Canalys: Global Cloud Infrastructure Spending Surges in Q1 2025

Global spending on cloud infrastructure services reached US$90.9 billion in Q1 2025, marking a 21% year-over-year increase. Based on research by Canalys, enterprises have recognized that deploying AI applications requires renewed emphasis on cloud migration. Meanwhile, to accelerate the enterprise adoption of AI at scale, leading cloud providers are intensifying efforts to optimize infrastructure, most notably through the development of proprietary chips aimed at lowering the cost of AI usage and improving inference efficiency.

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.

Growth momentum diverged among the top players. Microsoft Azure and Google Cloud both maintained growth rates of over 30% (although Google Cloud’s growth slowed slightly from the previous quarter), while AWS grew by 17%, a deceleration from 19% growth in Q4 2024. This deceleration was largely driven by supply-side constraints, which limited the ability to meet rapidly rising AI-related demand, according to Canalys. In response, cloud hyperscalers have continued to invest aggressively in AI infrastructure to expand capacity and position themselves for long-term growth.

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 generative AI, which relies heavily on cloud infrastructure, has in turn reinforced enterprise cloud strategies and hastened migration timelines.

“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 commercialization.”

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