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Gartner Forecasts Worldwide AI-Optimized IaaS Spending to Reach $42 Billion in 2026

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การ์ทเนอร์คาดมูลค่าใช้จ่ายบริการ AI-Optimized IaaS ทั่วโลกปี 2569 พุ่งแตะ 4.2 หมื่นล้านดอลลาร์ ขณะที่การใช้จ่ายฝั่ง Inference แซงหน้าการ Training โมเดล

Brickinfo News Agency – Worldwide AI-optimized infrastructure as a service (IaaS) spending is projected to increase by 96% through 2026 to reach $42 billion, driven by sustained demand for large language model (LLM) training and enterprise operationalization of AI across workflows, according to research firm Gartner, Inc.

In Thailand, organizational spending on AI-optimized IaaS is forecast to exceed THB 1.8 billion in 2026, marking a 207.3% surge from 2025. Local enterprise spending is expected to expand further to nearly THB 3.2 billion in 2027 as businesses integrate AI capabilities directly into applications and day-to-day operations. Globally, the overall AI-optimized IaaS market is projected to maintain strong momentum, rising by 56.5% in 2027 to reach $66 billion.

Table 1: Spending on Infrastructure as a Service, Worldwide, 2025-2027 (Millions of Dollars)

Segment2025 Spending2025 Growth (%)2026 Spending2026 Growth (%)2027 Spending2027 Growth (%)
Total AI-optimized IaaS  21,529  180.0  42,276  96.4  66,143  56.5
Total IaaS222,17025.3287,34729.3359,89925.2
Source: Gartner (August 2026)

A key structural shift in AI computing is the transition toward inference workloads surpassing training workloads in 2026. Spending on inference is estimated to reach $23.3 billion globally, exceeding the $19 billion allocated to model training. Inference workloads are projected to account for 55% of all AI-optimized IaaS spending in 2026 and rise to 59% in 2027, driven in part by the compute requirements of agentic AI execution.

Hardeep Singh, Senior Principal Research Analyst at Gartner, stated that organizations are shifting focus from model development to production-scale deployment. He noted that 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. This operational shift is altering cloud consumption patterns and establishing steady demand for AI-optimized infrastructure.

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