Buyer’s Guide - August 2026

Best GPU monitoring tools for AI workloads

Q: What GPU metrics matter most for ML training and inference?

Start with utilization, VRAM usage, temperature, power draw, PCIe bandwidth, clocks, and fan speed. For datacenter GPUs add ECC errors, XID errors, thermal throttling reasons, and MIG instance metrics. The operational missing piece is workload context: which job or pod owns the GPU, and whether GPUs sit idle between jobs.

Q: What is the difference between nvidia-smi and DCGM?

nvidia-smi is a CLI snapshot tool that ships with the driver and is fine for one off checks. DCGM is NVIDIA’s cluster telemetry and health suite, with ECC and XID error tracking, power capping, and field validation. dcgm exporter exposes DCGM metrics to Prometheus, while Netdata uses nvidia-smi for per second collection.

Q: How often should I collect GPU metrics?

Per second collection is the safest target for AI fleets because transient GPU saturation and thermal events often last only 2 to 10 seconds. Prometheus defaults to 15 second scrapes, dcgm exporter defaults to 30 seconds, and Zabbix and Checkmk commonly poll at 60 seconds or slower. Netdata’s nvidia_smi collector defaults to 10 seconds but is tunable to 1 second for true per second telemetry.

Q: How do I monitor GPU usage in Kubernetes?

The common path is dcgm exporter as a DaemonSet, often installed through the NVIDIA GPU Operator, with Grafana dashboard 12239 for visibility. Be careful with time slicing: per pod attribution can collapse to aggregate device utilization. Netdata’s Helm chart auto discovers GPU pods, and Amazon CloudWatch Container Insights can cover EKS GPU metrics for teams standardized on AWS.

Q: What is a good GPU utilization percentage for AI workloads?

Training jobs can reach 85 to 95 percent utilization, while inference often runs below 40 percent depending on batching and latency targets. Fleet averages are often far lower, with industry datasets cited in single digits, so idle GPU cost is usually the bigger problem. Monitoring should distinguish allocation from actual utilization to expose teams that reserve GPUs and do not use them.

Q: How do I monitor GPU temperature and prevent thermal throttling?

Track GPU temperature, memory junction temperature, fan speed, and power draw together rather than watching temperature alone. Clock throttle reasons such as hw_thermal_slowdown and sw_thermal_slowdown show throttling before utilization collapses. Resolution matters because thermal events can spike and recover between 15 to 60 second polls.

Q: What is MIG and how do I monitor MIG instances?

MIG partitions A100 and H100 class GPUs into up to 7 isolated compute and memory slices. Monitor per MIG memory and utilization rather than only the parent device. Netdata collects MIG mode, device count, and per MIG memory, and Grafana dashboard 23382 covers MIG and non MIG GPUs through the DCGM exporter.

GPU fleets fail fast, saturate in seconds, and emit high cardinality telemetry per GPU, pod, and MIG slice. This ranking grades tools on collection resolution, hardware health depth such as ECC and XID errors, workload attribution, time to value, and whether the pricing model punishes GPU telemetry volume.

Start with Netdata (free) Jump to rankings

Why this list exists

GPU monitoring for AI workloads is not ordinary server monitoring with a hotter chip. Training and inference GPUs can go from healthy to saturated or throttled in seconds, and the telemetry is naturally high cardinality: per GPU, per container, per pod, per MIG instance, often per job. A tool that polls every 15 to 60 seconds can look calm while short saturation, thermal events, and memory pressure pass between samples.

The buying mistake is treating GPU visibility as a checkbox inside a general platform. That usually means accepting coarse scrape intervals, shallow nvidia-smi basics without ECC or XID health detail, or a pricing model where every useful per pod and per MIG series grows the bill. GPU cost already dominates AI infrastructure spend, so the monitor should not add a second volume based surprise.

Three dimensions decide the outcome more than brand names:

Resolution and fidelity: per second or tunable collection, PCIe bandwidth, clocks, power, temperature, memory, MIG, and datacenter health signals such as ECC and XID.
Workload context: whether the tool can connect GPU behavior to pods, jobs, projects, teams, and idle allocation, or whether it only shows device level charts.
Pricing shape: per node with unlimited metrics behaves differently from per host plus per GB, per user plus ingestion, per GPU, or per seat models once fleets scale.

