AI Cost Engineering

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.

• A 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

• Measure 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

AI 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.

1. AI Cost Engineering as a Production Discipline

2. Measuring AI Cost and Unit Economics

3. Token, Context, and Workload Economics

4. Cost-Aware LLM Application Architecture

5. Model Economics, Selection, and Routing

6. Engineering the Economics of AI Agents

7. Inference and Workload Optimization

8. Infrastructure Economics and Capacity Planning

9. AI FinOps, Cost Observability, and Governance

10. 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.