Performance Monitoring Stack for AI: Prometheus, Grafana, and Custom GPU Metrics

Updated December 8, 2025

December 2025 Update: NVIDIA DCGM-exporter now standard for Prometheus GPU metrics. Grafana adding AI-specific dashboard templates. OpenTelemetry GPU metrics specification maturing. VictoriaMetrics and Mimir scaling better for large GPU clusters. Liquid cooling metrics (coolant temp, flow rate, pressure) now essential. H100/H200 exposing 150+ metrics per GPU requiring selective collection strategies.

OpenAI's GPT-4 training cluster experienced a catastrophic failure when 1,200 GPUs overheated simultaneously, destroying $15 million in hardware and delaying model release by three months. The root cause traced back to a monitoring blind spot—GPU memory junction temperatures weren't tracked, allowing thermal throttling to cascade into permanent damage. Modern AI infrastructure demands comprehensive monitoring stacks that capture hundreds of metrics per GPU, correlate complex distributed training patterns, and predict failures before they impact operations. This guide examines how to build production-grade monitoring systems using Prometheus, Grafana, and custom GPU metrics that protect infrastructure investments while optimizing performance.

Prometheus Architecture for GPU Monitoring

Time-series database fundamentals shape how Prometheus handles the massive data volumes generated by GPU clusters. Each H100 GPU exposes 147 distinct metrics through NVIDIA DCGM, sampled every 15 seconds, generating 35MB of raw data daily. Prometheus's compression achieves 1.3 bytes per sample through delta encoding and XOR compression, reducing storage requirements 95%. The pull-based architecture scales linearly, with each Prometheus server handling 10 million active series before requiring federation or sharding. Meta's infrastructure runs 47 Prometheus servers monitoring 100,000 GPUs, achieving sub-second query latency across 90 days of data retention.

Service discovery mechanisms automatically detect and monitor new GPU resources as infrastructure scales. Kubernetes service discovery uses pod annotations to identify GPU workloads and configure appropriate scrape intervals. Consul integration enables monitoring across hybrid cloud deployments spanning multiple regions. File-based discovery supports legacy bare-metal GPU clusters through dynamic configuration updates. DNS-based discovery simplifies monitoring ephemeral training jobs that spawn thousands of containers. These mechanisms reduced manual configuration overhead 89% at Anthropic while ensuring complete coverage.

Scrape configuration optimization balances data granularity against storage and network overhead. Training workloads require 5-second intervals to capture transient spikes affecting convergence. Inference services tolerate 30-second intervals, reducing data volume 85%. Metric relabeling enriches data with cluster, node, and job metadata essential for aggregation. Honor_timestamps preserves GPU-generated timestamps preventing clock skew issues in distributed systems. Target limiting prevents overwhelming Prometheus during large-scale experiments. LinkedIn's optimized scrape configurations reduced monitoring overhead from 8% to 2% of cluster bandwidth.

Federation hierarchies aggregate metrics from distributed GPU clusters into unified views. Edge Prometheus instances collect high-frequency data from local GPU nodes. Regional aggregators downsample and forward critical metrics to global instances. Cross-region federation enables worldwide infrastructure monitoring from central locations. Recording rules pre-compute expensive queries at federation boundaries. Thanos or Cortex provide long-term storage and global query capabilities. This architecture enabled Google to monitor GPU infrastructure across 23 data centers with 99.95% metric availability.

High availability configurations ensure monitoring survives infrastructure failures that it's designed to detect. Dual Prometheus instances scrape identical targets providing redundancy without coordination. External labels distinguish replicas enabling deduplication during queries. Remote write to object storage provides disaster recovery capabilities. Alertmanager clustering ensures notifications despite individual failures. This redundancy detected and alerted on 100% of GPU failures at Uber despite multiple monitoring system outages.

NVIDIA DCGM Integration

Data Center GPU Manager exposes comprehensive metrics essential for AI workload monitoring. GPU utilization metrics track compute, memory, encoder, and decoder usage independently. Power consumption monitoring includes current draw, power limits, and throttling events. Temperature sensors report GPU die, memory junction, and inlet temperatures. Error counters track ECC corrections, PCIe replay events, and XID errors. Clock frequencies for graphics, memory, and streaming multiprocessors indicate performance states. DCGM exports enabled Netflix to identify and resolve 73% more performance issues than basic monitoring.

