AI is increasingly an inference business: organizations are shifting more spending and operational attention toward running trained models in products and workflows. Gartner forecasts that inference will exceed training in 2026 spending on AI-optimized infrastructure-as-a-service (IaaS). That is a meaningful change in emphasis—not evidence that training has ended or that inference happens only in the cloud.
What is AI inference?
Inference is the use of a trained model to produce an answer, prediction, or action for a request. When an assistant answers a question, a system classifies an image, or an agent uses a tool to complete a task, the model is performing inference. Training, by contrast, creates or updates the model’s parameters. Deployed products may run inference repeatedly, while training is a separate process that remains necessary to build and improve models.
Why are companies focusing on inference now?
As AI moves from model development into products and business workflows, the repeated cost and engineering work of serving requests become harder to ignore. Gartner forecasts that global spending on AI-optimized IaaS for inference will reach $23.3 billion in 2026, compared with $19 billion for training. It forecasts inference at 55% of AI-optimized IaaS spending in 2026 and 59% in 2027. These are Gartner forecasts published in August 2026, not audited year-end results, and they describe this specific IaaS segment—not all AI spending.
Gartner also forecasts total AI-optimized IaaS spending of $42.276 billion in 2026 and $66.143 billion in 2027, with 96.4% year-over-year growth in 2026. Those figures describe the same infrastructure segment, not the full value of the AI market.
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A separate estimate points in the same broad direction but measures something different. Deloitte’s 2026 outlook, published in November 2025, predicts that inference will account for roughly two-thirds of AI compute in 2026. Deloitte also expects data centers and enterprise systems to remain responsible for most computation, rather than AI shifting entirely to edge devices. This is a forecast of compute allocation, not Gartner’s IaaS spending share; the percentages should not be combined.
Why can inference still be expensive?
A lower price for each token does not guarantee a lower bill for a completed task. More capable applications can use longer prompts, more model calls, extra reasoning steps, retries, and tool use. A task may also be routed to a more capable model. The relevant business measure is often the total cost of a successful outcome—not the price of one token in isolation.
Gartner forecasts that inference costs per agentic workflow will grow more than fivefold through 2028. Gartner’s August 17, 2026 explanation is that increasingly capable applications use more complex workflows and tokens; it also says that routing a task to an agentic reasoning model costs providers at least five times as much as a basic chatbot interaction. This is a forecast about workflow/provider costs, not a universal end-user price comparison. Gartner analyst Will Sommer summarized the underlying tension: “Product leaders cannot rely on more efficient token economics to rationalize AI costs.”
Will inference replace AI training?
No. Training remains necessary to create and update models. The change is that, as more models are deployed, inference can become the larger recurring workload and a greater focus for investment and operations. Gartner’s forecast supports that conclusion for AI-optimized IaaS spending; it does not establish that training has ended, or that inference outweighs training under every definition of AI compute.
Where does inference run?
Inference can run in cloud data centers, on enterprise or on-premises systems, or on devices at the edge. These are not mutually exclusive destinations: an organization may place different workloads in different locations, or use a hybrid arrangement.
- Cloud: can provide infrastructure that scales across demand and locations, but brings network, service-dependency, and data-governance considerations.
- On-premises or enterprise systems: may suit workloads that need local control or proximity to enterprise data, while requiring the organization to manage infrastructure and capacity.
- Edge devices: can reduce network round trips and may keep some functions available when connectivity is lost. They are not a universal replacement for data centers, which Deloitte expects to remain central to AI computation in 2026.
Google Cloud reports that 90% of organizations in research it cites rank edge deployment as important for AI initiatives, and that 52% use a hybrid multicloud architecture. These are figures presented by a cloud vendor, not independently validated universal rates.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should organizations evaluate?
“Inference” alone does not identify the right chip, cloud, or deployment model. Compare options against the actual workload and its operating constraints:
- Latency and throughput: Interactive assistants and always-on agents may need quick responses or sustained request capacity. A system optimized for low latency is not automatically the least expensive for every workload.
- Total cost per successful task: Include model calls, tokens, reasoning steps, retries, tool use, and routing—not only a quoted token price.
- Location and resilience: Consider network dependence, locality, service availability, and what happens when connectivity or a provider is unavailable.
- Power, cooling, and facility capacity: High-performance systems require physical infrastructure as well as accelerators. Capacity constraints can shape where and how quickly a workload can grow.
- Governance and security: Systems that access data or take actions need appropriate permissions, auditability, and controls. Deployment location can also affect data-residency and sovereignty requirements.
- Matched hardware and software: Chips, memory, networking, software, and orchestration work together. A peak-performance number alone is not a reliable comparison unless it reflects a matched workload and a clearly described benchmark.
OpenAI’s Sarah Friar describes the company’s own infrastructure strategy this way: “Different workloads place different demands on the system. Frontier training, high-volume inference, and always-on agents have different requirements across chips, software, networks, power, and latency.” That is a vendor’s strategic framing, but it captures why a single accelerator or deployment choice is unlikely to suit every workload.
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