{"product_id":"ai-cost-playbook-finops-for-llm-systems-token-economics-caching-routing-and-cost-governance-paperback","title":"AI Cost Playbook: FinOps for LLM systems, token economics, caching, routing, and cost governance - Paperback","description":"\u003cp\u003eby \u003cb\u003eCaio Incau\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBring FinOps discipline to production LLM systems by measuring AI spend, optimizing token usage, and building cost controls that scale with your applications\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Build tokenwatch to measure, attribute, and optimize production LLM costs\u003c\/p\u003e\u003cp\u003e- Reduce AI spend using caching, batching, model routing, and RAG optimization\u003c\/p\u003e\u003cp\u003e- Establish budgets, quotas, forecasting, and accountability for sustainable AI\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eLLM 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.\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003eYou'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.\u003c\/p\u003e\u003cp\u003eFinally, 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.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Measure token usage and attribute LLM costs accurately\u003c\/p\u003e\u003cp\u003e- Calculate AI costs per request, user, and product feature\u003c\/p\u003e\u003cp\u003e- Optimize prompts to reduce unnecessary token consumption\u003c\/p\u003e\u003cp\u003e- Use prompt caching and calculate its break-even point\u003c\/p\u003e\u003cp\u003e- Cut workload costs with batching and model routing\u003c\/p\u003e\u003cp\u003e- Optimize RAG pipelines and control agent-related costs\u003c\/p\u003e\u003cp\u003e- Evaluate the economics of APIs versus self-hosted models\u003c\/p\u003e\u003cp\u003e- Build budgets, quotas, forecasts, and AI cost governance\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- The AI Bill Shock\u003c\/p\u003e\u003cp\u003e- Token Economics 101\u003c\/p\u003e\u003cp\u003e- Measuring First: Token Metering and Cost Attribution\u003c\/p\u003e\u003cp\u003e- Unit Economics: Cost per Request, User, and Feature\u003c\/p\u003e\u003cp\u003e- Prompt Engineering for Cost\u003c\/p\u003e\u003cp\u003e- Prompt Caching: Mechanics, Hit Rates, and Break-Even\u003c\/p\u003e\u003cp\u003e- Batch Processing and Async Workloads\u003c\/p\u003e\u003cp\u003e- Model Routing and Cascades\u003c\/p\u003e\u003cp\u003e- RAG Cost Optimization\u003c\/p\u003e\u003cp\u003e- Agents: The Cost Multiplier\u003c\/p\u003e\u003cp\u003e- Self-Hosting Break-Even\u003c\/p\u003e\u003cp\u003e- Governance: Budgets, Quotas, and Cost Accountability\u003c\/p\u003e\u003cp\u003e- Negotiating and Forecasting\u003c\/p\u003e\u003cp\u003e- Final Project: Full Tokenwatch Integration\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 206\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.44 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e September 29, 2026\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":71744003342589,"sku":"9781808822155","price":50.18,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/3414\/0157\/files\/CPd_kqz_hs9781808822155.webp?v=1793299891","url":"https:\/\/booktolia.com\/products\/ai-cost-playbook-finops-for-llm-systems-token-economics-caching-routing-and-cost-governance-paperback","provider":"booktolia","version":"1.0","type":"link"}