Kubernetes Observability for AI Platforms: Metrics, Logs, Traces, and GPU Monitoring

Standard Kubernetes monitoring — CPU, memory, pod counts — tells you almost nothing about AI platform health. When a model serving endpoint returns 200 OK but latency doubles, CPU metrics won't show the GPU memory fragmentation causing it. When a training job runs for 72 hours with 5% GPU utilization, Prometheus default scrape won't capture it. AI platforms need purpose-built observability.

The Four Pillars + One

Traditional observability has three pillars: metrics, logs, traces. AI platforms add two more:

• Metrics — System metrics, Kubernetes metrics, GPU metrics, application metrics
• Logs — Application logs, audit logs, training job logs, inference request logs
• Traces — Request traces through the inference pipeline, data pipeline traces
• GPU telemetry — Utilization, memory, temperature, power, error counts per GPU
• Model metrics — Inference latency, throughput, accuracy drift, data distribution shift

The Stack

Prometheus + Thanos/Mimir

Prometheus for metric collection. For multi-cluster and long-term storage, add Thanos (sidecar mode) or Grafana Mimir. Key metric sources:

• kube-state-metrics — Kubernetes object state
• node-exporter — Node-level system metrics
• DCGM Exporter — NVIDIA GPU metrics (utilization, memory, temperature, ECC errors)
• cAdvisor — Container resource metrics
• Custom exporters — Application-specific metrics

Grafana Loki

Lightweight log aggregation. Deploy Promtail as DaemonSet for log collection. Index by namespace, pod, container, and custom labels. For training jobs, capture stdout/stderr and store alongside training metrics. For inference, log request/response pairs for debugging and compliance.

Grafana Tempo or Jaeger

Distributed tracing for inference pipelines. When a prediction request flows through: load balancer → preprocessing → model inference → postprocessing → response, you need to see where latency hides. OpenTelemetry SDK in your application code, Tempo for trace storage and query.

Grafana Dashboards

Build role-specific dashboards:

• Platform team — Cluster health, GPU pool utilization, node autoscaler activity, cost allocation
• ML engineers — Training job progress, GPU utilization per job, checkpoint status, estimated completion time
• Data scientists — Model inference latency, throughput, error rates, accuracy metrics
• Management — Cost per team, GPU utilization trends, platform adoption metrics

GPU-Specific Monitoring

GPU monitoring deserves special attention:

• GPU utilization vs GPU memory utilization — Low compute utilization with high memory utilization means the model fits but the workload isn't compute-bound. Might benefit from a smaller GPU.
• SM (streaming multiprocessor) utilization — More granular than overall GPU utilization. Shows actual compute engine usage.
• NVLink bandwidth — For multi-GPU training, NVLink saturation indicates communication bottlenecks
• ECC errors — Increasing correctable ECC errors predict GPU hardware failure. Alert on trends, not individual events.
• Power consumption — Correlates with actual work. Idle GPU + high power = stuck kernel or driver issue.

Alerting Strategy

Alert on symptoms, not causes:

• GPU utilization below 10% for more than 1 hour (wasted spend)
• Inference latency P99 exceeds SLO threshold
• Training job progress stalled (no checkpoint for 2x expected interval)
• GPU temperature exceeding thermal throttle threshold
• PersistentVolume usage above 85% (training data storage)

What Open Empower Delivers

We build purpose-built observability stacks for AI platforms on Kubernetes. From DCGM GPU metrics to model performance monitoring to cost dashboards — visibility that drives optimization and reliability.