Gartner: AI-Optimized IaaS Spending to Reach $42B in 2026
Gartner forecasts worldwide AI-optimized IaaS spending will reach $42.3 billion in 2026, a 96.4% increase, as enterprises shift to production-scale AI, with inference workloads surpassing training.
Worldwide spending on AI-optimized infrastructure as a service (IaaS) is forecast to reach $42.3 billion in 2026, up 96.4% from 2025, as enterprises move artificial intelligence from model development into production-scale deployment, Gartner said on Aug. 10. The shift matters for cloud providers, CIOs, CTOs and enterprise technology buyers because inference is expected to overtake training as the largest AI infrastructure workload, increasing demand for computing capacity that can support continuous, real-time AI applications.
AI Infrastructure Spending Nearly Doubles in 2026
According to Gartner’s forecast, AI-optimized IaaS spending is expected to rise from $21.5 billion in 2025 to $42.3 billion in 2026 before reaching $66.1 billion in 2027, a further 56.5% increase. By comparison, total worldwide IaaS spending is projected at $287.3 billion in 2026 and $359.9 billion in 2027.
Inference Overtakes Training as the Largest Workload
The acceleration reflects a change in how enterprises are using AI infrastructure. Gartner said spending on inference workloads will reach $23.3 billion in 2026, exceeding the $19 billion expected for training. Inference is forecast to account for 55% of AI-optimized IaaS spending this year and 59% in 2027.
Hardeep Singh, senior principal research analyst at Gartner, attributed the increase to continued infrastructure demand for large language model training and the operationalization of AI across enterprise applications and workflows. Gartner also pointed to the expansion of agentic AI, where systems perform multistep tasks and require repeated model execution, as a factor increasing inference consumption.
Production AI Raises the Stakes for SaaS Economics
For B2B software and SaaS companies, the spending shift puts greater attention on the economics of running AI features after they reach production. Some cloud economists caution that inference cost growth may be offset by efficiency gains in model compression and hardware optimization. Applications using copilots, autonomous agents, recommendation systems, search, customer support automation or real-time analytics can create recurring compute requirements whenever models process requests. That makes inference efficiency, latency, capacity planning and cloud costs increasingly important as usage grows.
For CIOs and CTOs, the shift increases the importance of infrastructure architecture, capacity planning and vendor strategy as AI workloads become part of day-to-day operations. Finance and procurement teams may also face greater scrutiny over cloud commitments and recurring inference costs, particularly as AI usage scales across employees, customers and business processes.
Infrastructure Investment Expands Alongside AI Software Spending
The infrastructure forecast also adds context to broader enterprise AI spending. In July, We reported that Gartner expects AI platform and model spending to reach $64.25 billion in 2026, with buyers placing greater emphasis on cost, reliability, governance and measurable business outcomes. The latest forecast shows that spending on models and software is being accompanied by substantial investment in the cloud infrastructure required to operate AI systems at scale.
Cloud and infrastructure suppliers are also expanding capacity for AI workloads. Oracle’s planned investment of up to $50 billion in AI infrastructure and Microsoft-3M partnership focused partly on AI data-center infrastructure illustrate how computing, networking and power capacity are becoming more closely connected to enterprise AI deployment.
The effects extend beyond cloud providers to data-center operators, networking suppliers and power infrastructure companies, which may see greater demand as providers add the physical capacity needed to support expanding AI workloads.
AI-Optimized Services Gain Share of the IaaS Market
Gartner’s figures indicate that AI-optimized services are taking a larger role within the broader IaaS market. Based on its forecasts, AI-optimized IaaS would represent roughly 15% of total IaaS spending in 2026, up from about 10% in 2025, and could approach 18% in 2027.
The next stage of infrastructure spending will increasingly center on production use rather than model training alone. Gartner’s forecast points to inference taking a larger share of AI infrastructure budgets through 2027 as enterprises embed AI into operational and customer-facing systems.
