TB TokenBurn Sentinel

AI coding cost attribution

AI Coding Cost Attribution by PR, Issue, Client, and Model

AI coding cost attribution connects token usage and tool calls to business units of work, so engineering leaders can see which PRs, issues, customers, and models drive the bill.

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Best-fit situations

  • A consultancy needs to include AI agent usage in client delivery reports.
  • A product team wants to compare agent cost against engineering time saved.
  • A platform team needs model-level spend by repo and team.
  • A finance lead wants a clean internal chargeback report without raw prompt logs.

Operating steps

  1. Tag each agent task with project, repo, issue, PR, customer, user, and model.
  2. Record token burn, tool calls, wall-clock time, and output artifacts for each run.
  3. Merge provider usage with engineering system metadata instead of relying on invoices alone.
  4. Group usage by client, milestone, model, and delivery type.
  5. Generate an export that explains cost without exposing sensitive internal prompts.

Common risks

  • Costs remain hidden in one shared API key or one provider account.
  • Manual spreadsheets drift from real agent activity.
  • Client-facing reports expose too much internal context.
  • Attribution is inaccurate when tasks move between PRs or models mid-run.

How TokenBurn Sentinel helps

TokenBurn Sentinel maps every tracked run to work metadata and produces dashboards, exception lists, and client-ready cost reports.

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Questions

Common buyer questions.

What problem does this solve?

AI coding cost attribution connects token usage and tool calls to business units of work, so engineering leaders can see which PRs, issues, customers, and models drive the bill.

When should a team use it?

A consultancy needs to include AI agent usage in client delivery reports.

What should be tracked first?

Record token burn, tool calls, wall-clock time, and output artifacts for each run.

Where does TokenBurn Sentinel fit?

TokenBurn Sentinel maps every tracked run to work metadata and produces dashboards, exception lists, and client-ready cost reports.

Related AI workflow reference

Teams comparing workflow plans with launch and market assumptions can also review MiroFish AI Simulator, a companion reference for simulation-style product reasoning.