Composable Infrastructure and AI Workload Optimisation: Combating Spiralling Power and Compute Costs
The infrastructure decisions you make as AI scales into production will define the economics of your entire programme. And the landscape of options is wider than it has ever been. Public cloud from the hyperscalers. Private cloud and on-premises deployments for workloads where control, latency, or data residency matter. Neo clouds purpose-built for AI that offer GPU-dense compute at a fraction of traditional costs. And at the edge, AI and TPU-enabled devices capable of taking on specific workloads entirely away from hosted models, reducing dependency on centralised infrastructure and the token spend that comes with it.
Getting this right is not about choosing one model over another. It is about placing each workload in the environment where it performs best and costs least. Sovereignty considerations add another dimension for organisations in regulated industries or jurisdictions with data localisation requirements – not a barrier to AI, but a factor that should shape architecture from the start. This track explores how to build a multi-environment infrastructure strategy that keeps costs proportional as production use cases multiply, and that gives your AI programme the foundation to scale without the bill running ahead of the value.