Worldwide AI-optimised infrastructure as a service (IaaS) spending is projected to grow 96% through 2026, reaching $42-billion, according to Gartner.
“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,” says Hardeep Singh, senior principal research analyst at Gartner.
The market is forecast to sustain its high growth and reach $66-billion in 2027.
The rise of agentic AI amplifies compute intensity through multistep, autonomous execution, making inference the dominant consumption model and positioning AI-optimised IaaS as a critical enabler of enterprise AI strategies.
“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, realtime execution rather than periodic training,” says Singh. “This shift is accelerating the cloud consumption patterns and creating sustained demand for AI-optimised infrastructure.”
In 2026, global spending on inference ($23,3-billion) will surpass that of training ($19-billion). Fifty-five percent of AI-optimised IaaS spending is forecast to support inference in 2026 and is set to reach 59% in 2027. The growing share of inference workloads is expected to reshape cloud investment priorities.