If you are still building production AI systems the same way you built REST services five years ago, you are already behind.
MCP vs APIs vs Plugins is a practical, architecture-first guide to designing scalable, production-ready LLM and AI systems in the age of agentic AI. This book explains why traditional API-first thinking breaks down when applied to large language models, how Plugins and tool calling change the execution model, and why the Model Context Protocol (MCP) is emerging as a critical orchestration layer for modern AI systems. Without hype or theory for theory's sake, it shows how real AI systems are actually built, deployed, monitored, and scaled in production.
Inside, you will learn how to choose the right integration paradigm for each workload, how to design reliable RAG pipelines, how to orchestrate multi-tool and multi-agent workflows, and how to operate LLM systems with real observability, security, and cost control. You will see how APIs, Plugins, and MCP work together in hybrid architectures, not as competing ideas but as complementary building blocks for serious AI engineering.
This book is written for AI engineers, software architects, and senior developers who need systems that work under real constraints: latency, cost, scale, security, and change. You will gain practical frameworks for architectural decision-making, clear patterns for MCP-based orchestration, and production-tested guidance for monitoring, LLMOps, and deployment. Every chapter is grounded in real scenarios drawn from startups and enterprises building AI systems that must survive real users and real traffic.
What makes this book different is its focus on architecture over tools and principles over trends. Instead of chasing frameworks, it teaches you how to reason about AI system design so your architecture remains valid as models, vendors, and platforms evolve. It does not assume MCP replaces APIs or Plugins; it shows exactly where each belongs and how to combine them safely and effectively.
If you are serious about building AI systems that scale beyond demos, survive production, and adapt to what comes next, this book was written for you. Start designing AI architectures that actually work.
