Global cloud infrastructure spending surged as hyperscalers accelerated investment to support enterprise AI moving from experimentation to production.
Global spending on cloud infrastructure services reached US$110.9 billion in Q4 2025, reflecting year-on-year growth of 29%, according to Omdia.
Growth accelerated from the previous quarter, marking the sixth consecutive quarter in which the market expanded by more than 20%. As enterprise AI demand shifts from experimentation to production deployment, hyperscalers are increasing investment to expand AI infrastructure capacity.
Looking ahead to 2026, Omdia forecasts that global cloud infrastructure services spending will grow by 27%, with competitive differentiation increasingly shaped by infrastructure scale, capital efficiency and the strength of AI agent-related platform capabilities.
During the quarter, Amazon Web Services’s growth accelerated to 24%, while Microsoft Azure and Google Cloud recorded strong year-on-year growth of 39% and 50%, respectively. AI demand is no longer confined to specialised compute such as GPUs but is also driving broader infrastructure demand across CPUs, storage and networking.
As enterprise AI adoption increasingly centres on agents, workflows and data integration, organisations require infrastructure environments that can be effectively orchestrated, scaled and governed. This reinforces the role of cloud platforms as the operational foundation for AI, while continuing to support the migration of both traditional and emerging workloads to the cloud.
Meanwhile, AWS, Microsoft and Google Cloud all reported backlog growth, pointing to sustained demand and continued enterprise investment in AI and cloud infrastructure. Rising demand is also prompting hyperscalers to step up capital spending to accelerate AI infrastructure expansion.
AWS expects capital expenditure to reach US$200 billion in 2026, more than 50% above the nearly US$132 billion recorded in 2025. Microsoft reported quarterly capital expenditure of US$37.5 billion, up by nearly US$15 billion year on year. Google, meanwhile, raised its 2026 capital expenditure guidance to between US$175 billion and US$185 billion, more than double the prior year’s level.
“For cloud vendors, the challenge is no longer just about scaling capacity quickly enough to meet surging demand, but about doing so with discipline across investment pace, resource allocation and global operational efficiency,” said Rachel Brindley, Senior Director, Omdia. “As AI continues to raise infrastructure requirements while constraints remain, vendors that can expand in a more targeted and efficient way will be best positioned to lead in the next phase of competition.”
At the same time, competition is increasingly extending beyond model access and infrastructure scale toward the application layer, particularly in the development and deployment of AI agents.
“For enterprise customers, the key question is whether these capabilities can be embedded into existing systems, workflows and data environments, and then scaled reliably in production,” said Yi Zhang, Senior Analyst, Omdia. “This is pushing cloud vendors to invest more heavily in tool governance, workflow orchestration and deployment capabilities, helping AI move closer to operational use at scale.”
For example, AWS has introduced productised agent offerings such as Kiro, Amazon Quick, Transform and Connect, while Microsoft is extending agents into cloud operations and application modernisation workflows.
AWS remained the leader in the global cloud infrastructure market in Q4 2025, with a 32% market share and 24% year-over-year revenue growth, up from the previous quarter. It ended Q4 with a total backlog of US$244 billion, underscoring sustained demand.
AWS stated that Amazon Bedrock had reached a multi-billion-dollar annualised run rate, with customer spending increasing 60% quarter on quarter. In December 2025, AWS introduced Nova Forge, enabling enterprises to incorporate proprietary data during the early training stages of Amazon Nova models to build customised foundation models, known as Novellas. This supports deeper model customisation for enterprise AI agents.
AWS has also introduced productised agent solutions including Kiro, Amazon Quick, Transform and Connect, helping translate AI model capabilities into tangible business value. Meanwhile, AWS continues to expand its global infrastructure footprint, with ongoing investment in data centre capacity across Europe and the United States to support growing demand for AI compute.
Microsoft Azure remained the world’s second-largest cloud service provider in Q4 2025, with a 22% market share and year-on-year revenue growth of 39%.
Since December 2025, Microsoft has continued to expand the range of models available in Azure AI Foundry, adding models such as Mistral Large 3, GPT-5.2 and Claude Opus 4.6, further reinforcing its position as an enterprise-grade multi-model AI platform.
At the same time, Azure is moving agentic AI beyond model access and into enterprise execution. The launch of agentic cloud operations in February 2026 extended Azure Copilot into cloud operations and continuous optimisation, while new agentic capabilities introduced in March across Azure Copilot and GitHub Copilot further integrated application modernisation into an end-to-end workflow.
On the infrastructure front, Microsoft has announced that its Saudi Arabia East data centre region will open in Q4 2026, further extending its localised cloud and AI footprint.
Google Cloud held its position as the world’s third-largest cloud service provider in Q4 2025, delivering robust year-on-year growth of 50% and expanding its market share to 12%.
By the end of the quarter, it reported a total backlog of US$240 billion, up sharply from US$157.7 billion in Q3, underscoring improved demand visibility. In January 2026, Google entered a multi-year partnership with Apple to develop the next generation of Apple Foundation Models leveraging Gemini models and Google Cloud technologies.
Since December 2025, Google Cloud has continued enhancing its enterprise AI platform, Vertex AI, with additions including Gemini Embedding, Gemini 3.1 Pro and Nano Banana Pro/2 to further strengthen enterprise capabilities in retrieval, complex reasoning and multimodal generation.
Concurrently, it has strengthened enterprise AI agent readiness through tool governance in Vertex AI Agent Builder and Provisioned Throughput for stable, high-concurrency deployments.
