Global spending on AI-optimised infrastructure as a service (IaaS) is expected to rise 96.4% in 2026 to $42.3 billion, according to Gartner.
The technology research and advisory firm expects spending to continue growing in 2027, reaching $66.1 billion, up 56.5% from 2026.
“This growth is driven by continued demand for infrastructure to support large language model (LLM) training and the rapid operationalization of AI across enterprise applications and workflows,” said Hardeep Singh, senior Principal Research Analyst at Gartner.
Overall IaaS spending worldwide is forecast to reach $287.3 billion in 2026, up 29.3% from $222.2 billion in 2025. It is expected to rise to $359.9 billion in 2027.
Spending on AI inference is expected to exceed spending on AI training globally in 2026.
Gartner forecasts $23.3 billion in spending on inference workloads in 2026, compared with $19 billion on training.
Inference is expected to account for 55% of AI-optimised IaaS spending in 2026. Its share is forecast to rise to 59% in 2027.
The growing use of agentic AI, which involves systems carrying out multiple steps autonomously, is increasing demand for computing resources. As AI moves from model development into production, companies are also using fine-tuned and domain-specific models in customer-facing and operational systems.
“As organizations 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,” said Singh. “This shift is accelerating the cloud consumption patterns and creating sustained demand for AI-optimized infrastructure.”
Gartner expects the increasing use of inference workloads to influence how businesses allocate their cloud infrastructure spending.