Gartner Says AI-Optimized IaaS Is Poised to Become the Next Growth Engine for AI Infrastructure; Inferencing Workloads Will Become a Dominant Force for AI-optimized IaaS Demand
FinTech BizNews Service
Mumbai, October 14, 2025: AI-optimized infrastructure as a service (IaaS) is emerging as the next disruptive growth engine for AI infrastructure. As a result, end-user spending is projected to grow 146% by the end of 2025, according to Gartner, Inc. a business and technology insights company.
The AI-optimized IaaS market includes spending on high-performance computing (HPC) resources—such as graphics processing units (GPUs), application-specific integrated circuits (ASICs), and other AI accelerators—designed for large-scale AI processing.
“Traditional IaaS is maturing, however, AI-optimized IaaS spending growth projections are higher than that of traditional IaaS over the next five years,” said Hardeep Singh, Principal Analyst at Gartner. “As organizations expand their use of AI and GenAI, they will need specialized infrastructure such as GPUs, tensor processing units (TPUs) or other AI ASICs, high-speed networking and optimized storage for fast parallel processing and data movement. As such, traditional central processing unit (CPU)-based IaaS will face significant challenges in meeting these demands.”
Gartner estimates worldwide end-user spending on AI-optimized IaaS will total $18.3 billion by the end of 2025 and $37.5 billion in 2026 (see Figure 1).
Worldwide Spending and Annual Growth Rate of AI-Optimized IaaS, 2024-2029
Source: Gartner (October 2025)
As AI adoption scales across industries, inferencing workloads will become a dominant force driving demand for AI-optimized IaaS. Gartner projects end-user spending on inferencing to take over that of training-intensive workloads in 2026. Spending on inference-focused applications is expected to reach $20.6 billion, up from $9.2 billion in 2025. In 2026, 55% of AI-optimized IaaS spending will support inference workloads and it is projected to reach more than 65% in 2029.
“Unlike training which involves intensive, large-scale compute cycles that occur during model development and ongoing updates, inference happens continuously — powering real-time applications such as chatbots, recommendation engines, fraud detection systems and industry-specific applications,” said Singh.
