Optimize LLM costs without hurting the product

Optimizing a model based only on price per token can be misleading. The real signal combines cost, success rate, latency, volume, product value and customer impact.

The first step is to identify which features or customer segments expose margin. Only then should the team test model, prompt or routing changes.

A savings recommendation should be treated like a product change. It should be simulated, approved, applied progressively and measured afterward.

The best savings often come from small adjustments: shorter prompts, more selective fallback, cheaper models for simple cases, caching or batching for repeated flows.

Estimated savings are not confirmed savings. Teams need to compare before and after, isolate volume changes and preserve the decision trail.

That evidence gives finance and product teams a reliable proof point, not just a feeling that spend went down.