Building Scalable AI Systems

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 traffic

• Choose and combine providers, models, embedders, and vector databases

• Control costs with concrete, token-level calculations

• Build robust RAG pipelines (and understand when RAG is the wrong choice)

• Handle auth, observability, monitoring, and incident response

• Operate safely with rate limits, timeouts, fallbacks, and guardrails

This 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.