Cost and token harness
Token and compute spend is tracked by team and project as it happens, with budgets and limits enforced before the bill arrives, not explained after it.
- Policy harness
- Model routing harness
- Data transfer harness
- Guardrails harness
- Security harness
- Evaluation harness
- Observability harness
- Cost and token harness
- Reliability harness
- Audit harness
What it controls
This harness meters usage — tokens, compute, calls — at the level that actually matters for accountability: which team, which project, which model. It sets budgets and limits against that usage, and it can stop a request before it blows past them rather than only reporting on it afterward.
It applies across every model a team is allowed to use, frontier, open-weight or small, so switching models for cost or performance reasons doesn't mean losing visibility into spend.
Why it matters
AI spend is unusually easy to lose track of: a single workflow can fan out into dozens of model calls, a runaway loop can burn budget in minutes, and the invoice is often the first place anyone notices. That is not a failure of the model doing the work; it is a failure of nothing watching the meter while it worked.
Without this harness, cost conversations happen after the fact, between finance and whichever team got the surprise. With it, cost is a dial teams can see and adjust, not a shock at the end of the month.
What good looks like
- Usage tracked per team and project, not just one organization-wide total
- Budgets and limits that can actually stop a request, not just flag it in a report
- The same visibility across every model in use, not just the primary one
- Spend data available to the teams incurring it, not locked in a finance-only report
- Alerts before a limit is hit, not only after
How it fits the other nine
The model routing harness decides which model handles a task partly on cost; this harness is what makes that decision informed instead of a guess. It draws its usage data from the same traces the observability harness captures, and the limits it enforces are a form of the policy harness's rules applied specifically to spend.
Frequently asked questions
Does this replace my cloud provider's billing dashboard?
No. It tracks AI-specific usage at the team and project level the way your business actually organizes work, which a general cloud billing dashboard usually doesn't.
Can it block a request outright, or only alert?
The harness is designed to support both: alerting as usage approaches a limit, and enforcement that stops a request once a hard limit is reached. Which mode applies depends on how it's configured for a given team or workload.
Does it cover every model we use?
The intent is model-agnostic coverage, frontier, open-weight or small, so a team switching models for cost or performance doesn't lose visibility.
Questions about your AI rollout?
Talk to us about which harnesses fit your models and data.