As machine learning across industries grows, the choice of infrastructure becomes increasingly important. Infrastructure determines model scalability, performance, and cost-effectiveness. Machine learning workloads need significant storage, compute power, and data management capabilities.25 Thus, selecting suitable infrastructure is complex and very impactful. Organizations must decide to deploy ML models in the cloud, on-premise, or in a hybrid approach. By on-premise, we refer to infrastructure hosted and managed within the organization’s own environment (e.g., in-house servers or local data centers). For this purpose, they should consider factors such as security, cost, and performance.