Observability, GPU Monitoring, GenAI Evaluations, GenAI Observability, MCP Observability and VectorDB Observability. It is a natural fit when engineers already use Grafana workflows but want a managed service for storage, upgrades and access.
Confirm that the dashboards accept your GPU labels and model metadata, and test how their hosted metrics, logs and traces connect to private clusters. A prebuilt dashboard accelerates setup; it does not replace runbooks or an acceptance test for your own workloads.
New Relic NVIDIA DCGM integration
New Relic’s NVIDIA DCGM integration uses its infrastructure agent and Prometheus remote write. The supplied dashboard covers utilization, XID errors, clocks, performance states, temperature and power. It is most compelling for organizations already standardized on New Relic, because existing alerting, access controls and service ownership can be reused.
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Which platform is best for Kubernetes GPU monitoring?
For Kubernetes, begin with GPU Operator’s DCGM and DCGM Exporter path, then decide where the metrics should be stored and correlated. A Prometheus/Grafana deployment provides direct control; a managed service can consume the same signals or use its own host agent.
- Verify prerequisites: install compatible NVIDIA drivers, confirm the device plugin or GPU Operator exposes allocatable GPUs, and check that each node reports a stable GPU UUID.
- Enable collection: deploy DCGM Exporter (GPU Operator deploys DCGM and the exporter by default) and scrape its endpoint with Prometheus or forward the metrics through your chosen integration.
- Add cluster context: collect node, kubelet, scheduler, namespace, pod, container and quota metrics. Include pending-pod reasons and requested versus allocated GPUs.
- Label responsibly: attach cluster, namespace, workload, team and model labels. Avoid unbounded request IDs in metrics; keep high-cardinality identifiers in traces or logs.
- Build the triage view: correlate GPU and node panels with scheduling events, container logs and inference traces on one time axis.
- Test failure paths: generate or replay an XID/ECC event, a throttling condition, a pending GPU pod and a low-utilization job, then verify the alert identifies the node, workload and owner.
For Slurm, substitute job, partition, account and node identifiers for Kubernetes labels while retaining the same GPU, node, fabric and inference joins. Ask vendors to demonstrate both models if your organization runs Kubernetes and HPC side by side.
How should I evaluate workload attribution, idle capacity and cost?
Attribution is the difference between a chart and an operating system. Require every GPU sample or derived utilization record to be attributable to a node and allocation, and require a path from that allocation to a pod, job, team or model.
Utilization versus allocation
Track requested, allocated and actually used GPU capacity separately. A pod can hold an accelerator while using little compute, and a scheduler can report a healthy allocation while the process is blocked on storage or fabric. Dashboards should expose idle time by team, job and model, with the sampling interval and aggregation method visible.
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Cost views
Cost attribution depends on your own rate card, reservations and shared-host policy. A platform may provide cost views, but you must supply the mapping from GPU-hours, node-hours, storage, egress and managed-service fees to an internal owner. Validate whether the product can ingest those rates and whether idle capacity is charged to the requesting team, the platform team or a shared pool.
Exact current prices and retention limits were not established in the official material for these options. Obtain a written quote for ingestion, host or agent counts, metric and log volume, trace retention, high-cardinality usage, support and overage terms; do not infer a total cost from a dashboard count.
What alerting and runbook capabilities should I require?
An alert is operationally useful only when it narrows the next action. Set a small top-k alert set first and make every alert name an owner, affected resource and remediation playbook.
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High-value initial alerts
- XID or ECC error associated with a GPU UUID, node, workload and recent driver or reset event.
- Thermal or power throttling with duration, affected devices and the node’s cooling or power context.
- Fabric link errors, congestion or retransmits correlated with the hosts in a distributed job.
- GPU request pending beyond a defined scheduling objective, including quota, taint or node-pool reason.
- Low GPU utilization while a job holds an allocation, with CPU, storage and network context.
- Inference latency regression by model version, route and dependency, including trace exemplars.
Use severity thresholds tied to service objectives rather than vendor defaults. Suppress duplicate alerts for the same root event, but retain the evidence needed to identify all affected workloads. Link the alert to a runbook that states safe actions, escalation owner and rollback conditions.
How do deployment, data residency and query performance affect the decision?
Deployment model changes both risk and labor. An on-premises stack keeps telemetry inside your boundary but requires engineering for scaling and upgrades. A managed service shifts that work to the provider but introduces agent connectivity, egress, tenancy and residency questions.
Questions to ask in procurement
- Where are raw metrics, logs and traces stored, and can retention or deletion be configured by region?
- What happens when a GPU host loses network access to the service?
- Are dashboards and alerts usable during an outage of the control plane or vendor endpoint?
- How are metric cardinality, log volume and trace sampling priced and throttled?
- Can you export raw data, alert rules, dashboards and resource labels if you leave?
- What integrations exist for Kubernetes, Slurm, IT service management and incident chat?
- What support tier covers a driver, exporter or agent compatibility failure?
Run realistic queries over the retention period you need. Test a fleet-wide GPU search, a workload drill-down and a trace-to-log lookup during a simulated incident. A platform that stores every sample but cannot return the affected jobs quickly will not shorten recovery.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is a practical rollout plan for GPU and AI observability?
Roll out in failure-domain order so that each stage produces an actionable improvement rather than another disconnected dashboard.
