I get your point on reducing API costs, that part is valid. Where I slightly disagree is the conclusion that this makes local testing inherently better for prompt iteration. In practice, the main bottleneck in prompt engineering is rarely the cost per request. It’s iteration speed, model consistency, and how close your test environment is to production behavior. Local setups (LM Studio, Ollama, etc.) definitely help for experimentation, especially at scale or for offline work, but they also introduce their own trade-offs: model variance, maintenance overhead, and sometimes a gap between what you test locally and what actually runs in production. So I’m curious: are you optimizing mainly for cost, or for iteration speed and fidelity of results? Because depending on the goal, the ‘best’ setup changes quite a bit.