When to Keep AI On-Prem: Data Gravity, Latency, Sovereignty, and Cost as Architecture Inputs

This article examines the architectural decision of placing AI systems on-premises versus in the cloud for enterprise workloads. It argues that as AI moves from isolated experiments into production workflows and agentic patterns, placement becomes critical. The article introduces four key inputs for decision-making: data gravity (where context lives), latency (proximity to users/actions), sovereignty (data processing and governance requirements), and cost (sustainable scaling). It provides a framework for architects to evaluate where inference, retrieval, orchestration, and policy enforcement should reside to ensure performance, compliance, and operability.