Worldwide AI-optimized infrastructure as a service (IaaS) spending is projected to grow 96% through 2026, reaching $42 billion, according to Gartner, Inc., a business and technology insights company.
“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 Mr. Hardeep Singh, Sr Principal Research Analyst at Gartner.
The market is forecast to sustain high growth and reach $66 billion in 2027 (see Table 1).
Table 1: Spending on Infrastructure as a Service, Worldwide, 2025-2027 (Millions of Dollars)
| Segment | 2025 Spending | 2025 Growth (%) | 2026 Spending | 2026 Growth (%) | 2027 Spending | 2027 Growth (%) |
| Total AI-optimized IaaS | 21,529 | 180.0 | 42,276 | 96.4 | 66,143 | 56.5 |
| Total IaaS | 222,170 | 25.3 | 287,347 | 29.3 | 359,899 | 25.2 |
Source: Gartner (August 2026)
Inference Workload Spending to Surpass Training Spending in 2026
The rise of agentic AI amplifies compute intensity through multistep, autonomous execution, making inference the dominant consumption model and positioning AI-optimized IaaS as a critical enabler of enterprise AI strategies.
“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.”
In 2026, global spending on inference ($23.3 billion) will surpass that of training ($19 billion). Fifty-five percent of AI-optimized 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.
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