About this book:

"AI Cost Engineering" is a comprehensive guide for optimizing the total cost of ownership (TCO) for AI projects across various environments. The book introduces a practical TCO framework emphasizing unit economics, helping organizations understand costs per request, user, or outcome. It dissects AI spend into infrastructure, inference, and development, and highlights the importance of workload profiling and demand modeling to anticipate resource needs accurately. Early chapters focus on foundational optimizations like reducing data pipeline costs, judicious model selection and right-sizing, and applying techniques such as quantization, pruning, and knowledge distillation to shrink models without compromising product quality.

The book then dives into advanced inference serving strategies, detailing how batching, caching, and key-value (KV) cache reuse dramatically improve hardware utilization and reduce per-request costs, especially for large language models (LLMs). It explores sophisticated batching and scheduling techniques, as well as various caching tactics including feature stores. Hardware choices are meticulously examined, comparing the price-performance trade-offs of CPUs, GPUs, and TPUs, along with the economic implications of deploying AI in cloud, edge, or hybrid environments, paying close attention to data locality and networking costs like egress.

A significant portion of the book is dedicated to the financial and operational aspects of AI cost management. It emphasizes the critical role of observability for cost, utilizing metrics and tracing to establish unit economics and identify cost drivers. The principles of FinOps are introduced for effective cost allocation, tagging, showback, and chargeback, empowering cross-functional teams with financial transparency. Chapters on budgeting and forecasting AI spend, alongside a detailed look at cloud pricing models (On-Demand, Reserved, Spot, Savings Plans), equip readers to make financially sound procurement decisions. Finally, the book addresses the non-negotiable costs of reliability, security, and compliance, while also advocating for low-cost experimentation strategies like offline evaluation and synthetic data. It concludes by highlighting the immense ROI of developer productivity tooling and how product design itself can intrinsically reduce inference demand.

The overarching theme is that AI cost engineering is not about austerity, but about sustained, efficient growth. It calls for a holistic, iterative approach, integrating technical optimizations with sound financial practices and collaborative decision-making across engineering, product, and finance teams. The book concludes with case studies and playbooks that illustrate the practical application of these strategies in real-world cloud, edge, and hybrid scenarios, demonstrating how continuous optimization can transform AI from a potential cost sink into a sustainable and strategically brilliant engine of innovation.

What You'll Find Inside:

• Apply a practical TCO framework to break down AI spend into infrastructure, inference, and development, enabling unit‑economics‑driven trade‑offs.
• Use workload profiling and demand modeling to identify bottlenecks, forecast usage, and right‑size resources across cloud, edge, and hybrid environments.
• Reduce data pipeline costs through efficient ingestion, storage tiering, feature stores, and embedding caching while preserving data quality.
• Optimize models and inference with quantization, pruning, knowledge distillation, batching, KV‑cache reuse, and autoscaling to lower per‑request spend without degrading quality.
• Implement FinOps practices (tagging, showback, chargeback) and leverage pricing models (reserved, spot, savings plans) to align AI spend with business value and drive continuous optimization.

Who's It For:

This book is for AI/ML engineers, data scientists, MLOps practitioners, product managers, finance and FinOps professionals, and technology leaders who own AI budgets, infrastructure, or SLOs. It equips them with the tools to measure, optimize, and continuously reduce the total cost of AI projects while maintaining performance and quality across cloud, edge, and hybrid deployments.