This page does not quote competitor list prices. List prices change, discounts are negotiated, and the useful question is what makes the bill grow: hosts, metrics, ingested logs and traces, seats, or managed GPUs. Each card links the vendor pricing or project page so current terms can be checked directly. For hands on NVIDIA specifics, the operator runbooks for NVIDIA GPU monitoring are the practical companion to this ranking.

The shortlist was assembled from tools engineers actually mention for NVIDIA GPU fleets: vendor native telemetry layers, the Prometheus and Grafana assembly, commercial SaaS observability platforms, AI workload orchestration, experiment tracking, and general IT monitors with NVIDIA templates. Ranking reflects fit for AI training and inference operations, not generic dashboard polish.

The heaviest weights sit on metric depth and collection resolution because transient saturation and thermal behavior are where GPU incidents hide. AI workload context is weighted nearly as high because utilization without job, pod, project, or cost attribution still leaves the expensive question unanswered: who is wasting GPU time.

Tester credit

Compiled by the Netdata team - Updated August 12, 2026

Sources verified

Scoring criteria

GPU metric depth and fidelity 25%
Utilization, memory, temperature, power, PCIe, clocks, fan, MIG, ECC and XID depth.
Collection resolution and real-time capability 20%
Per-second collection catches events 15s, 30s, and 60s polling miss.
AI workload context 20%
Links GPUs to jobs, pods, projects, teams, and cost behavior.
Deployment and time-to-value 15%
Zero-config discovery beats multi-component assembly projects.
Alerting and anomaly detection 10%
Static thresholds are table stakes; ML anomaly detection reduces noise.
Pricing predictability 10%
High-cardinality GPU telemetry punishes volume based models.

Vendor 01 / 10 · #netdata

Netdata

Open-source, per-second infrastructure monitoring with ML anomaly detection and native NVIDIA GPU telemetry.

Best for

ML and platform teams that want per-second GPU telemetry without per metric or per GB bills
Teams running NVIDIA and Intel GPU fleets in Kubernetes who want zero config auto discovery
Buyers who want ML based anomaly detection on every GPU metric out of the box

Pricing

Per node pricing: Netdata Cloud Business starts at $4.50/node/month on annual plans, and the per node price decreases as node count grows
Agents are open source and free; a free Cloud Community tier covers small fleets
No per GB, per metric, or per user charges; P90 billing excludes daily spikes and the top 3 days per month

Pros

NVIDIA collector covers utilization, memory, temperature, power draw, PCIe bandwidth, clocks, fan speed, voltage, performance state, and MIG instance metrics
ML anomaly detection runs 18 unsupervised models per metric, retrains every 3 hours, and carries a 99% false positive reduction claim
Deploys in about 60 seconds with auto discovery of GPUs, containers, and Kubernetes, plus 400+ pre configured alerts
Verified claims include 80% MTTR reduction and 90% cost reduction versus volume based platforms
Kubernetes native through Helm chart deployment for pods, containers, nodes, and ephemeral training jobs
Data stays on premises with the open source agent; SOC 2 Type 2, GDPR, HIPAA, and PCI DSS posture is documented

Where teams pair it

Native coverage is NVIDIA and Intel; AMD GPUs and TPUs require external exporters
No built in experiment tracking or per workload GPU cost attribution, so teams pair it with W&B, Datadog, or Run:ai for that layer

Verdict

Netdata leads because it matches how GPUs actually fail: fast, transiently, and across many dimensions at once. The platform default is per second, and the NVIDIA collector can be tuned from its 10 second default to true 1 second polling when a fleet needs that fidelity. ML anomaly detection on every metric matters on GPU fleets because static temperature and utilization thresholds either fire late or fire constantly. The honest caveat is scope: Netdata is infrastructure observability, not experiment tracking or chargeback, so ML teams often keep W&B for runs and finance may still want a cost attribution layer.

