AI Cost Management

Control AI Infrastructure Costs and Build Stronger GenAI Economics

As artificial intelligence shifts from experimental proofs-of-concept to production-scale deployments, enterprises face a difficult reality: rising GPU, cloud compute, and API costs. AI Cost Management delivers a practical roadmap for cloud architects, DevOps engineers, and technology leaders who need clearer visibility and better control over AI budgets.

This practical guide breaks down the hardware and software architectures powering modern LLMs and shows how to build more economically sustainable systems. From improving silicon utilization to navigating API pricing models, you will learn how to align AI performance with business-first financial logic.

Inside this comprehensive handbook, you will discover:

• GPU Optimization: Techniques to improve utilization, reduce idle silicon waste, and understand advanced scheduling approaches.
• Efficient Inference & Training: Step-by-step strategies for model quantization, pruning, and lower-cost distributed training.
• Token Budgeting & Prompt Engineering: How to reduce LLM token expenses while protecting useful response quality.
• Hybrid & Serverless Architectures: Financial trade-offs of cloud vs. on-premise hardware and when serverless AI may fit.
• AI Unit Economics: How to establish tracking, tagging, and monitoring frameworks built for AI workloads

Whether you are running open-source models on-premise or consuming commercial APIs in the cloud, this book provides a practical framework to reduce waste, improve cost visibility, and support long-term AI ROI. Equip your organization with the metrics, methodologies, and architecture patterns to scale AI more responsibly. Grab your copy today!