2 posts tagged with "Claude Code"

In the last 24 hours, one engineer's Claude Code sessions burned $166.97, 833K input/output tokens, and 959 API requests - and I can tell you exactly which Jira story got that money, which model spent it, which subagents did the heavy lifting, and that the prompt-cache hit ratio stayed at 97.2% the whole time. Not from a vendor console: from a self-hosted Grafana stack fed by telemetry Claude Code already emits.

That last part is the point. Claude Code has native OpenTelemetry support - rich metrics and events for tokens, cost, sessions, tools, skills, and agents - but nothing to look at them with. As soon as AI-assisted development becomes a line item, every engineering leader asks the same questions: what are we spending, on what, and is it worth it? This post shows how we answer them at KubeRocketCI with a small OTel Collector → Prometheus + Loki → Grafana pipeline that runs identically on a laptop and on a team Kubernetes cluster - the claude-code-telemetry stack - without ever capturing a single prompt or line of your code.

Most of my day-to-day platform work happens in a conversation. I sit in a Claude Code session - or any AI assistant with shell access - and ask plain-language questions about the state of our delivery cluster: what's failing, what's drifting, what's vulnerable, what's stale. The agent answers by calling the krci CLI, the predictable, JSON-emitting client over the KubeRocketCI Portal's tRPC API. I read the answer, decide what to do, and when a question turns into a routine I drop the underlying invocation into a script and let it run on cron or /loop. This post is a snapshot of that workflow with one running example - operator vulnerability status - and a tour of the other questions the same pattern answers.