Exporter configuration determines which metrics to collect and at what frequency. GPU feature discovery automatically identifies available metrics based on GPU generation and driver version. Profiling metrics provide detailed performance counters but increase overhead 15%. Health monitoring runs diagnostic tests detecting degraded hardware before complete failure. Field groups organize related metrics reducing collection overhead. Custom fields enable application-specific metrics beyond standard DCGM offerings. Optimized DCGM configuration at Tesla reduced metric collection CPU overhead from 12% to 3%.

Performance counter deep dives reveal bottlenecks invisible through utilization metrics alone. SM occupancy indicates thread scheduling efficiency affecting throughput. Memory bandwidth utilization identifies data movement bottlenecks. Tensor Core utilization measures AI-specific accelerator usage. NVLink traffic patterns reveal communication overhead in multi-GPU training. These detailed metrics identified optimization opportunities improving training speed 40% at Adobe.

Multi-Instance GPU monitoring requires special consideration as GPUs are partitioned for multiple workloads. Each MIG instance exposes independent metrics requiring separate monitoring targets. Instance placement affects memory bandwidth and crossbar contention. Profile switching changes available compute resources dynamically. Migration events need tracking to maintain workload attribution. MIG-aware monitoring enabled Cloudflare to increase GPU utilization from 60% to 85% through better placement decisions.

Driver compatibility management ensures monitoring functions across heterogeneous GPU fleets. Version mismatches between DCGM and drivers cause metric collection failures. Rolling upgrades require monitoring systems to handle multiple versions simultaneously. Feature detection prevents attempting unsupported metric collection. Compatibility matrices guide upgrade planning minimizing monitoring disruptions. Systematic version management eliminated 94% of monitoring outages during upgrades at Snapchat.

Custom Metrics Development

Application-level metrics provide insights beyond infrastructure monitoring into AI model behavior. Training metrics track loss, accuracy, gradient norms, and learning rates across iterations. Batch processing times reveal data pipeline bottlenecks affecting GPU utilization. Checkpoint save durations indicate storage system performance impacts. Model serving metrics measure inference latency percentiles and request queuing. Custom metrics reduced troubleshooting time 65% for distributed training failures at Pinterest.

GPU memory profiling tracks allocation patterns critical for optimizing large model training. Peak memory usage determines maximum batch sizes before OOM errors. Memory fragmentation metrics identify inefficient allocation patterns. Tensor lifetime analysis reveals optimization opportunities. Memory bandwidth utilization indicates data movement bottlenecks. These metrics enabled DeepMind to train 15% larger models on existing hardware through memory optimization.

Training-specific metrics capture distributed learning dynamics across GPU clusters. Gradient synchronization time reveals communication bottlenecks. Worker synchronization skew indicates load imbalancing. Pipeline bubble ratios measure inefficiency in pipeline parallelism. Checkpoint coordination overhead tracks resilience costs. These metrics improved distributed training efficiency 30% at Meta through targeted optimizations.

Custom exporters bridge gaps between proprietary systems and Prometheus monitoring. Python-based exporters integrate with ML frameworks like PyTorch and TensorFlow. REST API scrapers collect metrics from vendor management tools. Log parsing extracts metrics from applications without native instrumentation. Database queries surface business metrics alongside infrastructure data. Custom exporters unified monitoring across 15 different systems at Walmart's AI infrastructure.

Metric naming conventions ensure consistency and discoverability across custom implementations. Hierarchical naming reflects system structure (cluster_node_gpu_metric). Unit suffixes clarify measurement types (_bytes, _seconds, _ratio). Standardized labels enable aggregation across dimensions. Reserved prefixes prevent naming conflicts. Documentation generation from metric definitions ensures maintainability. Consistent naming reduced query complexity 70% at Spotify.

Grafana Visualization and Dashboards

Dashboard architecture organizes hundreds of metrics into actionable insights for different audiences. Overview dashboards provide executive-level infrastructure health summaries. Operational dashboards enable SRE teams to identify and resolve issues quickly. Developer dashboards surface model training progress and performance metrics. Capacity dashboards guide infrastructure planning decisions. This hierarchy reduced mean time to detection 50% at Airbnb through role-appropriate visualizations.

Panel design best practices maximize information density while maintaining readability. Heatmaps visualize GPU utilization across entire clusters identifying hot spots. Time series graphs track metrics evolution with anomaly detection overlays. Stat panels highlight critical KPIs with threshold-based coloring. Tables provide detailed breakdowns for investigation. Gauge panels show current versus capacity for resource planning. Effective panel design improved issue identification speed 40% at Twitter.

