Global Cloud Spending Surged 21 Percent in 1Q25

Global spending on cloud infrastructure services, according to Canalys estimates, reached $90.9 billion in 1Q25, marking a 21% year-on-year increase. Enterprises have recognized that deploying AI applications requires a 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 optimize infrastructure, most notably through the development of proprietary chips, aimed at lowering the cost of AI usage and improving inference efficiency.

In 1Q25, 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 and Google both maintained growth rates of over 30% (although Google’s growth slowed slightly from the previous quarter), while AWS grew by 17%, a deceleration from 19% growth in 4Q24. This 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.

Overall, the global cloud services market sustained steady growth in the quarter, 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.”

“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 underutilized.”

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

Amazon Web Services (AWS) maintained its position as the market leader in 1Q25, capturing 32% of the global market share and recording a 17% year-over-year increase in revenue. Microsoft Azure remained the second-largest cloud provider, holding a 23% market share and delivering strong year-over-year growth of 33%. Google Cloud, the world’s third-largest cloud provider, maintained a 10% market share in 1Q25 and delivered strong year-over-year growth of 31%.