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AI Cost Optimization System Design for AI Engineers Design scalable LLM, RAG, and agentic AI systems that reduce token

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AI Cost Optimization System Design for AI Engineers: Design scalable LLM, RAG, and agentic AI systems that reduce token spend, control inference costs, and improve production ROI.
English | June 11, 2026 | ASIN: B0H4XDHVZR | 123 pages | PDF | 3.25 MB
Stop Letting Your AI Architecture Quietly Destroy Your Profit MarginsBuilding an AI system that works is only the beginning. The real challenge is designing one that remains fast, reliable, and financially sustainable as usage grows.AI Cost Optimization System Design for AI Engineers is a practical guide to controlling the cost of production LLM, RAG, and agentic AI systems without sacrificing the quality users expect.Rather than focusing on temporary provider prices, this book teaches durable engineering principles for understanding where AI spend originates, measuring cost per successful task, and selecting the highest-impact optimization strategy.Inside, you will learn how to:Design cost-aware architectures for LLM applicationsReduce input, output, reasoning, and hidden-token usageSelect and route requests to the right modelBuild efficient RAG pipelines with optimized retrieval and rerankingIncrease savings through prompt, semantic, and provider cachingPrevent runaway agent loops, tool calls, and multi-agent workflowsOptimize GPU inference, batching, and serving infrastructureMeasure cost per request, user, feature, and successful taskEstablish AI FinOps dashboards, budgets, alerts, and governanceBalance cost, quality, latency, reliability, and business marginEach chapter combines system-design principles with formulas, architecture diagrams, implementation patterns, production case studies, optimization checklists, common mistakes, summary notes, and senior-level interview questions.The book also includes practical projects covering model routing, RAG optimization, agent budget control, caching, observability, evaluation, and cost-aware AI platform design.This book is ideal for:AI and machine learning engineersLLMOps, MLOps, and platform engineersAgentic AI developersSolutions architects and technical leadersFounders building AI-powered productsEngineers preparing for senior AI system-design interviewsWhether you are reducing an unexpectedly large AI bill, designing a new production platform, or preparing for your next engineering interview, this book gives you the frameworks needed to make every AI request economically defensible.Build AI systems that do more than work. Build systems that can afford to scale.​


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