SECTION 1
Define a fair benchmark
Match model, workload, sample period and quality rubric.
VARION / PRACTICAL GUIDE
Verifiable Ai Savings
Evaluate verifiable ai savings with clear metrics, known limitations and measurements from your own workload.
SECTION 2
Measure the complete result
Include provider charges, success rate, output quality, latency and passthrough requests.
SECTION 3
Make verification reproducible
Record sample size, version, aggregate usage and error cases.
Validate with your actual traffic.
Compare quality, provider usage and full cost of completed tasks. Results differ by workload and safeguards matter.
Explore Token Optimisation ↗PRACTICAL IMPLEMENTATION
From the concept to a reliable operating plan
Use this topic-specific checklist to evaluate real engineering outcomes and limitations.
Write down exactly what is being compared
Use the same task requirements, model revision, provider configuration and representative inputs whenever the architecture allows. Capture the source request hash, test timestamp and prompt-size distribution. A benchmark containing only short examples cannot justify savings claims for an application with large retrieval payloads. Separate token-reduction estimates from the amounts actually billed, because cache pricing, retries and output can materially change the final result.
Include quality and safety in the denominator
Calculate results for all eligible and ineligible requests, including the ones that required passthrough. Review required identifiers, tools used, task success, final outputs and policy compliance. Removing 30% of a prompt but lowering successful completion is not a reliable improvement. Score results before comparing costs so the quality threshold cannot be moved after an attractive cost result appears. Preserve original output contracts and deployment constraints.
Publish reproducible measures rather than slogans
Report the number of workloads, median and distribution of savings, model settings, failure count and eligible traffic share. Explain whether energy, compute and API usage were measured or only estimated. Use a repeatable evaluation suite with known acceptance criteria and a retained record of aggregated results. Do not claim zero change in outcomes merely because two strings look similar; verify the actual function that mattered to the user.
Example: how a claim changes after full accounting
Imagine a trial where the provider-bound input is 20% smaller but the completion rate falls from 95% to 90%. That may not create usable business savings after retries or human review. Conversely, a modest input reduction that preserves task quality could be worthwhile in a high-volume operation. Both conclusions are illustrative: calculate the result using your own rate card, outcome definition, traffic mix, sample size and operational constraints.
Evaluate the result with your own baseline.Discuss a technical evaluation ↗
Cost calculator
Enter your current rates and measured average. Nothing is uploaded.