Worldwide spending on artificial intelligence-optimised infrastructure as a service is forecast to reach $42.3 billion in 2026, up 96.4% from $21.5 billion in 2025, according to Gartner. The research firm expects AI-optimised IaaS spending to rise a further 56.5% to $66.1 billion in 2027. By comparison, total IaaS spending is forecast at $287.3 billion in 2026, representing growth of 29.3%, before increasing 25.2% to $359.9 billion in 2027.
Gartner attributed demand for AI-focused infrastructure to spending on large language model training and the deployment of AI across enterprise applications and workflows. “This growth is driven by continued demand for infrastructure to support large language model (LLM) training and the rapid operationalisation of AI across enterprise applications and workflows,” said Hardeep Singh, senior principal research analyst at Gartner.
Inference workloads are expected to account for an increasing share of AI infrastructure spending as organisations move models into production. Gartner forecasts global spending on inference infrastructure at $23.3 billion in 2026, exceeding the $19 billion expected to be spent on training.
Inference is projected to account for 55% of AI-optimised IaaS spending in 2026 and 59% in 2027. Gartner said the shift reflects greater use of fine-tuned and domain-specific models in customer-facing and operational systems, where models require continuous execution.
“As organisations shift from model development to production-scale deployment, fine-tuned and domain-specific models (DSMs) are increasingly integrated into customer-facing and operational systems, requiring continuous, real-time execution rather than periodic training,” Singh said. Gartner also expects agentic AI systems, which can carry out multistep tasks autonomously, to contribute to inference demand because their operation requires repeated computing resources during execution.
