Agentic AI applications are built with various platforms based on specific requirements including scalability, latency, security, and cost-efficiency. Hyperscalers are ideal for dynamic, global agentic AI systems but incur high operational costs. Private Clouds and Sovereign Clouds provide secure environments suitable for compliance-heavy applications at moderate costs. Co-location providers are cost-effective for predictable workloads, while Edge Computing Servers support latency-critical applications like IoT and autonomous vehicles. GPU Clouds ensure optimal performance for computationally intensive tasks, though they are expensive in the long run. The right platform selection depends on workload characteristics and long-term cost considerations.