Reduce LLM calls by triage and uncertainty gating to restore SaaS margins

Where this came from

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

AI-heavy SaaS products burn margin on naive blanket LLM inference. Use deterministic filters, cached classifiers and an uncertainty gate so only ambiguous cases hit the frontier model, cutting calls and returning SaaS-like margins.

Who it is for

Engineering, platform, and product teams at e-commerce marketplaces, catalog-heavy SaaS, and data platforms that run recurring LLM inference for classification, routing, or extraction workflows

Source pains

• Skyrocketing inference bills as usage grows, turning predictable SaaS margins into variable token costs.
• Naive architectures send all inputs to expensive frontier models, even when many examples are trivial or repetitive.
• Teams lack a repeatable pattern or tooling to separate high-confidence, deterministic work from uncertain cases that actually need expensive models.