Observability & Cost Control for AI
The bill is a production metric. So is whether the answers are still any good.
# what you build
AI systems fail in ways a green health check never catches: latency creeps up, GPUs sit idle at full price, and answers quietly get worse. You will instrument the service with Prometheus and Grafana, track cost per request and tokens per user, and set the budget alerts that stop a forty-dollar month becoming a four-thousand-dollar one.
What you end up with
handles after this
the same — but now you can see it
what it costs to run
+$10 / month
What you will be able to do
- The four metrics an AI service must expose, and why averages lie
- Track cost per request and tokens per user, in the same dashboard as latency
- Trace one slow request end to end — retrieval, model, network
- Watch GPU utilisation, and find the idle capacity you are paying for
- Budget alerts that fire before the invoice does
The build, step by step
These are the chapters of the video, in order.
- 1Emit the metricsLatency percentiles, queue depth, tokens and errors, straight from the service.
- 2Build the dashboardOne Grafana board that answers “is it healthy and what is it costing?”
- 3Trace a slow requestFind out whether it was retrieval, the model, or the network.
- 4Guard the billBudget alerts, per-user caps, and a kill switch you can reach at 3am.
The stack
- Prometheus
- Grafana
- Tracing
- Budget alerts
# before this one
This project continues the system built in 06 · Autoscaling on Kubernetes — GPUs, Queues & Spot. You can start here, but the repo assumes the previous rung exists.
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