A North American producer spent most of 2024 and early 2025 doing what many revolutionary enterprises did: aggressively standardizing on the general public cloud through the use of information lakes, analytics, CI/CD, and even an excellent chunk of ERP integration. The board preferred the narrative as a result of it appeared like simplification, and simplification appeared like financial savings. Then generative AI arrived, not as a lab toy however as a mandate. “Put copilots in all places,” management mentioned. “Begin with upkeep, then procurement, then the decision middle, then engineering change orders.”
The primary pilot went dwell shortly utilizing a managed mannequin endpoint and a retrieval layer in the identical public cloud area as their information platform. It labored and everybody cheered. Then invoices began arriving. Token utilization, vector storage, accelerated compute, egress for integration flows, premium logging, premium guardrails. In the meantime, a sequence of cloud service disruptions compelled the staff into uncomfortable conversations about blast radius, dependency chains, and what “excessive availability” actually means when your utility is a tapestry of managed companies.
The ultimate straw wasn’t simply price or downtime; it was proximity. Probably the most priceless AI use circumstances have been these closest to individuals who construct and make things better. These individuals lived close to manufacturing crops with strict community boundaries, latency constraints, and operational rhythms that don’t tolerate “the supplier is investigating.” Inside six months, the corporate started shifting its AI inference and retrieval workloads to a personal cloud positioned close to its factories, whereas maintaining mannequin coaching bursts within the public cloud when it made sense. It wasn’t a retreat. It was a rebalancing.
