Production LLM Architecture
A Systems Handbook for Engineers Shipping AI
Most LLM systems in production are slower, more expensive, and more complex than they need to be.
The gap isn't the model. It's the architecture.
Production LLM Architecture is a practical engineering handbook for building production-grade LLM systems that are fast, reliable, and cost-effective. Rather than focusing on prompts or model comparisons, this book examines the engineering decisions that determine whether an AI system succeeds or fails in production.
Drawing on more than 26 years of software engineering experience and real-world production deployments, Debabrata Acharjee presents a systematic framework built around six governing laws of production LLM systems. These principles explain why latency grows, costs increase, context windows become inefficient, and orchestration becomes increasingly difficult as systems scale.
Inside you'll learn how to:
- Reduce API costs from $11,000/month to roughly $400/month.
- Cut response latency from 17 seconds to under 2 seconds.
- Design efficient context architectures that minimise token waste.
- Build scalable multi-agent systems and orchestration pipelines.
- Implement caching, routing, evaluation, and guardrails.
- Diagnose and prevent 24 production failure modes that emerge at scale.
- Design AI systems that remain reliable as traffic and complexity increase.
This book covers:
- Inference mechanics and KV cache optimisation
- Context architecture and information density
- Prompt caching and token efficiency
- Routing strategies and model selection
- Agent orchestration patterns
- Retrieval and execution architecture
- Evaluation frameworks and guardrails
- Observability, monitoring, and deployment
Whether you're building internal AI copilots, customer-facing AI products, or enterprise AI platforms, this book provides practical techniques, architectural patterns, and engineering trade-offs that can be applied immediately.
Who should read this book?
- Software Engineers
- Staff and Principal Engineers
- AI Engineers
- Platform Engineers
- Engineering Managers
- Technical Architects
- Teams building production AI systems
Inside the book:
- 14 comprehensive chapters
- 75 production-focused code examples
- 62 engineering tables and diagrams
- 24 real-world production failure modes
If you're responsible for moving beyond AI demos and delivering production LLM systems that are fast, scalable, and reliable, Production LLM Architecture provides the engineering principles and practical patterns to help you build them with confidence.
