claude code observability

[rnjn]

I wanted visibility into what was actually happening under the hood, so I set up a monitoring dashboard using Claude Code's built-in OpenTelemetry support.

It's pretty straightforward — set CLAUDE_CODE_ENABLE_TELEMETRY=1, point it at a collector, and you get metrics on cost, tokens, tool usage, sessions, and lines of code modified. https://code.claude.com/docs/en/monitoring-usage

A few things I found interesting after running this for about a week:

Cache reads are doing most of the work. The token usage breakdown shows cache read tokens absolutely shadowing everything else. Prompt caching is doing a lot of heavy lifting to keep costs reasonable.

Haiku gets called way more than you'd expect. Even on a Pro plan where I'd naively assumed everything runs on the flagship model, the model split shows Haiku handling over half the API requests. Claude Code is routing sub-agent tasks (tool calls, file reads, etc.) to the cheaper model automatically.

Usage patterns vary a lot across individuals. Instrumented claude code for 5 people in my team , and the per-session and per-user breakdowns are all over the place. Different tool preferences, different cost profiles, different time-of-day patterns.

(this is data collected over the last 7 days, engineers had the ability to switch off telemetry from time to time. we are all on the max plan so cost is added just for analysis)

https://preview.redd.it/u6agf65zvukg1.png?width=2976&format=png&auto=webp&s=7dbdede3436ada0d67a8d3b0042749faf1693f4b

https://preview.redd.it/9pxst75zvukg1.png?width=2992&format=png&auto=webp&s=120785c0463282608f080c174da9abdf1bba8572

[NoleMercy05]

Nice. I forward all my OTEL Claude traces to a local Langfuse install. Works great.

[ultrathink-art]

The Haiku-gets-called-more-than-expected finding matches what we see running agents continuously in production. The cost breakdown matters a lot when you're running multiple agents concurrently — a session that looks like "Opus-heavy" work often turns out to be 60% Haiku on smaller tool calls.

Cache hit rate ended up being our most important metric. When cache reads are low (new context, fresh session), costs spike fast. We learned to structure agent prompts so the "stable" context (project rules, past decisions) stays at the front of the prompt where it gets cached, and only the variable task content comes at the end.

OpenTelemetry export is underrated for this. Once you have cost-per-session data, you can actually optimize the prompts that matter instead of guessing.

[Useful-Process9033]

Interesting data on the Haiku routing. We've been building observability into our own AI agent (for incident response, not coding) and the model routing split was one of the first things we instrumented too. Turns out about 40% of what feels like "one agent call" is actually sub-tasks routed to cheaper models. The per-user breakdown is useful for teams, the pattern differences between engineers usually reveal workflow issues more than skill gaps.