- Define domains and owners. Write down responsibility for GPU, node, fabric, scheduler, workload and inference. Record escalation paths and service objectives.
- Establish GPU and node telemetry. Deploy DCGM Exporter or an equivalent collector, host metrics and health events. Confirm stable GPU and node identities.
- Join fabric and cluster state. Add UFM or NetQ, Redfish or IPMI, Kubernetes or Slurm data where those domains are material. Put GPU, node and fabric signals on one timeline.
- Add application correlation. Instrument inference and control-plane services with OpenTelemetry. Propagate trace IDs and resource attributes into logs and metrics.
- Create the top-k alert set. Start with XID/ECC, throttling, fabric errors, pending allocation, idle-held GPU and latency regression. Attach an owner and playbook to each.
- Run failure drills. Exercise an XID or ECC event, thermal or power throttling, a fabric error, a pending pod, low utilization and an inference-latency regression.
- Close only demonstrated gaps. Add NVSM, UFM or NetQ, BCM, Run:ai or NIM when a drill shows an uncovered domain. NVIDIA cautions that extra exporters without a triage path add noise.
What acceptance tests should a platform pass before purchase?
Use your own cluster and representative training and inference workloads. A successful demo is not enough; require evidence that an on-call engineer can move from symptom to action.
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| Test | Pass condition | Evidence to capture |
|---|---|---|
| GPU health event | An XID or ECC alert identifies the GPU UUID, node, affected pod or job and owner. | Alert payload, dashboard drill-down and remediation link. |
| Thermal or power throttling | The platform distinguishes throttling from ordinary utilization and shows duration and impacted workloads. | Metric timeline with host and workload context. |
| Distributed fabric fault | A link or congestion signal can be correlated to the affected training workers. | Fabric view, job IDs and related logs. |
| Scheduling delay | A pending GPU request shows the scheduler reason, requested resources and accountable team. | Pod or job event, quota context and alert. |
| Idle allocation | The system attributes low use and estimated GPU-hours to a team, job or model. | Allocation-versus-use report and rate-card calculation. |
| Inference regression | A latency alert links model version and request trace to GPU, node and dependency signals. | Trace exemplar, logs and correlated infrastructure panels. |
Record query latency, missing labels, dropped samples, alert delay and the number of manual screens needed for each test. These operational results are more predictive than a feature checklist.
What are the most common selection mistakes?
Buying a GPU dashboard instead of an observability system
A dashboard that stops at utilization cannot explain a scheduler backlog, a congested link or a model-serving regression. Make cross-domain correlation a pass/fail requirement.
Adding exporters without an operating workflow
Every new collector creates labels, storage and maintenance. If no owner, alert or triage view uses its data, it increases noise and can hide the important signal.
Ignoring identity and cardinality
Teams often discover too late that pod, job or model labels were dropped, or that high-cardinality request labels made queries expensive. Define the label contract and sampling policy before rollout.
Comparing list prices without a workload model
Telemetry bills depend on host count, GPU count, metric frequency, log volume, trace sampling, retention and overages. Compare vendors with the same representative workload and retention assumptions, then include engineering labor for a self-managed stack.
Assuming managed means no prerequisites
Managed services still need compatible drivers, device plugins or GPU Operator components, network access, permissions and reliable resource labels. Test installation and upgrade procedures on a non-production node pool.
Which choice fits common team situations?
| Situation | Reasonable starting point | Decision check |
|---|---|---|
| Strong platform engineering team, strict data boundary | DCGM Exporter, Prometheus, Grafana and OpenTelemetry | Can the team operate storage, upgrades, access and cardinality at the required scale? |
| Existing enterprise Agent and cost workflows | Datadog GPU monitoring | Do its device, workload and cost views preserve your Kubernetes or Slurm ownership model? |
| Grafana standard with desire for managed operations | Grafana Cloud AI Observability | Do the six AI dashboards and hosted pipelines accept your labels and private-cluster topology? |
| Organization already standardized on New Relic | New Relic NVIDIA DCGM integration | Does the infrastructure agent and remote-write path provide the needed GPU and workload joins? |
Treat these as starting hypotheses, not universal rankings. The winner is the option that passes your failure drills with acceptable query speed, ownership clarity, residency controls and operating effort.
Frequently Asked Questions
Do I need DCGM if my managed observability service has a GPU integration?
Not always. Some managed agents collect NVIDIA GPU data without DCGM, but you should compare the resulting metric depth, labels and failure-event coverage with DCGM Exporter and confirm that the integration supports your driver and Kubernetes setup.
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Keep the GPU collector and node telemetry, then attach Slurm job, partition, account and user or project identifiers to the time series or linked events. During a pilot, verify that a fabric or GPU alert can be filtered to the affected job and account, not just the host.
What is a sensible retention period for GPU metrics and traces?
Set retention from your incident and capacity-planning needs: keep high-resolution data long enough to investigate an outage, and retain lower-resolution aggregates for trend and cost analysis. Test query performance and storage cost with your actual sampling and label cardinality rather than adopting a vendor default.
How do I prevent model and request labels from making metrics too expensive?
Keep bounded attributes such as model name, version, team and route in metrics, and put unbounded request or trace identifiers in logs and traces. Use exemplars or trace links to move from an aggregate latency alert to an individual request without indexing every ID.
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