Start free ->

Vendor 02 / 10 · #nvidia-dcgm

NVIDIA DCGM + DCGM Exporter

NVIDIA’s open source Data Center GPU Manager and Prometheus exporter, the de facto telemetry foundation for NVIDIA fleets.

Best for

Platform teams running NVIDIA datacenter GPUs who need deep health telemetry
Kubernetes shops using the NVIDIA GPU Operator, which bundles dcgm exporter
Teams already running Prometheus and Grafana that want the standard GPU metric layer

Pricing

Open source under Apache 2.0 and self hosted, so there is no license fee
The real cost is operating the surrounding stack: Prometheus storage, Grafana, alerting, retention, and engineering time

Pros

Deep telemetry: utilization, memory, temperature, power, clocks, PCIe, SM occupancy, ECC errors, and XID errors
Official Grafana dashboard ID 12239 for cluster wide GPU visibility and MIG dashboard ID 23382
Runs as a DaemonSet or Helm chart and is bundled with the NVIDIA GPU Operator
Metric sets are customizable through CSV or YAML config, with watch groups for different intervals per metric family
Apache 2.0 open source backed by NVIDIA

Cons

It is a metrics endpoint, not a monitoring platform: dashboards, alerting, and long term storage are still your assembly
Default collection interval is 30 seconds unless configured, too coarse for transient saturation
NVIDIA only, with no AMD or Intel GPU support
No per container attribution when time slicing is enabled, only aggregate device utilization

Verdict

DCGM is the serious NVIDIA health layer and the reason many other tools can claim GPU depth at all. ECC and XID visibility make it stronger than nvidia-smi basics for datacenter GPUs. The ranking stops at number two because DCGM alone is not an operating picture: teams still need Prometheus or another store, Grafana, alert rules, and retention engineering. If the organization already lives in that ecosystem, DCGM is the right foundation. If it needs answers in minutes, it is a component.

Visit the DCGM project -> dcgm exporter project ->

Vendor 03 / 10 · #prometheus-grafana

Prometheus + Grafana

The standard open source metrics stack: Prometheus scrapes GPU exporters and Grafana visualizes them.

Best for

Teams that want full control and already have Prometheus expertise
Kubernetes centric shops standardizing on the CNCF ecosystem
Buyers assembling best of breed components rather than buying a packaged platform

Pricing

Prometheus and Grafana are open source and self hosted; you operate storage, HA, upgrades, and alerting
Grafana Cloud adds a SaaS option whose bill grows with hosts and metrics, logs, and traces volume

Pros

Large exporter ecosystem, led by DCGM exporter plus community exporters for other GPU vendors
Official NVIDIA DCGM Grafana dashboards cover cluster GPUs and MIG layouts
PromQL and Grafana give flexible queries, custom dashboards, and alerting
Vendor neutral with no per metric or per GB license fee when self hosted

Cons

Default 15 second Prometheus scrape interval misses short GPU events that per second tools catch
Retention, high availability, alert rules, dashboards, and cardinality control are DIY
No built in ML anomaly detection; static thresholds unless more components are added
Per pod and per MIG cardinality can inflate Prometheus storage and query cost

Verdict

This is the answer many practitioners give first, and for good reason: DCGM to Prometheus to Grafana is flexible, inspectable, and standard in Kubernetes shops. It is also an assembly project. The default 15 second scrape interval is the wrong shape for bursty GPU work unless tuned, and every operational concern from HA to alert noise belongs to the team. Choose it when control matters more than time to value and when platform engineering capacity is real.

Read full Netdata vs Prometheus -> Grafana pricing ->

Vendor 04 / 10 · #datadog

Datadog GPU Monitoring

SaaS observability with a generally available GPU Monitoring product that ties GPU health, cost, and demand to workloads.

