- The AI Bill Shock
- Token Economics 101
- Measuring First: Token Metering and Cost Attribution
- Unit Economics: Cost per Request, User, and Feature
- Prompt Engineering for Cost
- Prompt Caching: Mechanics, Hit Rates, and Break-Even
- Batch Processing and Async Workloads
- Model Routing and Cascades
- RAG Cost Optimization
- Agents: The Cost Multiplier
- Self-Hosting Break-Even
- Governance: Budgets, Quotas, and Cost Accountability
- Negotiating and Forecasting
- Final Project: Full Tokenwatch Integration
- Appendix: Cost Optimization Checklist and Pricing Worksheet
The AI Cost Playbook
FinOps for LLM systems — token economics, caching, routing, and cost governance
This book is 100% completeLast updated on 2026-07-22
Your AI bill is growing faster than your revenue. This book teaches you to cut 50-90% of your LLM spend without sacrificing quality. Fourteen chapters covering token metering, unit economics, prompt caching, batch processing, model routing,
RAG cost optimization, agent cost controls, self-hosting break-even analysis, and cost governance.
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About
About the Book
Every team that ships LLM features to production eventually gets the same unpleasant surprise: the bill. AI costs are non-linear, usage-based, and hard to predict — and they grow with success. This book is the operations manual for making AI
systems financially sustainable. Over fourteen chapters, you build "tokenwatch," a complete cost observability and optimization toolkit in Python: token metering middleware, unit economics calculators, prompt cache layers, batch processing
pipelines, model routers with quality/cost cascades, and budget governance dashboards. Every technique uses real API mechanics (Anthropic prompt caching, batch API, model routing) with configurable pricing from a pricing.yaml file — no
hardcoded numbers that go stale. Whether you are an engineer optimizing a pipeline or a leader accountable for AI spend, this book takes you from bill shock to financial control.
Author
About the Author
CAIO INCAU
aio Incau é Engineering Manager com experiência liderando times de engenharia de software em larga escala. No dia a dia, combina gestão de pessoas com profundidade técnica para entregar produtos que impactam milhões de usuários. Começou a usar Claude Code por curiosidade, virou adepto por produtividade, e escreveu este livro para que outros desenvolvedores não precisem descobrir tudo sozinhos.
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Table of Contents
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