AI usage and estimated spend
Review AI usage evidence and estimated spend.
Ingest coding-agent telemetry, break usage down by session, user, model, tool, and repo, and estimate spend from a dated price catalog, with privacy controls that decide what is ever stored.
estimated spend · trend
~$2,000
Trending up as adoption grows
projectedOne connected loop, held on the stage this capability serves. The other stages stay as context so you can see what feeds in and what comes next.
Prove what happened.
Review AI usage evidence and estimated spend.
Review supported workspace changes and their recorded context.
Read spend by team and model at a glance: each cell fills by its share, so a heavy team or an expensive model stands out. These are estimates from public token prices, not invoices.
estimated spend · darker fill means more
Send OTLP or a custom JSON envelope. Each event carries tokens, duration, and git context, keyed so retries never double-count.
Emit
OTLP or JSON envelope
Dedupe
by idempotency key
Cost
tokens x dated price
Rollup
hourly and daily
Spend is grouped by the provider behind each model, computed from public token prices applied to reported tokens. The total counts up from the parts.
estimated · this month
$3,020
Cost comes from a dated price catalog, split four ways: input, output, cache read, and cache write. Read-time and backfill use identical logic.
Privacy is a control
Four ranked levels decide what a payload keeps, from metadata_only structural telemetry up to opt-in full_content. Gitleaks and redaction tripwires reject any batch that leaks secrets, and every privacy toggle is written to the audit log.
An honest limit
Spend is estimated from public token prices applied to reported tokens, not a bill. Git context fields are attribution hints, not proof of what shipped, and AI activity is usage evidence, not a productivity score or a causal ROI claim.
Usage evidence with privacy controls.