Forrester Research Identifies Hybrid Cloud Shift Driven by AI Infrastructure Economics

A new Forrester Research evaluation, detailed in its 2026 State of Cloud report, highlights a structural migration toward hybrid and multi-cloud operating models across enterprise IT estates. As organizations shift machine learning and generative AI workloads from experimental sandboxes into core production pipelines, IT leaders are migrating critical datasets and predictable workloads into managed private infrastructure while selectively leveraging public cloud hyperscalers for dynamic capacity and frontier foundation models. The driver behind this hybrid architectural adjustment is a fundamental realignment of cloud economics and compliance reality. Enterprise engineering teams are discovering that persistent AI workloads—especially continuous model fine-tuning, retrieval-augmented generation (RAG) vector searches, and high-frequency inferencing—generate compounding compute, memory, and cross-boundary network egress costs when executed entirely on public cloud on-demand instances. Furthermore, intensifying geopolitical scrutiny and data protection mandates require organizations in critical infrastructure and regulated sectors to maintain deterministic control over data gravity and physical jurisdiction. This development fits into the broader evolution of enterprise platform engineering, where multi-cloud adoption has matured from simple multi-vendor redundancy into workload-specific topology design. Rather than engaging in wholesale repatriation or single-vendor lock-in, infrastructure teams are adopting consistent control planes, unified container orchestration, and multi-cloud abstraction layers. This allows developers to consume infrastructure as code while enabling platform teams to dynamically balance AI training, inferencing, and traditional transactional systems across private data centers, specialized GPU clouds, and global public clouds based on performance and cost thresholds. For DevOps, platform engineers, and cloud architects, this shift requires a deliberate reassessment of capacity planning and workload placement. Practitioners must avoid treating AI as an external service add-on and instead design architectures assuming AI as a baseline workload constraint. Concrete operational steps include auditing egress pipelines to minimize costly inter-region data movements, implementing automated FinOps telemetry across both private and public clusters, and adopting portable deployment frameworks—such as cloud-agnostic Kubernetes distributions and standardized model-serving runtimes—so workloads can seamlessly transition between managed private infrastructure and hyperscalers as unit costs dictate.