ModelMargin AI — Profit-Aware Inference

Every company shipping an LLM feature has the same silent problem: inference is the largest variable cost of the product, and it grows with usage — the more users you win, the worse your gross margin gets. Teams suspect some traffic could run on cheaper models, but can't prove which, or by how much, without risking quality. ModelMargin AI is a profit-aware inference copilot that turns raw prompt logs into a board-ready decision. It profiles traffic by task, complexity, and risk; simulates five routing strategies; uses Gemma 4 to judge whether cheaper routes retain quality; benchmarks the AMD ROCm/vLLM route; and generates a Margin Dossier with before/after cost, latency, quality retention, gross-margin lift, risks, and a one-command deployment. Crucially, the AMD numbers are not simulated. We served Google Gemma 4 E4B — the featured model — on a real AMD Instinct MI300X via vLLM 0.23.0 / ROCm 7.2.4 on AMD Developer Cloud, and measured 449.7 ms average latency, 213.4 tokens/sec, and 100% success at full GPU utilization. Simulated values elsewhere are always clearly labeled. The market is horizontal: every AI startup and platform team owns an inference cost-of-goods problem, and the buyer is the founder or platform lead who answers for margin. Routing is crowded — our edge is the honest, measured Margin Dossier and the guardrails that keep high-risk prompts on premium fallback. ModelMargin AI turns "should we move to AMD?" from a gut call into a measured business decision.