AI economics are exposing a gap between what people think the cloud costs and what the cloud actually costs.

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Enterprises are shifting AI inference workloads from public to private clouds due to escalating costs, reliability concerns, and proximity requirements. AI workloads differ from traditional applications: they're GPU-intensive, scale rapidly and stay scaled, and generate unpredictable costs through token usage, vector storage, and accelerated compute. Private clouds offer predictable capacity, reduced dependency chains, and better control over unit economics. Key drivers include data locality needs, operational maturity requirements, and the desire to keep AI systems close to manufacturing plants and core processes. Success requires treating unit economics as a design requirement, planning for data locality, operationalizing GPU capacity with governance, and building resilience through reduced dependency chains.

The private cloud returns, for AI workloads