Gartner Predicts AI Inference Spending Will Surpass Training for First Time in 2026
Gartner predicts that global spending on AI-optimized Infrastructure as a Service (IaaS) will reach $42 billion in 2026, representing a 96% year-over-year growth. Driven by the rise of agentic AI, spending on AI inference is projected to reach $23.3 billion, surpassing the $19 billion allocated for model training for the first time. This shift marks a critical transition in the AI industry from experimental development and model training to real-world production deployment. As businesses integrate AI into daily operations, cloud infrastructure investments will pivot to support continuous, real-time query processing rather than one-off training runs. Inference tasks are expected to account for 55% of AI-optimized IaaS spending in 2026, rising to 59% in 2027. Meanwhile, overall global IaaS spending is forecasted to grow from $222.17 billion in 2025 to $359.90 billion by 2027.
## BACKGROUND
Model training is the initial phase where an AI model learns patterns from massive datasets, requiring immense computational power. Inference is the subsequent phase where the trained model is deployed to process new, real-world inputs and generate outputs, such as answering user queries or recommending content. Agentic AI refers to autonomous AI systems capable of pursuing goals and taking actions with minimal human intervention, which heavily rely on continuous inference.