Service
Model strategy and cost optimization
Cut inference cost without touching output quality, by routing each task to the cheapest model that clears the bar.
Teams default to the most capable model for every task, spend rises in step with usage, and nobody has visibility into which calls are actually driving the bill or whether a cheaper model would produce an identical result.
Method
What we do
Audit current model usage by workflow and by cost
A real breakdown, not an estimate from the vendor dashboard.
Test cheaper models against the same quality bar
Measured, not assumed.
Design a cascade routing strategy
Cheap model first, escalate only when the task requires it.
Set a monitoring baseline
Cost per unit of output, tracked going forward.
Establish a review cadence
So model choice does not drift back to the most expensive default.
Deliverables
What you get
- A cost breakdown by workflow
- A tested model recommendation per task
- A cascade routing design
- A monitoring baseline
Logistics
Typical engagement
| Duration | Participants | Format |
|---|---|---|
| 4 to 6 weeks | Engineering lead, finance or FP&A | Technical audit, testing, and a written recommendation |
Who this is for
- You have meaningful production AI usage and a rising bill you cannot explain
Who this isn't for
- You are still in early pilots with negligible spend. Revisit once usage is real