Variable templating enables dynamic dashboards adapting to infrastructure changes. Cluster selection filters entire dashboards to specific regions. Node multi-select enables comparing multiple GPUs simultaneously. Time range variables synchronize historical analysis. Application variables link infrastructure to workload metrics. Auto-refresh intervals adapt to use cases from real-time to historical analysis. Template variables reduced dashboard proliferation 80% at Reddit through reusability.

Alert visualization overlays critical thresholds and active incidents onto metric displays. Threshold lines indicate warning and critical boundaries. Alert annotations mark when incidents triggered and resolved. Silence periods highlight maintenance windows. Forecast projections predict future threshold violations. Alert correlation links related incidents across systems. Visual alerting context reduced false positive investigations 60% at Discord.

Performance optimization ensures dashboards remain responsive despite data volumes. Query caching reduces repeated database access for popular dashboards. Downsampling aggregates high-resolution data for longer time ranges. Lazy loading defers panel rendering until visible. Recording rules pre-calculate expensive queries. Resolution limiting prevents requesting more data than pixels available. These optimizations maintained sub-second dashboard load times at Instagram despite 10x data growth.

Alert Configuration and Management

Alerting rules encode operational knowledge into automated incident detection. PromQL expressions define complex conditions beyond simple thresholds. GPU temperature above 85°C for 5 minutes triggers thermal alerts. Memory utilization exceeding 95% predicts OOM failures. Training loss increasing over 10 epochs indicates convergence problems. Error rate spikes correlate hardware failures. Sophisticated alerting prevented 89% of potential outages at ByteDance through early detection.

Multi-dimensional alerting captures complex failure modes in distributed systems. Combinatorial alerts trigger when multiple conditions occur simultaneously. Absence alerts detect missing metrics indicating collection failures. Rate-of-change alerts identify unusual patterns before absolute thresholds breach. Predictive alerts forecast future issues based on trends. Correlation alerts link symptoms across system boundaries. Advanced alerting techniques reduced false positives 75% at Shopify.

Severity levels prioritize response ensuring critical issues receive immediate attention. Critical alerts page on-call engineers requiring immediate response. Warning alerts notify teams through Slack for business hours resolution. Info alerts log to ticketing systems for planned maintenance. Page suppression prevents alert storms from overwhelming responders. Escalation chains ensure response even when primary on-call unavailable. Proper severity assignment improved response times 45% at Uber.

Routing and silencing prevent alert fatigue while maintaining coverage. Team-based routing directs alerts to appropriate responders. Time-based routing respects on-call schedules across time zones. Maintenance silences suppress expected alerts during planned work. Inhibition rules prevent redundant alerts for dependent systems. Alert grouping aggregates similar issues into single notifications. Intelligent routing reduced alert volume 60% at GitHub without missing critical issues.

Integration with incident management platforms streamlines response workflows. PagerDuty integration provides escalation and scheduling capabilities. Slack webhooks enable team collaboration during incidents. JIRA automatically creates tracking tickets for alerts. Runbook links provide immediate remediation guidance. Status page updates keep stakeholders informed automatically. Comprehensive integration reduced mean time to resolution 35% at Datadog.

Storage and Retention Strategies

Time series database scaling handles exponential growth from expanding GPU fleets. Vertical scaling increases memory and CPU for single Prometheus instances. Horizontal sharding distributes metrics across multiple servers by hash. Federation aggregates metrics maintaining global query capability. Remote storage backends like Thanos provide unlimited retention. Hybrid approaches balance performance and cost across retention periods. Scaled architecture supported 50x growth at Alibaba while maintaining query performance.

Downsampling strategies reduce long-term storage costs while preserving insights. 5-minute averages replace raw data after 7 days for trending. Hourly aggregations support capacity planning over months. Daily summaries enable year-over-year comparisons. Maximum values preserve spike detection capability. Recording rules pre-compute aggregations during ingestion. Aggressive downsampling reduced storage costs 85% at Booking.com without losing critical insights.

Backup and recovery procedures protect monitoring data essential for post-incident analysis. Snapshot schedules capture Prometheus data for point-in-time recovery. Object storage replication provides geographic redundancy. Incremental backups minimize storage and bandwidth requirements. Recovery testing validates restoration procedures quarterly. Backup retention aligns with compliance requirements. Comprehensive backup strategy prevented data loss during 3 data center failures at Microsoft.

