Map spend to workloads
Group traffic by application, model, team and task type. Capture request volume, token classes, retries, latency, successful outcomes and any supporting compute cost. This makes expensive patterns visible without assuming every application behaves the same way.
Find repeatable cost drivers
Look for duplicated context, oversized tool schemas, stale history, unnecessary high-cost model use and repeated failed attempts. Prioritise patterns that occur frequently because small improvements on repeatable traffic can matter more than a dramatic one-off benchmark.
Cost calculator
Enter your current rates and measured average. Nothing is uploaded.
Validate savings before forecasting them
Test representative light, normal and heavy workloads. Apply measured gains only to traffic that resembles the test set and include quality, reliability and operational overhead in the decision.
Measurement checklist
- Choose a representative completed task, not an artificial one-line prompt.
- Record the selected model, provider input, cached input, output, retries and final result.
- Change one optimization mechanism at a time so the cause remains visible.
- Verify required identifiers, tool calls, code changes or business fields.
- Keep passthrough available when the reduced request does not pass.
Varion commercial evaluation
Varion keeps proprietary product implementation details private. Evaluate Token Optimisation on representative traffic and judge it by the measured commercial result. New verified users receive 100,000 processed input tokens and 50 local test runs.
Frequently asked questions
Should an enterprise AI audit start from invoices or prompts?
Use both. Invoices establish total spend while request-level measurements explain which workloads and behaviors create it.
Can one benchmark represent the whole company?
No. Different applications, models and task types can have very different cost structures.