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Recent data from Omdia shows that global cloud infrastructure spending reached US$110.9 billion in the fourth quarter of 2025, up 29% year over year. The firm said growth was driven by AI, with demand expanding to storage and networking. Full-year spending reached US$399.6 billion, up 24% year over year.
Amazon Web Services, Microsoft Azure, and Google Cloud are seeing increased use tied to AI models that require more compute and network capacity, the company says.
Running AI in production requires steady access to high-performance infrastructure. Models need to process large datasets and respond in near real time. They also need to scale as use grows. These needs push companies to rely more heavily on cloud platforms not on-premise systems.
Analysts at Canalys also identify AI as an important driver of recent growth in cloud spending, with demand rising due to increased AI consumption and enterprise adoption.
The apparent growth in AI use places pressure on several parts of cloud infrastructure. Storage needs increase and network use grows as data moves between systems and users. A single AI application can require more resources than a traditional business system. When companies scale these tools in departments, the total demand rises quickly.
Cloud providers have been expanding data centre capacity and offering specialised hardware like GPUs and custom chips for AI workloads, though these come at a higher cost.
Pricing models are complex, with companies paying for storage, compute, data transfer, model training time, and inference use, making it harder for businesses to predict and control spending. Companies are committing larger sums to maintain and scale AI systems, so cloud spending becomes part of core operating costs.
Omdia expects cloud infrastructure spending to grow by a further 27% in 2026, which would push total annual spending beyond US$500 billion.
IT teams now need to monitor use more closely and optimise workloads. They also need to decide where to run different parts of an application. Some are exploring hybrid setups, with certain workloads on private systems and others in the cloud.
Higher cloud spending is raising questions about long-term cost management. AI tools can improve efficiency, but they can also introduce expenses that are hard to track. Companies are looking more closely at how they use cloud resources. This includes which workloads need high-performance infrastructure and which can run on lower-cost systems. They are also reviewing how use is tracked.
Some firms are also reconsidering their cloud strategies by spreading workloads across multiple platforms to lessen dependence on one vendor; the strategy can, however, add complexity. Cloud providers are offering new pricing options and tools to manage costs, including use dashboards, cost alerts, and reserved capacity plans.
Cloud as core infrastructure
Recent market data indicates that cloud platforms are now crucial to how businesses run modern applications, with cloud companies stating AI acts as a driver of infrastructure demand.
As AI spreads in industries, the link between AI adoption and cloud use is likely to grow stronger. The change is redefining what cloud infrastructure is used for, and how much companies are willing to spend on it.
(Photo by 金 运)
See also: Cloud demand shifts toward AI as enterprise use deepens
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