Kylace

The cheapest model is not necessarily the cheapest system, and the strongest model does not necessarily earn its premium. The right choice depends on what the workload needs to do, how reliably it must do it and what successful completion costs.

We’re building Kylace to keep that decision up to date. Each production AI workload will have an evaluation harness built around its prompts, tools, representative cases and acceptance criteria. Models, providers and settings are compared on the same work to measure how often they meet the requirements, how long they take and what each accepted task costs. The comparison accounts for token use, caching, retries and recorded repair costs.

When a model release, provider change, price change or deprecation could affect the decision, Kylace will reevaluate the relevant options against your current configuration. You see which options meet the bar, what the more expensive ones buy you and whether the evidence supports a change. Every recommendation links to the recorded evaluations behind it. Proposed changes arrive as pull requests for your team to review.