LLM Architecture for the Enterprise

Enterprise LLM deployment fails in predictable ways — a surprise $200K 
invoice, a compliance breach, or a production system that quietly degrades 
for months before anyone notices.

This book gives AI architects, engineering leads, and CTOs the frameworks 
to deploy, maintain, and fine-tune LLMs at their actual scale. Organized 
around three enterprise tiers — small, medium, and large — it provides 
separate architecture patterns, cost models, and team structures for each, 
because a 50-person startup and a global bank have fundamentally different 
LLM problems.

What's inside:

• The Make/Buy/Rent/Borrow framework for every LLM deployment decision
• Realistic TCO models — including the hidden costs that bust most budgets
• Fine-tuning decision frameworks: when RAG is the better answer (and when it isn't)
• Production maintenance: model lifecycle, evaluation pipelines, and MLOps
• Security, HIPAA/GDPR/SOC2 compliance paths, and responsible AI governance
• 6 extended case studies from real enterprise deployments
• Python code throughout: QLoRA training, model routing, eval harnesses

Every chapter ends with a Decision Checklist you can bring directly to 
your organization.

Written by a VP and AI Platform Architect with 20+ years of enterprise 
systems experience across financial services, healthcare IT, and cloud 
infrastructure — covering 24 chapters and 6 appendices across ~380 pages.