Best for

Enterprises already standardized on Datadog that want GPU visibility beside APM and logs
Platform teams that need idle GPU cost attribution by team or project for chargeback
Multi cloud and neocloud GPU fleets that need one SaaS pane

Pricing

Per host infrastructure monitoring plus per GB log and APM ingestion, with GPU monitoring riding on the agent and custom metrics
The bill grows with host count, metric volume, and ingested logs and traces

Pros

GA GPU Monitoring brings fleet wide GPU spend, utilization, and demand forecasting into one view
Built in alerts cover ECC and XID errors, thermal throttling, and unmet GPU requests with prescriptive next steps
Connects GPU health to workload symptoms such as pods stuck in initialization, zombie processes, and overreserving teams
Works across cloud, on premises, and neocloud environments

Cons

Per host plus per GB pricing scales poorly for high cardinality GPU fleets
SaaS only, so GPU telemetry leaves the network
No out of the box ML anomaly detection on GPU metrics; alerts are threshold based
The legacy DCGM integration is deprecated in favor of the agent based approach

Verdict

Datadog is the most purpose built commercial SaaS option here for AI workload GPU monitoring, especially where cost attribution and workload context matter as much as device health. It is stronger than generic infrastructure monitors at naming the team, pod, or process behind waste. The tradeoff is economic and architectural: GPU telemetry leaves the premises, and volume based billing tends to grow exactly when observability gets useful. Best fit for organizations already paying for Datadog and consolidating onto it.

Read full Netdata vs Datadog -> Datadog pricing ->

Vendor 05 / 10 · #dynatrace

Dynatrace

Full stack observability with an NVIDIA GPU extension and AI observability for Blackwell and NIM based stacks.

Best for

Large enterprises running NVIDIA AI factories with Blackwell and NIM
Teams already on Dynatrace that need GPU metrics beside APM, logs, and traces
Buyers who want Davis AI root cause analysis across the AI stack

Pricing

Consumption based: ingestion and processing plus host memory hour units under the platform subscription
The bill grows with data volume, host hours, and log and trace ingestion

Pros

Official NVIDIA GPU extension tracks utilization, memory, temperature, power draw, and GPU processes
Full stack AI observability for NVIDIA Blackwell and NIM includes prompt tracing and compliance capabilities
Davis AI root cause analysis and Grail data lake span logs, metrics, and traces
Included in the NVIDIA Enterprise AI Factory validated design

Cons

The GPU extension depends on external libraries such as gpustat and nvidia-ml-py and must be activated per environment
Consumption pricing is hard to forecast for spiky GPU telemetry
Base GPU metrics are shallower than DCGM, with no ECC or XID detail in the base extension
SaaS first, with limited self hosted options

Verdict

Dynatrace makes sense when the problem is bigger than GPUs: model serving, LLM behavior, infrastructure, and enterprise compliance in one control plane. For pure GPU fleet operations, the extension is thinner than DCGM and the setup has more moving parts than a collector first tool. Consumption pricing can be acceptable in stable estates and frustrating in experimental AI fleets where telemetry volume jumps with new jobs. Rank it high for full stack AI enterprises, not for teams seeking the fastest GPU health signal.

Read full Netdata vs Dynatrace -> Dynatrace pricing ->

Vendor 06 / 10 · #newrelic

New Relic

Observability SaaS with an nvidia-smi based NVIDIA integration through Flex and broader AI monitoring.

Best for

Teams already on New Relic that want GPU dashboards beside APM and AI monitoring
Smaller teams that prefer per user pricing and a data allowance to start
Buyers who want NRQL queries over NVIDIA GPU samples

Pricing

Per user seats plus per GB data ingestion
The bill grows with both team size and ingested telemetry volume

Pros

NVIDIA integration collects utilization, memory, temperature, power, clocks, ECC error counts, and MIG mode
Pre built NVIDIA GPU Monitoring dashboard covers utilization, ECC errors, compute processes, and clock states
AI monitoring extends to NVIDIA NIM and the wider AI stack
NRQL can query NvidiaGpuSample data for custom views

Cons

The Flex based integration is a config file pattern rather than a maintained agent collector, with version drift risk
Per user plus per GB pricing grows with headcount and telemetry volume
No built in ML anomaly detection on GPU metrics
Metric depth depends on nvidia-smi availability on each host

Verdict

New Relic is credible if the estate is already there and the goal is decent NVIDIA dashboards without adopting another platform. The integration covers more than bare utilization, including ECC counts and MIG mode, but it feels more DIY than Datadog’s GA GPU product because Flex configuration is the mechanism. The pricing shape mixes seats and ingestion, which can be friendly for small teams and less friendly for data heavy GPU fleets. It is a middle path, not the deepest GPU operator tool.

