Growing worries about AI security and compliance are fueling a surge in private cloud adoption.

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Whoa, buckle up! The shift back to private cloud ain't just about the money, it's about AI security and compliance too, according to a study by GTT Communications.

Given that, it's no wonder that private cloud investment is escalating faster than public cloud, following a pattern known as cloud repatriation.

This latest study from GTT Communications echoes findings from an earlier survey by Node4, which revealed a staggering 97% of respondents planning to move some workloads back in-house. Analyst firm IDC also reported similar results a few months back, attributing cloud repatriation to missed expectations around costs.

Perhaps the most famed example is SaaS company 37 Signals, who reportedly saved a cool $10 million annually by ditching public cloud and buying their own infrastructure. Looks like some repatriation is all about cutting costs.

GTT's research confirmed the trend, stating that the primary drivers for private cloud adoption included unpredictable public cloud costs and concerns about vendor lock-in. Interestingly, the study also hinted at some private cloud adoption being driven by privacy, security, and compliance requirements, with control over AI being a major factor.

The survey, conducted among US and European enterprise leaders, forecasts a 24% growth in private cloud spending above $10 million annually, far outpacing the 13% growth in public cloud spend for the same group of companies.

Truth be told, public cloud still holds an edge for use cases requiring high reliability and scalability.

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Is AI Surging the Growth of Private Cloud Adoption?

Seems like the move back in-house or to private cloud is partially due to AI too, as suggested by GTT's study. Surprisingly, more than half of AI workloads are already in private cloud or on-premise environments. Reasons such as better security (56%), compliance (51%), and the specific needs of AI workloads (50%) are primarily driving this shift.

While cost concerns still linger at 35%, AI applications are resource-intensive and can expose enterprises to higher, less predictable costs.

"Many companies are moving their sensitive workloads to private clouds as part of broader multi-cloud and hybrid strategies to support complex AI initiatives at scale," says Bastien Aerni, VP of Strategy and Technology Adoption, GTT.

Enterprises can either move their private data to where the AI model is hosted or move the model to where the data lives, according to the report.

Enterprises increasingly appreciate the importance and sensitivity of proprietary data for AI models and are consciously favoring private cloud for AI, the report adds.

However, complex AI workloads face challenges when it comes to repatriation, with 43% of respondents experiencing difficulties when migrating apps and data from public cloud environments.

"Organizations are evolving their cloud environments rapidly to accommodate their AI initiatives at scale," says Aerni.

"But many still underestimate the intricacies involved. In our experience, without adapting connectivity and security architectures, even the most ambitious private cloud strategies can fall short."

Growing worries about AI security and compliance are fueling a surge in private cloud adoption.