Building Scalable AI Systems: A Practical Guide to Production-Ready AI Infrastructure

Build AI systems that don’t fall apart in production.This book is a practical field guide for senior developers, AI engineers, DevOps professionals, and technical leaders who need to ship real AI products at scale — not just demos.Originally written in 2024 as an internal playbook while designing Arcannia, an AI memory infrastructure system, this guide distills years of hard lessons from building RAG systems, fine-tuning models, scaling APIs, and paying the GPU bills. The patterns and trade-offs here are the same ones used as foundations for Arcannia’s architecture.Inside, you’ll learn how to:Design end-to-end AI architectures that survive real trafficChoose and combine providers, models, embedders, and vector databasesControl costs with concrete, token-level calculationsBuild robust RAG pipelines (and understand when RAG is the wrong choice)Handle auth, observability, monitoring, and incident responseOperate safely with rate limits, timeouts, fallbacks, and guardrailsThis is not a “what is AI?” introduction and not a toy chatbot tutorial. You are expected to be comfortable with TypeScript, Python, Docker, and modern cloud tooling.If you’re responsible for making AI systems reliable, affordable, and secure — and you want battle-tested patterns instead of hype — this book is your shortcut to production-grade AI infrastructure. Read more