Hybrid Multi-Cloud Is Becoming The Default Architecture For AI And HPC
DCD, Monday, April 20th, 2026
Organizations are adopting hybrid multi-cloud architectures as the standard for AI and HPC workloads due to cost, performance, and flexibility requirements.
Training, inference, simulation, and modeling each impose different compute constraints on cost, performance, power, and data locality. Trying to force workloads into the cloud or on-premises is no longer practical. Hybrid multi-cloud is emerging as the new standard for AI and HPC.
AI and HPC workloads are GPU-dense, power-hungry, often bursty, and behave differently from the enterprise applications that shaped cloud adoption over the past decade. In today's world, inference workloads require stable, predictable performance, while training jobs may demand massive parallelism for short periods. In addition, simulation and analytics pipelines may sit idle for weeks before surging unexpectedly.