Global spending on AI-optimized Infrastructure as a Service (IaaS) is projected to reach $42.276 billion by 2026, a 96.4% increase from the previous year, according to the latest forecasts from Gartner. This figure reflects the rapid growth in computational capacity needed by companies to train models, but also points to an industry shift that could be even more significant: inference will soon outpace AI training in revenue.
The essentials of AI cloud infrastructure in 20 seconds
- Gartner forecasts around $42.276 billion in AI-optimized IaaS spending for 2026.
- The market will grow by 96.4% this year and another 56.5% in 2027.
- Inference will reach $23.3 billion compared to $19 billion allocated for training.
- Enterprise deployment and AI agents are driving ongoing demand for computational capacity.
The difference compared to the entire cloud market is significant. Gartner estimates that total global cloud infrastructure spending will rise from $222.17 billion in 2025 to $287.35 billion in 2026, a 29.3% growth. Infrastructure specifically designed for advanced AI workloads will grow at more than triple that pace.
Forecasts also suggest this growth will extend beyond 2026. Gartner predicts that the AI-optimized IaaS market will reach $66.14 billion in 2027, with a yearly increase of 56.5%. These are projections, not actual spending yet, but they illustrate how AI is fundamentally changing infrastructure needs for companies and cloud providers alike.
From training large models to executing millions of queries
Much of recent AI development focused on training. Creating large language models (LLMs) requires enormous computing power, specialized accelerators, and storage and networking systems capable of feeding data into these processors.
This demand remains. Gartner estimates that in 2026, approximately $19 billion will be spent on cloud infrastructure for AI training.
However, the next phase is gaining importance: deploying trained models.
Every time a user queries an AI assistant, a company processes documents through a model, a system generates code, or an agent performs a task, inference occurs. Unlike training processes, which can be concentrated during specific periods, production applications require constant available capacity.
For the first time in these forecasts, inference spending will surpass training. Gartner estimates that inference-related expenditure will reach $23.3 billion in 2026, roughly 55% of total AI-optimized IaaS spending.
By 2027, inference’s share will increase to 59%.
| Market | 2025 | 2026 | Growth 2026 | 2027 | Growth 2027 |
|---|---|---|---|---|---|
| AI-optimized IaaS | $21.53B | $42.28B | 96.4% | $66.14B | 56.5% |
| Total IaaS | $222.17B | $287.35B | 29.3% | $359.90B | 25.2% |
Gartner forecasts published in August 2026.
This evolution signifies a major shift for infrastructure providers. Training a model requires resources concentrated during specific periods, whereas serving models in production demands constant availability, low latency, capacity to handle demand spikes, and cost management to execute large volumes of requests.
AI agents further increase compute demand
The expansion of AI agents could accelerate this trend. An ordinary chatbot may receive one query and generate a response. An agent-based system may need to break down requests into multiple tasks, consult various data sources, utilize external tools, repeatedly call models, and validate results before completing the work.
This means a single user request can trigger multiple inference operations.
Gartner considers that this autonomous, multi-step execution boosts application compute intensity and helps position inference as the leading model for AI infrastructure consumption.
It is also transforming how companies utilize models. Besides large, general-purpose models, tailored versions are emerging for specific organizations, industries, or datasets. When integrated into customer service, internal processes, software development, or business operational systems, these tools require ongoing inference execution.
Senior Gartner analyst Hardeep Singh attributes the forecasted growth to both increased infrastructure demand for training large models and the rapid integration of AI into enterprise applications and workflows.
According to Singh, moving from experimental development to large-scale production deployments is creating more continuous cloud consumption patterns. Fine-tuned, domain-specific models are shifting from testing phases to being embedded in permanently operational systems.
Infrastructure becomes a core element of AI costs
Numbers help explain why major technology providers are investing heavily in expanding their AI platforms. While the focus often centers on models, the infrastructure chain behind each service includes accelerators, servers, memory, storage, high-speed networks, data centers, electrical systems, and cooling.
Inference introduces a different economic consideration. It’s not enough for a company simply to have a capable model; it must be able to run it repeatedly with acceptable performance, availability, and cost levels.
This can influence choices such as model size, accelerator type, infrastructure location, cloud provider, or whether to combine cloud services with dedicated or private infrastructure.
Gartner’s forecasts also show that AI-focused IaaS remains a small segment of the overall cloud infrastructure market. The projected $42.276 billion in 2026 accounts for approximately 14.7% of the estimated $287.35 billion total IaaS market, according to Gartner.
By 2027, this percentage should rise to nearly 18.4%, with $66.14 billion out of the estimated $359.90 billion total market.
While the percentage growth slows compared to 2026, it will still significantly outpace the overall IaaS market growth, which Gartner estimates at 25.2% in 2027 versus 56.5% for AI-optimized infrastructure.
This shift reflects a transition beyond just building larger models. The challenge now lies in how to run AI continuously and at scale, especially as assistants, specialized models, and agents move from demonstrations to daily-used applications.
Frequently Asked Questions
How much will be spent on AI cloud infrastructure in 2026?
Gartner forecasts that worldwide spending on AI-optimized IaaS will reach $42.276 billion in 2026, a 96.4% increase from 2025.
Will more be spent on training models or running AI?
Inference is expected to surpass training according to Gartner’s forecasts. In 2026, about $23.3 billion will be spent on inference, compared to $19 billion on training.
Why do AI agents require more infrastructure?
Because a single request can generate multiple operations: model queries, tool calls, searches, and additional inferences before reaching the final result. This can increase the computational load for each task.
Will infrastructure growth for AI continue in 2027?
Gartner estimates it will. Spending on AI-optimized IaaS could reach $66.14 billion in 2027, with a year-over-year growth of 56.5%.
Sources:
- Gartner, Gartner Forecasts Worldwide AI-Optimized IaaS Spending to Grow 96% Through 2026, 08/10/2026.
