Production AI can get expensive fast-and the bill rarely tells you why.
AI Cost Engineering gives you a practical, step-by-step way to understand where AI spending comes from, what actually creates value, and how to improve efficiency without sacrificing quality, latency, reliability, or useful outcomes.
You do not need prior experience with AI FinOps or cost engineering. If you already understand basic software, APIs, cloud infrastructure, or deployed applications, you have enough foundation to begin. Complex ideas are introduced progressively, with practical explanations, formulas, decision frameworks, production examples, and hands-on exercises that help you build confidence one improvement at a time.
Key FeaturesA vendor-neutral approach to production AI economics
Practical cost models, worksheets, checklists, and decision matrices
Real-world guidance for LLM applications, RAG pipelines, AI agents, and inference workloads
Clear treatment of hosted, managed, and self-hosted infrastructure economics
Practical exercises that turn concepts into engineering decisions
A continuous measure-diagnose-optimize-validate approach to cost improvement
What You Will LearnMeasure the true cost of production AI systems
Attribute spending across users, tenants, features, workflows, and agents
Calculate cost per request, workflow, customer, and useful outcome
Control token usage, context growth, memory, retries, and tool calls
Evaluate model selection, routing, cascades, and fallback strategies
Improve RAG economics through retrieval, caching, reuse, and context management
Optimize inference with batching, scheduling, compression, throughput, and utilization
Compare hosted APIs with managed and self-hosted deployment models
Apply AI FinOps practices including budgets, forecasts, showback, chargeback, anomaly detection, and guardrails
Build an operating model for continuous AI cost optimization
Who This Book Is ForAI and LLM application engineers, machine learning engineers, backend and platform engineers, MLOps and SRE professionals, cloud engineers, FinOps practitioners, software architects, technical leads, and engineering managers who want a structured way to control the economics of production AI systems.
You do not have to master everything at once. The book helps you learn progressively, test ideas against real workloads, and recognize small improvements-such as reducing repeated context, improving cache reuse, controlling retries, or routing work to a better-fit model-as meaningful progress.
Table of ContentsAI Cost Engineering as a Production Discipline
Measuring AI Cost and Unit Economics
Token, Context, and Workload Economics
Cost-Aware LLM Application Architecture
Model Economics, Selection, and Routing
Engineering the Economics of AI Agents
Inference and Workload Optimization
Infrastructure Economics and Capacity Planning
AI FinOps, Cost Observability, and Governance
Continuous AI Cost Optimization in Production
If you are ready to stop treating AI cost as a mysterious monthly bill and start managing it as an engineering discipline, AI Cost Engineering is the practical companion you need.
Start building more efficient, measurable, and economically controlled AI systems today.
