Bring FinOps discipline to production LLM systems by measuring AI spend, optimizing token usage, and building cost controls that scale with your applications

Key Features
Build tokenwatch to measure, attribute, and optimize production LLM costsReduce AI spend using caching, batching, model routing, and RAG optimizationEstablish budgets, quotas, forecasting, and accountability for sustainable AI
Book Description
LLM applications introduce a new kind of cloud economics. Costs are usage-based, model-dependent, and influenced by everything from prompt length and caching to RAG pipelines and autonomous agent loops. As grows, engineering teams need more than isolated cost-cutting tricks; they need FinOps for LLM systems. The AI Cost Playbook shows you how to apply financial accountability and engineering discipline to production AI. You’ll build tokenwatch, a Python-based cost observability and optimization toolkit, while learning to meter tokens, attribute spend, and calculate costs per request, user, and feature. You’ll then reduce unnecessary spend through prompt optimization, caching, batch processing, model routing and cascades, RAG optimization, and agent cost controls. You’ll also evaluate the break-even economics of self-hosting and learn how to forecast future AI expenditure. Finally, you’ll turn optimization into an operating discipline by establishing budgets, quotas, forecasting, and cost accountability. With configurable pricing rather than hardcoded model costs, the techniques remain useful as providers, models, and pricing evolve.
What you will learn
Measure token usage and attribute LLM costs accuratelyCalculate AI costs per request, user, and product featureOptimize prompts to reduce unnecessary token consumptionUse prompt caching and calculate its break-even pointCut workload costs with batching and model routingOptimize RAG pipelines and control agent-related costsEvaluate the economics of APIs versus self-hosted modelsBuild budgets, quotas, forecasts, and AI cost governance
Who this book is for
This book is for AI engineers, ML engineers, software engineers, platform engineers, technical leads, engineering managers, architects, and FinOps professionals responsible for building or operating LLM applications. It will also benefit technology leaders responsible for AI infrastructure and API spending who want to understand the economics behind production AI systems and establish better cost controls. Familiarity with LLM applications and basic Python will help readers get the most from the implementation-focused sections.