Training and inference clusters change how enterprises buy cloud: GPU availability, regional power, and network topology now sit next to classic VM capacity on the planning agenda.
Most companies should not build a private AI hall. They should pick regions and instance families that match latency and data residency, then isolate training from inference so costs stay explainable.
A practical 2026 strategy is hybrid: burst training on hyperscaler GPU pools, keep sensitive inference in a controlled VPC, and measure utilisation weekly. We help design that split without freezing the rest of the estate.
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