AI Behind Your Own Walls is the definitive 2025-2026 playbook for transitioning from risky public AI to secure enterprise infrastructure. It argues the "intelligence gap" has closed, with private models like Llama 4 and DeepSeek-V3 now matching GPT-4o, effectively removing the technical need to expose sensitive data to public APIs.
The book provides a rigorous financial and technical framework, demonstrating that for high-volume workloads, on-premise clusters (using NVIDIA H200/B200) offer a Total Cost of Ownership (TCO) significantly lower than cloud options.
It details actionable blueprints for two sovereign paths: "True On-Premise" air-gapped systems and "Secure Private Cloud" with Zero Data Retention (ZDR) and customer-managed keys.
Packed with Terraform modules, hardware sizing guides, and compliance checklists (EU AI Act, DORA) , it is an engineering manual for building leak-proof AI fortresses.
AI Behind Your Own Walls is the definitive 2025-2026 playbook for transitioning from risky public AI to secure enterprise infrastructure. It argues the "intelligence gap" has closed, with private models like Llama 4 and DeepSeek-V3 now matching GPT-4o, effectively removing the technical need to expose sensitive data to public APIs.
The book provides a rigorous financial and technical framework, demonstrating that for high-volume workloads, on-premise clusters (using NVIDIA H200/B200) offer a Total Cost of Ownership (TCO) significantly lower than cloud options.
It details actionable blueprints for two sovereign paths: "True On-Premise" air-gapped systems and "Secure Private Cloud" with Zero Data Retention (ZDR) and customer-managed keys.
Packed with Terraform modules, hardware sizing guides, and compliance checklists (EU AI Act, DORA) , it is an engineering manual for building leak-proof AI fortresses.
