Global spending on cloud infrastructure services rose 21% year-on-year, reaching $90.9 billion in the first quarter of 2025, analysis from Canalys says.

In Q1 2025, the ranking of the top three cloud providers (AWS, Microsoft Azure, and Google Cloud) remained unchanged from the previous quarter, with their combined market share accounting for 65% of global cloud spending. Collectively, the three hyperscalers recorded a 24% year-on-year increase in cloud-related spending.

However, growth among the three top players in cloud diverged. While all three saw growth, AWS, currently the top player in the cloud game, only grew by 17%, a deceleration from 19% in the last quarter of 2024, and trailing behind the over 30% growth rates seen by both its competitors.

The deceleration was largely driven by supply-side constraints, which limited the ability to meet rapidly rising AI-related demand. In response, cloud hyperscalers have continued to invest aggressively in AI infrastructure to expand capacity and position themselves for long-term growth.

When it comes to overall cloud growth, however, enterprises have recognised that deploying AI applications requires renewed emphasis on cloud migration.

Large-scale investment in both cloud and AI infrastructure remains a defining theme of the market in 2025.

Meanwhile, to accelerate the enterprise adoption of AI at scale, leading cloud providers are intensifying efforts to optimise infrastructure – most notably through the development of propriety chips – aimed at lowering the cost of AI usage and improving inference efficiency.

Overall, the global cloud services market sustained steady growth in Q1 2025, as enterprises sharpened their focus on two strategic priorities: accelerating cloud migration – either by shifting additional workloads or reviving stalled on-premises transitions – and exploring the adoption of generative AI.

The rise of genAI, which relies heavily on cloud infrastructure, has in turn reinforced enterprise cloud strategies and hastened migration timelines.

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“As AI transitions from research to large-scale deployment, enterprises are increasingly focused on the cost-efficiency of inference, comparing models, cloud platforms, and hardware architectures such as GPUs versus custom accelerators,” said Rachel Brindley, senior director at Canalys.

“Unlike training, which is a one-time investment, inference represents a recurring operational cost, making it a critical constraint on the path to AI commercialisation.”

“Many AI services today follow usage-based pricing models—typically charging by token or API call—which makes cost forecasting increasingly difficult as usage scales,” added Yi Zhang, analyst at Canalys.

“When inference costs are volatile or excessively high, enterprises are forced to restrict usage, reduce model complexity, or limit deployment to high-value scenarios. As a result, the broader potential of AI remains underutilised.”

To address these challenges, leading cloud providers are deepening their investments in AI-optimised infrastructure. Hyberscalers including AWS, Azure, and Google Cloud have introduces proprietary chips such as Trainium and TPU, and purpose-built instance families, all aimed at improving inference efficiency and reducing total cost of AI.