I let three different AIs work in my homelab every day — a coding assistant, a second CLI for pair-work, and a small agent that triages alerts overnight. One evening I realized I couldn't answer a basic question: what is all of this actually costing me? Two burn subscription quota I've already paid for, one spends real API dollars, and none showed up on the Grafana dashboards I'd built for everything else in the rack.
So I fixed it. Every call from every AI in the lab — tokens, latency, cache hits, and dollars — now lands in Prometheus and one Grafana dashboard. This walks through the four measurement legs, the PromQL traps that made my first dashboard lie, and the privacy scrub that makes the screenshots publishable.
Make the addresses your own. Every machine-specific value here is a placeholder: the monitoring host 10.0.0.5, agent host 10.0.0.7, Ollama nodes 10.0.0.1– 10.0.0.3, exporter ports, and any /home/youradmin paths.
The AI layer has a genuinely weird cost structure. Two interactive CLIs run on flat subscriptions, so their "cost" is quota — a percentage of a weekly allowance. The automated agent calls a hosted API and pays per token. Same lab, three billing models. So the dashboard has two columns: quota burn (a percentage that resets) and real dollars (the metered agent).
The coding CLI needs no wrapper — it has native OpenTelemetry support. Switch it on with env vars:
export CLAUDE_CODE_ENABLE_TELEMETRY=1
export OTEL_METRICS_EXPORTER=otlp
export OTEL_EXPORTER_OTLP_PROTOCOL=grpc
export OTEL_EXPORTER_OTLP_ENDPOINT=http://10.0.0.5:4317
Every session exports claude_code.token.usage (by type — input, output, cache read), claude_code.cost.usage in USD, and claude_code.session.count. Those arrive over OTLP, which Prometheus doesn't scrape directly — so an OpenTelemetry Collector on the monitoring host listens on :4317, applies processors, and re-exposes everything in Prometheus format on :8889.
The automated agent doesn't speak OTel, but it writes an honest log line per call: provider, model, token counts, latency, cache hit, and the occasional "fallback activated". A ~200-line stdlib-only Python exporter tails the log with a regex, keeps counters in memory, and serves them on :9109. It also reads the agent's task database (read-only) for queue-depth gauges — so the dashboard shows quality alongside spend: is the agent finishing work, or just burning tokens?
My three-node Ollama cluster was stubborn: Ollama ships no Prometheus endpoint (as of the 0.30 releases). But it leaks what you need in two places — its journald request log (status, latency, caller IP) and the documented /api/ps endpoint (which models are loaded right now). One exporter per node on :9110. The caller-IP label turned out useful: it shows who is using the local models.
The second CLI (Codex) records tokens and quota in session files, but the laptop sleeps and moves, so Prometheus can't reliably scrape it. That's exactly the Pushgateway case: a systemd user timer parses the session files every 5 minutes and pushes lifetime totals plus quota percentage to :9091.
Wiring it together is four scrape jobs:
scrape_configs:
- job_name: ai_claude_code
static_configs: [{ targets: ["10.0.0.5:8889"] }] # OTel Collector
- job_name: ai_codex
honor_labels: true
static_configs: [{ targets: ["10.0.0.5:9091"] }] # Pushgateway
- job_name: ai_agent
static_configs: [{ targets: ["10.0.0.7:9109"] }] # log-tail exporter
- job_name: ai_ollama
static_configs: [{ targets: ["10.0.0.1:9110", "10.0.0.2:9110", "10.0.0.3:9110"] }]
Trap 1: per-session counters break increase(). Claude Code's counters are per-session and ephemeral; a short session leaves one sample, and range functions need two, so increase() returns nothing while a real 30k-token session sits invisible. Read the last value each session reported and sum:
sum(max_over_time(claude_code_token_usage_tokens_total[1d]))
Trap 2: composite cost math collapses on absent series. In PromQL, arithmetic with an empty operand makes the whole expression empty — so before any cache reads existed, real spend rendered as $0.00. Guard every component with or vector(0):
(sum(rate(input_tokens[1h])) or vector(0)) * 1.00 / 1e6
+ (sum(rate(cache_tokens[1h])) or vector(0)) * 0.10 / 1e6
+ (sum(rate(output_tokens[1h])) or vector(0)) * 5.00 / 1e6
Trap 3: histogram_quantile returns literal NaN over idle windows. Documented behavior with zero observations — my latency panel drew garbage across every quiet hour. Consumers need to drop non-finite samples; Grafana panels just go sparse when the lab is idle, which is the honest picture.
Claude Code's telemetry attaches identity by default — your email, account ids, org id — as labels on every metric. Useful in a company; radioactive on a public screenshot. Going forward, the OTel Collector deletes those before they reach Prometheus:
processors:
attributes/scrub:
actions:
- { key: user.email, action: delete }
- { key: user.account_uuid, action: delete }
- { key: user.account_id, action: delete }
- { key: user.id, action: delete }
- { key: organization.id, action: delete }
Delete by key, not value, so anyone who ever exported from that laptop gets scrubbed. For history already on disk, open a temporary Prometheus admin window (--web.enable-admin-api), delete the identity-labeled series, then close it:
curl -X POST 'http://10.0.0.5:9090/api/v1/admin/tsdb/delete_series?match[]={user_email!=""}'
curl -X POST 'http://10.0.0.5:9090/api/v1/admin/tsdb/clean_tombstones'
One deliberate non-deletion: I kept session_id. Dropping it merges per-session cumulative counters into one series (last write wins, totals undercount). Scrub identity; keep cardinality that's structurally load-bearing.
My Node Down alert was up == 0. A sleeping laptop is not an outage, so it's now scoped to exclude the AI jobs — a dead AI exporter shows as a dashboard gap, real infra still pages:
up{job!~"ai_.*"} == 0
Not spend — the cache-hit rate. At ~78% cached input, the agent's metered bill stays in coffee money. The day that rate drops is the day something changed in how it builds prompts, and now I'll see it the same morning. Total cost of the measurement layer: two tiny Python exporters, one collector, one gateway, and an evening.
Originally published on peira.dev.