Gartner AI Cloud Market Prediction 2030: Neoclouds to Capture 20% Share

The Gartner AI cloud market prediction 2030 points to a significant shift in how enterprises may consume cloud infrastructure over the next five years. Gartner forecasts that neocloud providers will account for 20% of the $267 billion AI cloud market by 2030. These providers are built specifically for AI and high-performance computing workloads, positioning themselves differently from traditional cloud giants.

At the centre of this change is the rapid growth of generative AI. Rising demand for GPU-intensive computing workloads is pushing organisations to seek infrastructure that can deliver high performance while supporting increasingly complex AI applications. According to Gartner, this demand is exposing limitations in traditional cloud approaches and creating opportunities for specialised providers.

Performance and sovereignty become key differentiators

Gartner notes that neocloud providers are gaining traction because they focus on AI-optimized infrastructure and specialised performance requirements. Many are also building sovereign cloud capabilities that allow data, operations, and governance to remain within specific national boundaries.

This is becoming increasingly important as enterprises face stricter data sovereignty expectations. Regulatory requirements, including GDPR obligations and upcoming transparency requirements under the EU AI Act, are encouraging organisations to take a closer look at where AI data is stored, processed, and managed. As a result, enterprises are placing greater emphasis on localised digital resilience and compliance.

Why enterprises may rethink cloud strategies

The growing neocloud providers market share reflects a broader shift in enterprise cloud strategy. Gartner suggests that organisations may need to move beyond centralised cloud models and adopt more localised or hybrid architectures. Sovereignty, infrastructure specialisation, and workload performance are emerging as major decision factors alongside cost and scalability.

For infrastructure and operations leaders, this means evaluating alternatives beyond traditional hyperscalers. Access to specialised AI infrastructure and limited GPU resources could become a strategic advantage as AI adoption continues to accelerate.

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