The problem
Inference, GPU, and cloud spend are climbing faster than the value they produce, and no one can attribute the cost to a feature, a team, or a decision.
The approach
A measured teardown of where the money goes — model choice, token economics, GPU utilization, egress, idle capacity — and a remediation plan with the savings quantified before any change ships.
Engagement
Priced on a share of verified savings, so the engagement pays for itself or it does not bill.
What's delivered
- Spend attribution by feature/team/workload
- Token-economics and model-selection review (right model for the job, not the biggest)
- Infrastructure right-sizing and utilization fixes
- Budgets, alarms, and per-feature cost visibility that stay after I leave
The outcome
A materially lower, fully attributable spend curve — and the controls to keep it that way.
Think this is your situation?
Request an audit. You'll hear back from the person who'd do the work.