AI Cost Engineering: Control Token, Agent, and GPU Spend Without Sacrificing Quality
English | October 5, 2026 | ASIN: B0HLZF3X22 | 138 pages | Epub | 354.82 KB
What does one useful AI task actually cost?A low token bill can hide expensive failures, retries, tool calls, and human rework. If you cannot connect your AI spend to accepted outcomes, you cannot confidently decide what to optimize-or what to scale.AI Cost Engineering is a practical handbook for platform engineers, AI engineers, FinOps practitioners, technical founders, and engineering leaders building production AI systems.Javier Griffin takes you from a trustworthy cost ledger to a repeatable operating practice. You will learn to:- Calculate cost per successful task and expose the price of failure.- Trace hidden multipliers in context, retries, retrieval, and tool use.- Design model routes, cascades, caches, and batches around quality and deadlines.- Put enforceable spend, call, depth, and time limits around agents.- Compare API and GPU inference using realistic workload economics.- Run measurable optimization experiments and build budgets, forecasts, and review habits.Inside: thirteen focused chapters, a complete worked case, ten reusable operational templates, answered decision exercises, and a reproducible Python cost lab that requires no model API or paid tool.The examples use fictional workloads, transparent calculations, and generic model names. You learn a method you can apply when providers, prices, and frameworks change.Written for readers who understand the basics of AI applications and want to make better production decisions. No advanced mathematics or finance qualification required.Connect architecture, quality, and money-then improve the cost of the outcome your users actually need.
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