A growing number of enterprises are moving their artificial intelligence workloads from public cloud platforms to private cloud environments, signaling a major shift in how organizations deploy and manage AI.

According to a new survey of 1,800 senior IT decision-makers conducted by Radius Tech on behalf of Broadcom, private clouds are becoming the preferred destination for AI inference workloads. While public cloud usage for AI inference has fallen from 56% last year to 41%, private cloud adoption has edged upward, reaching 56%.

Figure 1. Private Clouds.

Industry experts describe the trend as a turning point in enterprise AI strategy. As organizations move beyond experimentation and pilot projects, they are increasingly looking to run AI systems closer to where their data is stored and generated. For many businesses, that means keeping workloads within their own private cloud infrastructure.

The shift is being driven by several key factors. Security and regulatory compliance remain the top concerns, followed by data sovereignty, performance requirements, and predictable operating costs. Enterprises are also becoming more cautious about the expenses associated with generative AI and agentic AI systems, which can dramatically increase computing demands and infrastructure spending. Figure 1. private clouds.

The survey found that nearly two-thirds of IT leaders are highly concerned about the rising costs of supporting advanced AI applications. At the same time, many organizations are reassessing their broader cloud strategies. Half of the surveyed enterprises have already moved some workloads back from public clouds to private environments, while another third is considering similar actions.

Cost efficiency is part of the equation, but it is not the only consideration. Respondents ranked security and compliance as the most important factor influencing deployment decisions, ahead of performance, data control, and integration with existing systems. Many organizations also reported significant waste in public cloud spending, highlighting growing concerns about long-term cost management.

AI workloads present unique challenges compared with traditional applications. They often require access to massive datasets, specialized accelerators such as GPUs, advanced networking capabilities, and stricter governance controls. These requirements can make private cloud deployments particularly attractive for organizations seeking greater oversight and operational predictability.

Outside the United States, data sovereignty concerns are adding further momentum to the trend. With most major public cloud providers headquartered in the U.S., enterprises in other regions are increasingly focused on ensuring that sensitive information remains compliant with local regulations [1]. Private cloud environments are often viewed as offering stronger safeguards and greater control over where data is stored and processed.

While public cloud platforms continue to play a critical role for many AI applications, especially those requiring flexibility and specialized services, the assumption that all enterprise workloads will eventually migrate to public clouds is fading. Instead, organizations are adopting a more balanced approach, choosing deployment models based on security, governance, performance, and economic considerations.

As AI adoption accelerates, private clouds are emerging as a central pillar of enterprise infrastructure, offering the control, compliance, and predictability that many organizations now consider essential for large-scale AI operations.

Reference:

1. https://www.networkworld.com/article/4182967/ai-inference-moving-to-private-clouds-broadcom-says.html

Cite this article:

Keerthana S (2026), Why AI Inference is Migrating to Private Clouds, AnaTechMaz, pp.191