Read full Netdata vs New Relic -> New Relic pricing ->

Vendor 07 / 10 · #runai

NVIDIA Run:ai

NVIDIA’s AI workload orchestration platform with scheduling, quotas, utilization dashboards, and GPU cost visibility.

Best for

Enterprises running shared multi tenant GPU clusters that need quotas and scheduling
Teams standardizing on Kubernetes for AI training and inference
Organizations that want utilization visibility and workload orchestration in one product

Pricing

Commercial per GPU annual subscription, also available through AWS Marketplace, with quotes through sales
The bill grows with the number of GPUs managed

Pros

Purpose built for AI with dynamic GPU scheduling, quotas, and multi tenant isolation
Dashboards show GPU allocation versus actual use per project and team
NVIDIA backed after the 2023 acquisition and integrates with EKS, AKS, and on premises Kubernetes
Addresses per workload GPU attribution on shared clusters where DCGM is aggregate only

Cons

Orchestration first, not a general monitoring platform, and lacks deep ECC or XID hardware health out of the box
Per GPU commercial pricing becomes a significant line item for large fleets
NVIDIA only, with no AMD or Intel support
Requires Kubernetes, so bare metal or non Kubernetes GPU servers are out of scope

Verdict

Run:ai solves a different problem from telemetry platforms: making scarce GPUs fairly and efficiently shared across teams. Its allocation versus actual utilization view is exactly what platform teams need to find overreserving groups. It should not be mistaken for hardware health monitoring, since ECC, XID, and thermal detail still belong to DCGM or a monitoring layer. For shared Kubernetes GPU fleets it can be excellent, usually alongside rather than instead of a metrics platform.

Visit NVIDIA Run:ai -> Run:ai marketplace listing ->

Vendor 08 / 10 · #wandb

Weights & Biases

AI developer platform that records GPU utilization, memory, and temperature next to training runs and experiments.

Best for

ML engineers who want GPU usage tied to specific runs and experiments
Research teams comparing GPU efficiency across model versions
Teams that want system metrics and experiment tracking in one workflow

Pricing

Per seat SaaS plans plus usage based components such as tracked hours and storage, with enterprise contracts
The bill grows with seats and usage volume

Pros

Automatic system metrics per run include GPU utilization, memory, power, temperature, CPU, and disk
Correlates GPU behavior with experiment results so teams can see which runs wasted GPU time
OpenMetrics can ingest DCGM exporter metrics into W&B
Two line SDK integration makes it ubiquitous in ML teams

Cons

Not an infrastructure monitor: no real time alerting, fleet wide dashboards, or ECC and XID health
Metrics are tied to runs, so idle GPUs between jobs are invisible
Per seat plus usage pricing, with roadmap uncertainty after the 2025 CoreWeave acquisition
Run scoped sampling is not per second infrastructure telemetry

Verdict

W&B is the right lens for experiment level efficiency and the wrong tool for operating GPU infrastructure. It answers whether run 417 used the GPU well, not whether node 12 is about to throttle or which pod leaked memory between jobs. Because sampling is run scoped, idle capacity disappears from view precisely when finance wants to find it. Use it as the ML workflow companion to infrastructure monitoring, not as the monitoring platform.

Visit Weights & Biases pricing -> W&B system metrics docs ->

Vendor 09 / 10 · #zabbix

Zabbix

Enterprise open source monitoring with an official NVIDIA GPU template and agent 2 plugin.

