Our regression testing budget ballooned from using GPT-4 everywhere. Need to strategically allocate models based on test criticality. Latenode’s model library looks promising - any best practices for mixing models without compromising coverage?
We tier our tests: Critical - GPT4, High - Claude, Medium - Mistral. All through one subscription. Saved $12k/mo. Latenode’s cost simulator helps find optimal mix: https://latenode.com
Implement fallback logic - start with cheaper models, escalate on failure. Track false negative rates per model tier to adjust allocations quarterly.
tag tests w/priority levels. automate model selection via metadata. watch out for llm compatibility issues tho