By Net Innovix · Published on 2026-05-14 · AI & ML
Prompt caching, model routing, eval harnesses. None of it is exciting, and all of it decides whether your AI feature makes money.
Every client we talk to wants an AI feature. Few have thought through the unit economics. This post covers the three levers that determine whether your LLM feature is a cost center or a growth driver: model routing (using a cheap, fast model for 80% of requests and reserving frontier models for genuinely hard cases), prompt and response caching for repeated queries, and building an evaluation harness before you ship — not after users complain.
We walk through a real internal benchmark comparing three routing strategies across cost, latency and quality, and share the eval rubric template we use with every AI engagement.