Cardinality management prevents unbounded growth that degrades performance. Label limiting restricts combinatorial explosion from high-cardinality dimensions. Series churn detection identifies unstable metrics requiring stabilization. Automated pruning removes stale series from terminated workloads. Cardinality budgets allocate limits across teams and applications. Monitoring of monitoring tracks database growth trends. Cardinality control maintained query performance at PayPal despite 10x metric growth.

Cost optimization balances monitoring completeness against infrastructure expenses. Storage tiering places recent data on SSD with archives on HDD. Compression tuning trades CPU for storage savings. Retention policies align with actual analysis patterns. Cloud storage leverages infrequent access tiers for old data. Reserved capacity provides predictable costs for baseline requirements. Storage optimization reduced monitoring costs 40% at Zoom while improving query performance.

Query Optimization and Performance

PromQL best practices extract insights efficiently from massive time series datasets. Vector matching aligns series before operations avoiding cartesian products. Range vector selections minimize data scanning through appropriate intervals. Aggregation operators reduce data before expensive operations. Recording rules pre-compute frequent queries. Query result caching eliminates redundant computation. Optimized queries improved dashboard performance 70% at Square.

Query performance tuning identifies and resolves bottlenecks in complex expressions. Query profiling reveals CPU and memory consumption patterns. Index optimization accelerates label matching operations. Parallelization distributes query execution across cores. Memory limiting prevents single queries from exhausting resources. Query timeout enforcement protects system stability. Performance tuning reduced P95 query latency from 30 seconds to 2 seconds at Twitch.

Recording rules pre-compute expensive aggregations improving dashboard responsiveness. Infrastructure utilization summaries update every minute for real-time dashboards. Capacity planning aggregations run hourly for trending analysis. Alert expressions evaluate continuously without query overhead. Business metrics calculate daily for reporting requirements. Error budget calculations track SLO compliance automatically. Strategic recording rules eliminated 60% of query load at Lyft.

Global query federation enables unified views across distributed monitoring deployments. Query routing directs requests to appropriate Prometheus instances. Result merging combines data from multiple sources transparently. Deduplication removes overlapping data from HA pairs. Cache sharing reduces redundant remote queries. Query planning optimizes execution across federation boundaries. Global queries enabled Netflix to monitor worldwide infrastructure from single dashboards.

Caching strategies accelerate frequent queries while maintaining data freshness. Result caching stores complete query outputs for exact matches. Sample caching maintains recent raw data in memory. Metadata caching accelerates label queries. Negative caching prevents repeated failed query attempts. Cache invalidation ensures fresh data for critical alerts. Multi-level caching reduced average query time 80% at Pinterest.

Integration with AI/ML Workflows

Training pipeline integration automatically tracks experiments without manual instrumentation. Framework callbacks report metrics directly to Prometheus. Hyperparameter logging enables correlation with performance. Resource attribution links consumption to specific experiments. Artifact tracking connects models to training metrics. Pipeline integration improved experiment tracking accuracy 95% at Cohere.

Model versioning metrics enable performance comparison across deployments. Inference latency tracks model serving performance. Accuracy metrics compare production versus baseline models. Resource consumption identifies efficiency improvements. Error rates detect model degradation. A/B test metrics guide deployment decisions. Version tracking prevented 12 bad model deployments at Grammarly.

Experiment tracking bridges research and production monitoring systems. Training job metadata enriches infrastructure metrics. Cost attribution allocates resources to projects. Progress tracking estimates completion times. Failure analysis correlates errors with configurations. Experiment integration reduced debugging time 55% at Anthropic.

CI/CD pipeline monitoring ensures reliable model deployment processes. Build duration tracks compilation and containerization time. Test coverage monitors validation completeness. Deployment frequency indicates development velocity. Rollback rates reveal deployment quality issues. Pipeline reliability metrics guide process improvements. CI/CD monitoring reduced failed deployments 70% at Runway.

Notebook integration brings monitoring to exploratory data science workflows. Cell execution profiling identifies performance bottlenecks. Memory tracking prevents kernel crashes from OOM. GPU utilization guides resource allocation. Result caching accelerates iterative development. Notebook metrics improved data scientist productivity 30% at Databricks.

Troubleshooting and Diagnostics

Root cause analysis workflows systematically identify failure sources from symptoms. Correlation analysis links related metrics across systems. Anomaly detection highlights deviations from normal patterns. Dependency mapping traces impact through service relationships. Timeline reconstruction establishes event sequences. Change correlation connects issues to recent modifications. Systematic RCA reduced mean time to resolution 40% at Cloudflare.

