AI Infrastructure Monitoring Requires Four Layers Beyond Standard Kubernetes Metrics

Monitoring AI workloads in production demands visibility across Kubernetes infrastructure, GPU accelerators, model inference, and end-to-end request paths, not just CPU and memory. Standard dashboards can show all pods healthy while GPU utilization is maxed out, queues are growing, and inference latency is steadily worsening. Key GPU signals such as utilization, memory, temperature, and device health can be collected using tools like NVIDIA's DCGM Exporter feeding into Prometheus and Grafana. Inference-specific metrics including time to first token, tokens per second, queue depth, and model errors are essential for understanding actual service behavior. The core principle is correlating signals across all four layers, since no single metric in isolation reveals the full picture of an AI platform's health.