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Reducing AI Cloud Spend: AI Cost Optimization for AWS, Azure & Google Cloud
Paperback
Series: The Economics of AI
Investing & FinanceApplications
Publisher: Independently Published
Published: Jul 8 2026
Pages: 252
Weight: 0.75
Height: 0.53 Width: 6.00 Depth: 9.00
Language: English
Artificial Intelligence is transforming every industry, but many organizations are discovering an uncomfortable reality: AI projects can become surprisingly expensive.
From GPUs and LLM inference to vector databases, Retrieval-Augmented Generation (RAG), AI agents, model training, cloud networking, and managed AI services, cloud costs can grow faster than the value they deliver.
Reducing AI Cloud Spend is a practical guide to designing, building, and operating cost-efficient AI systems across Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
Rather than focusing on a single technology, this book examines the complete economics of modern AI infrastructure and explains how architectural decisions directly influence long-term cloud spending. Whether you are building an enterprise chatbot, a RAG application, an AI search platform, an agentic workflow, or a large-scale machine learning system, this book provides proven strategies for reducing costs without sacrificing performance, scalability, security, or innovation.
Inside this book, you'll learn how to:
- Understand the true cost drivers of modern AI workloads
- Design cloud-native AI architectures that scale efficiently
- Optimize GPU, CPU, TPU, and AI accelerator usage
- Reduce LLM inference costs through better model selection, prompt engineering, caching, batching, and context optimization
- Build cost-effective vector search and Retrieval-Augmented Generation (RAG) systems
- Optimize AI agents, multi-agent workflows, tool calling, memory, and orchestration
- Reduce the operational costs of LangChain, LangGraph, CrewAI, Semantic Kernel, OpenAI Agents SDK, Model Context Protocol (MCP), and other AI frameworks
- Control training, fine-tuning, LoRA, QLoRA, PEFT, and MLOps infrastructure costs
- Optimize storage, networking, streaming data pipelines, Kafka, logging, monitoring, backups, and observability
- Apply AI FinOps practices, including budgeting, cost allocation, tagging, chargeback, governance, ROI measurement, and unit economics
- Build an enterprise AI cost optimization framework that supports long-term growth
The book also includes comprehensive cloud-specific reference guides for AWS, Azure, and Google Cloud covering AI services, GPU comparisons, managed AI platforms, vector databases, pricing considerations, optimization checklists, architecture review templates, and practical cost calculator worksheets.
Unlike books that focus solely on one cloud provider or one AI framework, Reducing AI Cloud Spend presents a cloud-agnostic approach grounded in sound engineering principles. It emphasizes measurable business value, thoughtful architecture, operational excellence, and continuous optimization rather than short-term cost cutting.
Whether you are a CTO, Solutions Architect, AI Engineer, Platform Engineer, Cloud Engineer, Engineering Manager, FinOps practitioner, Machine Learning Engineer, Technical Product Manager, or technology leader responsible for AI initiatives, this book provides the knowledge and practical frameworks needed to build intelligent systems that are both innovative and financially sustainable.
The future of AI belongs not only to organizations that build powerful models, but to those that build them efficiently.
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