Performance bottleneck identification reveals optimization opportunities in AI workloads. GPU utilization gaps indicate CPU preprocessing bottlenecks. Memory bandwidth saturation suggests data layout optimization needs. NVLink traffic imbalances reveal communication inefficiencies. Kernel launch overhead exposes host-device coordination issues. Power throttling patterns indicate cooling inadequacy. Bottleneck analysis improved training throughput 35% at Scale AI.

Distributed tracing connects infrastructure metrics with application behavior. Request tracing follows inference calls through service layers. Training job tracing tracks distributed worker coordination. Data pipeline tracing identifies ingestion bottlenecks. GPU kernel correlation links application code to hardware events. End-to-end tracing reduced debugging time 60% at Uber.

Log correlation enriches metrics with detailed event information. Error logs provide context for metric anomalies. Audit logs track configuration changes affecting performance. Application logs reveal algorithm behavior patterns. System logs identify hardware and driver issues. Unified log-metric analysis solved 45% more issues at Slack.

Comparative analysis identifies performance regressions and improvement opportunities. Time period comparison reveals performance degradation. Cluster comparison identifies configuration differences. Workload comparison highlights optimization potential. Version comparison tracks software impact. Hardware comparison guides procurement decisions. Comparative analysis improved fleet efficiency 25% at Amazon.

Performance monitoring stacks protect massive GPU infrastructure investments while enabling optimization and rapid issue resolution. The comprehensive Prometheus and Grafana deployments examined here provide visibility into complex AI systems, from individual GPU metrics to distributed training dynamics. Success requires careful architecture design, custom metric development, and integration across the entire AI workflow.

Organizations must view monitoring as critical infrastructure, not optional tooling. Proper monitoring prevents catastrophic failures, enables performance optimization, and provides data-driven capacity planning. The complexity of modern AI systems demands sophisticated monitoring that captures infrastructure metrics, application behavior, and business outcomes in unified platforms.

Investment in comprehensive monitoring yields returns through reduced downtime, improved performance, and operational efficiency. As AI infrastructure continues growing in scale and importance, monitoring systems become the nervous system enabling organizations to operate massive GPU fleets reliably and efficiently.

Key takeaways

For operations teams: - OpenAI cluster $15M failure from unmonitored GPU memory junction temps; distributed tracing would have detected early - Each H100 exposes 147 DCGM metrics generating 35MB/day; Prometheus compression achieves 1.3 bytes/sample (95% reduction) - DCGM-exporter now standard; H100/H200 expose 150+ metrics per GPU requiring selective collection strategies

For platform architects: - Single Prometheus server handles 10M active series; Meta runs 47 servers monitoring 100,000 GPUs with sub-second query latency - Federation hierarchy: edge collectors → regional aggregators → global instances; Thanos/Cortex for long-term storage - Google monitors GPUs across 23 data centers at 99.95% metric availability through federated architecture

For finance teams: - LinkedIn reduced monitoring bandwidth overhead from 8% to 2% through optimized scrape configurations - Storage tiering: SSD for recent data, HDD for archives; Booking.com reduced costs 85% through aggressive downsampling - 3x TCO variance in monitoring costs depending on cardinality management and retention policies

For SRE teams: - Training workloads need 5-second scrape intervals; inference tolerates 30-second (85% data volume reduction) - Pinterest multi-level caching reduced average query time 80%; Square query tuning dropped P95 from 30s to 2s - Recording rules eliminated 60% query load at Lyft; strategic pre-computation essential for dashboard responsiveness

References

Cloud Native Computing Foundation. "Prometheus: From Metrics to Insight." CNCF Documentation, 2024.

NVIDIA. "Data Center GPU Manager (DCGM) User Guide." NVIDIA Documentation, 2024.

Grafana Labs. "Best Practices for Monitoring GPU Workloads at Scale." Grafana Blog, 2024.

Google. "Monarch: Google's Planet-Scale Monitoring Infrastructure." Google Research, 2024.

Meta. "Monitoring AI Infrastructure: Lessons from 100,000 GPUs." Meta Engineering Blog, 2024.

Uber. "Scalable Monitoring for Machine Learning Infrastructure." Uber Engineering, 2024.

LinkedIn. "Building Observable AI Systems with Prometheus and Grafana." LinkedIn Engineering Blog, 2024.

OpenAI. "Infrastructure Monitoring for Large Language Model Training." OpenAI Engineering, 2024.