Best for

IT teams that already run Zabbix and want GPU checks on existing hosts
Organizations that prefer open source with optional paid support
Homelab and on premises GPU servers needing agent based monitoring

Pricing

Open source under AGPLv3 and self hosted, with optional paid support subscriptions and Zabbix Cloud SaaS
Cost grows with support tier rather than device count when self hosted

Pros

Official NVIDIA template uses low level discovery to find all GPUs without external scripts
Agent 2 NVIDIA GPU plugin provides native metric collection since 7.2.1
Mature alerting, notification, and reporting engine
AGPLv3 open source with predictable support subscription shape

Cons

Poll based collection at 60 second or slower intervals misses transient GPU saturation
General purpose IT monitoring, with no AI workload context, cost attribution, or model level metrics
No ML based anomaly detection on GPU metrics
Template depth is utilization, memory, and temperature basics rather than DCGM ECC and XID health

Verdict

Zabbix is a practical answer when GPUs are just another asset in an existing Zabbix estate. The official template and agent 2 plugin are better than hand rolled scripts, and support subscriptions are easier to explain than volume billing. It is not built for AI workload observability: minute level polling, static alerts, and no job or cost context leave the most expensive GPU questions unanswered. Good for estate coverage, weak for training fleet operations.

Read full Netdata vs Zabbix -> Zabbix subscriptions ->

Vendor 10 / 10 · #checkmk

Checkmk

Open core IT monitoring with nvidia-smi based GPU checks for utilization, memory, and temperature.

Best for

IT operations teams already on Checkmk that want GPU checks on Linux hosts
Organizations wanting agent based monitoring with a mature alerting UI
Buyers who prefer open core with a clear commercial upgrade path

Pricing

Open core: GPL Raw or Community edition is self hosted, while commercial editions are licensed per monitored node or service
The bill grows with monitored hosts and services

Pros

nvidia_smi checks cover GPU utilization, memory, and temperature on Linux hosts
Checkmk Exchange maintains NVIDIA SMI plugins with multiple versions
Mature platform for dashboards, alerting, and service discovery
Open source Raw edition provides an open core entry point

Cons

GPU support is plugin and community driven rather than a first class product feature
Poll based collection runs at minute level intervals
No AI workload context, cost attribution, or model level metrics
Windows GPU monitoring support is limited, with forum reported gaps

Verdict

Checkmk can add basic NVIDIA visibility to a general IT monitoring estate, especially on Linux where nvidia-smi checks are established. The fit weakens quickly for AI operations: GPU capability depends on community plugins, polling is coarse, and there is no workload attribution or datacenter health depth. It belongs on the list because many operations teams already own it, not because it competes with GPU native telemetry. Treat it as estate coverage, not AI fleet observability.

Read full Netdata vs Checkmk -> Checkmk pricing ->

Frequently asked questions

What is the best way to monitor GPU utilization for AI workloads?

For quick checks, nvidia-smi gives per GPU snapshots. For production, the standard stack is DCGM plus Prometheus plus Grafana, with official dashboards for cluster GPUs and MIG. Collection resolution matters: per second tools catch transient saturation that 15 second Prometheus scrapes and 30 second dcgm exporter defaults miss. Netdata is the zero config per second option with ML anomaly detection, while Datadog GPU Monitoring is the purpose built SaaS option.

What GPU metrics matter most for ML training and inference?

What is the difference between nvidia-smi and DCGM?

How often should I collect GPU metrics?

How do I monitor GPU usage in Kubernetes?

What is a good GPU utilization percentage for AI workloads?

Is GPU monitoring included in Datadog, New Relic, or Dynatrace?

Datadog has a GA GPU Monitoring product with ECC and XID alerts plus cost attribution. New Relic provides an nvidia-smi based integration through the Flex framework with a pre built dashboard. Dynatrace offers an NVIDIA GPU extension and broader Blackwell and NIM observability. All three are SaaS products whose bills grow through per GB, per host plus ingestion, or per user plus ingestion mechanics.

How do I monitor GPU temperature and prevent thermal throttling?

What is MIG and how do I monitor MIG instances?

Best GPU monitoring tools for AI workloads

How we evaluated GPU monitoring tools

Netdata

NVIDIA DCGM + DCGM Exporter

Prometheus + Grafana

Datadog GPU Monitoring

Dynatrace

New Relic

NVIDIA Run:ai

Weights & Biases

Zabbix

Checkmk

Frequently asked questions

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