Blog

WordPress powers 40% of the web but has no native observability story. Here's how to instrument it end-to-end with OpenTelemetry - PHP, browser RUM, and errors.

1,000 nodes × 8 GPUs × 60 metrics = 1.4M time series - before you add pod names or Slurm job IDs. GPU monitoring is a cardinality problem disguised as a metrics problem. How to design for it before production OOMs your Prometheus.

GPU observability isn't one thing - it's eight connected layers from silicon to cost. See why correlation across layers is what cuts debugging from 2 hours to 2 minutes, and why most teams instrument only one or two

Most teams monitor three GPU metrics - utilization, temperature, memory. There are 50+ that matter, and the ones you skip cause your worst outages. A vendor-neutral guide across NVIDIA, AMD, and Intel Gaudi

60% GPU utilization and 3-second response times? GPU utilization is the wrong signal for LLM inference. Here's why TTFT, KV-cache pressure, and queue depth - not utilization - predict user-facing latency.

Predict GPU hardware failures 48–72 hours in advance. A guide to the five rate-based signals — ECC error trends, XID events, thermal ramp, row remap exhaustion, PCIe downtraining — and how to combine them into a composite health score.

Flat per-token pricing is wrong by 10–50× per request. Prefill vs decode, batch sharing, and cache effects break the math. How to attribute real GPU cost - compute, energy, and dollars - to each inference request.

Compare 9 incident management tools: PagerDuty, Opsgenie, Incident.io, Rootly, FireHydrant, BetterStack, Grafana OnCall, Squadcast, and Last9. Features, pricing, and which fits your team.