Cut LLM inference costs 95% with uncertainty-based filtering

Where this came from

Reddit

TL;DR: I was building an AI compliance product where the product worked, but the inference economics were ugly. The fix was not switching provider or writing shorter prompts. It was realizing that a lot of what I was sending to an LLM was repeated classification work, and that only the uncertain cases actually needed…

The pain

High LLM bills are killing SaaS margins. Use deterministic classifiers, caching and uncertainty routing so only borderline cases go to expensive frontier models, cutting calls and restoring SaaS-like economics.

Who it is for

Developer teams and engineering leaders at ecommerce marketplaces, catalog platforms, and AI heavy SaaS companies who run large volumes of LLM inference and have measurable inference spend

Source pains

• Exploding recurring LLM API bills as product usage scales, turning predictable SaaS margins into variable inference costs.
• Large volumes of repeated, deterministic classification or matching work being sent to expensive models instead of cheaper logic or cached results.