Recent research indicates that public cloud solutions are not the most cost-effective option for AI infrastructure. On-premises infrastructure, whether in-house or via colocation, now provides superior value for developing and scaling AI projects. The decline in hardware prices over the past ten years juxtaposed with stable cloud costs has contributed to this shift. Deloitte highlights a critical juncture known as the 'public cloud cost cliff'—when AI workload costs in the cloud reach 60-70% of dedicated infrastructure costs, favoring private infrastructure investments.
AI workloads are 'sticky,' consuming large compute volumes that require specialized GPUs or accelerators, which come at premium cloud prices. Direct purchasing of these components is now cheaper than a decade ago.
Deloitte's findings highlight the 'public cloud cost cliff'—when cloud expenses for AI workloads reach 60-70% of dedicated infrastructure costs, private infrastructure becomes the more economical choice.
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