Key Takeaways
- Enterprises are accelerating private cloud spending—up 24% year-over-year—driven by AI workloads, security, and regulatory compliance.
- Over half of all AI workloads now reside in private cloud or on-prem environments, according to new survey data from GTT and Hanover Research.
- Post-migration challenges and cost optimization are more pronounced in public cloud environments compared to private cloud adoption.
Private Cloud Rebounds as AI Demands Greater Control, GTT Study Reveals
As enterprises deploy more advanced AI systems, concerns around security, compliance, and governance are reshaping cloud strategies. According to a new study commissioned by GTT and conducted by Hanover Research, organizations are moving workloads out of public cloud environments at a growing pace and reinvesting in private cloud infrastructure—an approach increasingly aligned with emerging AI needs.
The “Cloud Usage and Management Trends” white paper, based on responses from 283 enterprise IT and infrastructure leaders across the U.S. and Europe, shows that private cloud spending is rising significantly faster than public cloud in several key segments. Among organizations spending over $10 million per year on cloud infrastructure, private cloud investments are projected to grow 24% between 2024 and 2025—double the 12% growth rate expected for public cloud in the same cohort.
This trend is being described by some analysts as a form of “cloud repatriation,” where enterprises reallocate critical workloads to environments that offer greater control and isolation. While cost remains a factor, security, compliance, and workload-specific AI requirements are the primary drivers of this shift.
AI Demands Drive Infrastructure Redesign
“We know many companies are now shifting their sensitive workloads to private clouds as part of broader multi-cloud and hybrid strategies designed to support agentic AI and other complex AI initiatives at scale,” said Bastien Aerni, VP of Strategy and Technology Adoption at GTT.
Aerni explained that the increased use of AI across industries has highlighted the limitations of a purely public cloud approach. “Organizations are refining their cloud environments to support their AI initiatives at scale. But many still underestimate the complexity involved. Without reengineering connectivity and security architectures, even the most ambitious private cloud strategies can fall short.”
According to the study, 56% of respondents cited security as a primary reason for deploying AI workloads to private or on-premises environments. Other top drivers included compliance and regulatory mandates (51%) and specific workload requirements unique to AI models (50%). While cost was noted by 35%, it ranked lower than operational and risk-based factors.
Hybrid and Multi-Cloud Models Become the Norm
Instead of a full retreat from public cloud, the data suggests that most enterprises are embracing hybrid and multi-cloud strategies to manage AI complexity. In this approach, public cloud continues to serve less sensitive workloads, while private cloud and on-premises environments are used to handle data or models that require strict governance and control.
However, managing this complexity is a challenge in its own right. The survey found that post-migration challenges differ notably between environments. Public cloud users cited app and data migration as top concerns (43%), along with technical skill gaps. For private cloud users, the biggest issues were managing applications post-migration, securing hybrid environments, and feasibility—each cited by 38% of respondents.
This reinforces the notion that the decision between public and private cloud is less about performance and more about where enterprises can most effectively maintain visibility, control, and compliance.
AI Security and Governance Take Center Stage
The growing reliance on AI in sensitive enterprise functions—from financial forecasting to product design—has made security and governance central to cloud decision-making.
GTT notes that demand for AI-specific security architectures is rising, including support for agentic systems, AI lifecycle visibility, and runtime protection. These capabilities are often more feasible in private cloud setups, where IT and security teams can define and enforce tighter controls.
“Enterprise infrastructure strategies are evolving fast as AI workloads surpass the limits of traditional architectures,” said Aerni. “Without a corresponding evolution in networking and security infrastructure, enterprises risk bottlenecks and vulnerabilities that could derail their AI initiatives.”
Cross-Industry Participation Reflects Broad Demand
The survey sampled organizations with at least six locations and $200 million or more in annual revenue across industries such as healthcare, financial services, manufacturing, hospitality, and technology. This diversity suggests that AI-driven cloud transformation is not confined to a few verticals—it’s a broader trend touching nearly every enterprise sector.
From a network infrastructure standpoint, the resurgence in private cloud spending is creating downstream demand for secure connectivity, SD-WAN, and managed services that support hybrid architectures. GTT, which offers SASE, SD-WAN, security, and other services via its global Tier 1 IP network, says these capabilities are increasingly core to successful AI execution.
Looking Ahead
As AI adoption accelerates, organizations will likely continue investing in infrastructure models that offer greater control over data residency, compliance, and performance. For many, that means building or expanding private cloud capacity, either in-house or through managed services.
This transition won’t be without hurdles—especially around integration, post-migration optimization, and skills gaps—but the benefits in governance and risk reduction are proving persuasive.
GTT’s white paper is a timely snapshot of where enterprise priorities are heading. As more AI systems go live and regulations like the EU AI Act tighten oversight, the case for rethinking cloud strategy—starting with workload placement—is likely to become more urgent.
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Rich Tehrani serves as CEO of TMC and chairman of ITEXPO #TECHSUPERSHOW Feb 10-12, 2026 and is CEO of RT Advisors and is a Registered Representative (investment banker) with and offering securities through Four Points Capital Partners LLC (Four Points) (Member FINRA/SIPC). He handles capital/debt raises as well as M&A. RT Advisors is not owned by Four Points.
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