In 2025, global spending on IaaS and PaaS reached $90.9 billion, marking a 21% increase from 2024. This growth is largely attributed to the migration of workloads to the cloud and the rising adoption of AI technologies. However, companies face challenges in the cost management of AI, particularly in the inference phase. While training costs are typically one-time expenditures, inference costs can escalate unpredictably based on usage, leading businesses to reconsider their AI strategies, potentially delaying the deployment of sophisticated AI models.
In 2025, the worldwide expenditure on IaaS and PaaS reached $90.9 billion, a 21% rise from the previous year driven by cloud migration and AI.
Enterprises are increasingly concerned about the cost-effectiveness of inference services, as costs can quickly accumulate, creating pressure on AI project viability.
Today's pricing for inferencing services, based on usage metrics, makes cost prediction challenging, which can lead to scaled-back AI model sophistication.
The unpredictability of inference costs may deter businesses from fully deploying AI, restricting them to less cutting-edge approaches and hindering advancement.
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