Worldwide spending on AI-optimized infrastructure as a service (IaaS) is forecast to reach $42.3 billion in 2026, an increase of 96.4% year over year, according to Gartner. The market is expected to grow further to $66.1 billion in 2027 as businesses move AI projects from development and experimentation into production.
AI infrastructure is growing faster than the overall cloud market
Gartner forecasts AI-optimized IaaS spending to rise from $21.5 billion in 2025 to $42.3 billion in 2026. By comparison, the overall IaaS market is expected to grow by 29.3% this year, reaching $287.3 billion.
The difference highlights the growing importance of infrastructure designed for AI workloads. Organizations need computing resources capable of supporting large language models (LLMs), generative AI applications and increasingly complex automated workflows.
This demand extends beyond GPUs. AI infrastructure also relies on high-performance networking, memory, storage and the cloud resources required to deploy and operate models reliably at scale.
AI inference spending is set to overtake training
One of the most significant changes in Gartner's forecast is the shift in spending from training AI models to running them.
In 2026, global spending on AI inference within AI-optimized IaaS is forecast to reach $23.3 billion, compared with $19 billion for training. Inference is expected to account for 55% of AI-optimized IaaS spending this year, rising to 59% in 2027.
The shift reflects a move toward production use. Training develops a model, while inference runs it to generate responses, make predictions or perform tasks. As businesses integrate AI into customer-facing applications and internal workflows, models increasingly need to process requests continuously rather than only during periodic training runs.
The growth of agentic AI adds to this demand. AI agents can execute multistep tasks and make repeated model calls, increasing the computing resources required to complete a workflow.
What the shift means for cloud infrastructure
As AI applications move into production, infrastructure decisions increasingly depend on workload requirements, utilization and operating costs—not just peak computing performance.
Training workloads may require substantial GPU capacity for defined periods. Inference workloads, by contrast, can generate ongoing demand that fluctuates with application usage. Providers and businesses therefore need to consider how resources can scale with demand while maintaining predictable performance and costs.
Several infrastructure capabilities become particularly important:
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GPU and compute capacity: Selecting resources suited to model size, performance requirements and expected request volumes.
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Scalability: Adjusting capacity as workloads grow or demand fluctuates.
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Networking and storage: Supporting data-intensive applications and efficient communication between computing resources.
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Resource utilization and cost control: Monitoring consumption and avoiding unnecessary capacity when workloads are variable.
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Reliability and security: Keeping production AI applications available while protecting the data they process.
The right architecture depends on the application. Some workloads may benefit from dedicated GPU infrastructure, while others can run on shared cloud resources or a combination of cloud and on-premises systems.
The outlook for AI-optimized IaaS
Gartner forecasts worldwide spending on AI-optimized IaaS to reach $66.1 billion in 2027, representing a further 56.5% increase. The broader IaaS market is also expected to expand, reaching $359.9 billion in the same year.
These projections point to a cloud market in which AI-specific infrastructure is becoming an increasingly important segment. As more organizations deploy models in production, the ability to provision suitable computing resources, scale workloads efficiently and manage ongoing infrastructure costs will be central to successful AI operations.
For businesses planning their AI infrastructure, the challenge is no longer limited to training a model. It is also about choosing the right environment to run it reliably, securely and cost-effectively as usage grows.
Source: Gartner, “Forecasts Worldwide AI-Optimized IaaS Spending to Grow 96% Through 2026,” August 10, 2026.
