Private cloud regains ground as AI reshapes cloud cost and risk calculus
Enterprises are pushing persistent inference, embedding caches, and retrieval layers into private or localized clouds to tame rising AI inference costs, latency and correlated outage risk, while keeping burst training and large-scale experimentation in public clouds. This hybrid posture is reinforced by shifts in data architecture toward projection-first stores, growing endpoint inference capability, and silicon-market dynamics that favor bespoke, on-prem stacks.
