The Economics of Inference
Shipping models you can afford to run
A book by Alpesh Nakrani
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- Pages
- 92
- Chapters
- 11
- Format
- PDF · 17.6 MB
What's in the book
The model that wins is the one you can afford to run at scale and explain to a customer. This manual works through quantization, routing, caching, and the plain arithmetic that decides whether an AI feature has a margin or just a demo.
Revenue rarely rewards the biggest model. A working account of latency, cost, and the smaller model that wins the P&L.
Table of contents
PDF page- INTIntroduction: The Request That Won on Quality and Lost on Margin9
- 01The Arithmetic Nobody Runs Before Launch15
- 02Gross Margin Per Completed Task Is the Only Metric That Matters21
- 03Routing to the Cheapest Model That Still Clears the Bar27
- 04Caching the Request You Already Answered33
- 05Batching and the Shape of Your Traffic39
- 06Quantization and the Margin You Compress Into45
- 07Fallback and Cascade Design51
- 08Cost Attribution and Observability58
- 09The Build-Versus-Buy Cost Curve65
- 10Pricing the Feature to the Customer71
- 11Where Premium Capability Earns Its Keep77
- ENDConclusion: Price Every Call Before It Becomes a Habit83
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