Measure the full cost stack
Track provider input, cached input, output, retries, latency, gateway fees and failed tasks. Group results by model and workload. Averages across unrelated workloads hide the places where optimization is effective or risky.
Apply the right control
Use cheaper models for tasks that pass quality tests, exact caching for identical deterministic requests, prompt caching for repeated required prefixes and context reduction for avoidable input. Set budgets and rate limits around teams and applications.
Cost calculator
Enter your current rates and measured average. Nothing is uploaded.
Protect production quality
Every change needs a release gate. Compare required fields, tool calls and task success, and retain passthrough. Varion focuses on provider-bound input optimization with transparent measurement rather than claiming to replace routing, observability or provider billing.
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.
How Varion fits
Varion Token Engine is a gateway and testing platform for reducing eligible input-token waste across supported AI traffic. It reports original and provider-bound input, keeps provider charges separate, and does not claim that every request can be reduced. New verified users receive 100,000 processed input tokens and 50 local test runs.
Frequently asked questions
What is the fastest LLM cost optimization?
It depends on the workload. Exact duplicate removal, unused-tool pruning and model right-sizing are common starting points.
Should cost be measured per token or per task?
Track both. Cost per successful task captures retries and quality failures that token metrics alone miss.