The new AWS instance types indicate that public clouds recognize this problem and are attempting to address it by offering tailored solutions for on-prem environments. However, this is more of an exception than a rule, so far. Public clouds remain heavily invested in premium pricing models for their centralized services, which won’t work for enterprises looking to scale AI operations at a reasonable price point.
The path forward
Strategic decisions must support scalability, cost-efficiency, and innovation in an AI-driven future. Simply put, enterprises need to take control of their infrastructure decisions rather than being wholly reliant on public cloud services.
First, organizations must deeply analyze their workloads. Developing a clear understanding of where to place each workload—public cloud, on-prem, or hybrid cloud model—is essential. AI workloads, in particular, should be examined through the lens of performance needs, latency requirements, and long-term cost implications. The public cloud might be ideal for development and initial testing, but long-term operations will often benefit from more cost-effective on-premises solutions.
