Enterprises Shift AI Workloads to Neoclouds, Facing 3 Operational Trade-Offs

1 articles · Updated · InfoWorld · Aug 14

Enterprises moving AI into production are increasingly weighing neoclouds because GPU capacity on AWS, Azure and Google Cloud is costly, constrained or slow to secure.
3 operating gaps stand out versus hyperscalers: security controls are often less mature, performance management is more hardware-specific, and disaster recovery needs more explicit planning.
Security teams may need greater direct ownership of identity, encryption, logging, tenant isolation and provider access because neocloud controls can vary widely by vendor.
Performance also shifts closer to infrastructure economics, with GPU type, interconnects, storage throughput and scheduling directly determining utilization and whether AI cost savings hold up.
The broader takeaway is that neoclouds are not replacing hyperscalers; they are a specialized option whose long-term value depends on managing resilience and operations provider by provider.