coreweave-observability

name	coreweave-observability
description	Set up GPU monitoring and observability for CoreWeave workloads. Use when implementing GPU metrics dashboards, configuring alerts, or tracking inference latency and throughput. Trigger with phrases like "coreweave monitoring", "coreweave observability", "coreweave gpu metrics", "coreweave grafana".
allowed-tools	Read, Write, Edit, Bash(kubectl:*), Grep
version	1.11.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","gpu-cloud","kubernetes","inference","coreweave"]
compatibility	Designed for Claude Code

CoreWeave Observability

Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.

Overview

CoreWeave runs GPU-intensive workloads on Kubernetes where hardware failures, memory exhaustion, and underutilization directly impact cost and reliability. Observability must cover DCGM GPU metrics, Kubernetes pod health, inference latency, and job completion rates. Proactive monitoring prevents wasted spend on idle GPUs and catches OOM conditions before they cascade.

Key Metrics

Metric	Type	Target	Alert Threshold
GPU utilization	Gauge	> 60%	< 20% for 30m
GPU memory usage	Gauge	< 85%	> 95% for 5m
Inference latency p99	Histogram	< 200ms	> 500ms
Job completion rate	Counter	> 99%	< 95% per hour
Pod restart count	Counter	0	> 3 in 15m
Node GPU temperature	Gauge	< 80C	> 85C for 10m

Instrumentation

async function trackInference(model: string, fn: () => Promise<any>) {
  const start = Date.now();
  try {
    const result = await fn();
    metrics.record('coreweave.inference.latency', Date.now() - start, { model, status: 'ok' });
    metrics.(, { model });
     result;
  }  (err) {
    metrics.(, { model, : err. });
     err;
  }
}