Dashboard

Cost#

Where the money is going, at every level of detail you might need it.

The breakdowns#

By runtime. The first cut, and usually the surprising one. Which of your agents is actually expensive.

By model. Per-model daily token and cost rollup. This is where you find out you have been running a frontier model on a task a cheap one handles.

By session. Every session with its totals, sortable. The tail of this list — a handful of sessions that cost more than the rest combined — is where the value is.

By skill and by tool. Where a runtime attributes work to a named skill or tool, cost follows the attribution.

By team. Team mappings let you roll individual keys or agents up to a group, which is what makes an internal chargeback conversation possible.

Trends and windows#

Daily, weekly and monthly totals with the trend. One thing worth knowing: a "cost window" has exactly one definition in ClawMetry, applied everywhere. The number on Home, the number on this page and the number in clawmetry status are the same query against the same rollups — if they ever disagreed, that would be a bug rather than a subtlety.

Cache efficiency#

Prompt caching is the single largest lever on agent cost, and it is nearly invisible without instrumentation.

Cache hit rate over time
Cache trends — is your caching getting better or worse as prompts drift?
Cache risk — sessions where a small change would invalidate a large cached

prefix

bash

curl -s localhost:8900/api/efficiency/cache-hit-rate | jq
curl -s localhost:8900/api/usage/cache-trends | jq
curl -s localhost:8900/api/usage/cache-risk | jq

Anomalies#

Spend that does not look like your normal pattern gets flagged. This is a statistical comparison against your own history, not a fixed threshold — a $40 session is unremarkable on one fleet and an emergency on another.

bash

curl -s localhost:8900/api/usage | jq '.anomalies'
curl -s localhost:8900/api/token-velocity | jq

→ Anomaly detection

Optimization recommendations#

The routing advisor looks at what your sessions actually do and suggests where a cheaper model would have produced the same result, plus where caching is being left on the table.

bash

curl -s localhost:8900/api/usage/optimization-recommendations | jq
curl -s localhost:8900/api/efficiency/routing-advisor | jq
curl -s localhost:8900/api/cost-optimizer | jq

→ Reducing spend

Where the numbers come from#

Cost is derived at ingest, once, from the token split and the model, using a multi-provider pricing table. Deriving at read time would mean a change to the pricing table silently rewriting history.

Where a runtime records real dollars — Cline, opencode, Pi, Copilot, Exo, Goose for paid providers — those are used instead of an estimate, and the session is marked exact. Where neither is available the session says unavailable and shows no dollar figure at all.

The per-runtime detail is in What each runtime exposes; the full model is in How cost is computed.

Do not compare against a tool that estimates

Cost tools that reconstruct spend from transcripts without cache accounting can overstate by several times, because a cache read is priced very differently from a fresh input token. If ClawMetry's number disagrees with another tool's, check whether the other one is accounting for cache reads and writes separately.

The API#

bash

curl -s 'localhost:8900/api/usage'                        | jq
curl -s 'localhost:8900/api/local/models?since=2026-08-01' | jq '.rows'
curl -s 'localhost:8900/api/local/runtimes'               | jq '.rows'
curl -s 'localhost:8900/api/local/aggregates'             | jq '.rows[-14:]'

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