AI Cloud Migration: Key Steps to Success
TechTarget, Tuesday, September 8th, 2026
Moving AI workloads to public cloud requires assessment, modernization, careful data planning, phased rollout and ongoing governance.
Local AI deployments are costly to build and power and can limit scalability, pushing companies to host LLMs and AI platforms in public clouds.
TechTarget's Stephen J. Bigelow warns that lift-and-shift migrations rarely work well for AI and can degrade performance.
Teams should map every model, agent, pipeline and dependency, and assess data constraints such as PII or intellectual property that may require local handling.
Components should be modernized by refactoring, rearchitecting or rebuilding for cloud-native infrastructure, followed by a well-structured data pipeline, phased deployment and continuous management and governance.