Public cloud has become the default platform for enterprise AI due to its speed, scalability, and managed services. However, the convenience comes with a compounding cost structure — enterprises pay not just for compute but for abstraction layers, service premiums, and provider margins. As AI portfolios grow, these costs can crowd out future investments. Cloud providers face incentives to expand services over resilience, making enterprises increasingly dependent. The recommended approach is a selective cloud strategy: use public cloud where speed and ecosystem access justify the premium, but evaluate private cloud, hybrid, or on-premises options for workloads where long-term economics matter more than time-to-launch.
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