For the first time, enterprises are spending more on running AI models than building them, according to a Gartner forecast released Monday. Worldwide spending on AI-optimized infrastructure-as-a-service will reach $42 billion in 2026, nearly double the $21.5 billion recorded in 2025. Of that total, $23.3 billion will go toward inference workloads while $19 billion goes to training. Gartner projects AI-optimized IaaS growing 96% year over year in 2026, while the broader cloud infrastructure market is projected to reach $287.3 billion in 2026, up 29.3% from 2025. AI-optimized IaaS is roughly 15% of total IaaS. The trajectory is expected to continue with $66 billion in 2027, a further 56.5% increase, with inference's share of the total climbing from 55% to 59%. Training spending will continue to grow in absolute terms, but its relative share will keep shrinking. Hardeep Singh, Senior Principal Research Analyst at Gartner, said organizations are shifting from model development to production-scale deployment, with fine-tuned and domain-specific models integrated into customer-facing and operational systems, requiring continuous, real-time execution rather than periodic training. The inference surge is not simply a function of more users querying more models.