Cloud computing expenses are increasingly becoming a major financial concern for midsize IT companies, driven significantly by the adoption of artificial intelligence (AI) workloads. A recent survey by Cloud Capital reveals that for some firms, cloud services now represent the second-highest operational cost, with spending exhibiting substantial month-to-month volatility. This trend underscores a growing challenge for financial and IT leaders as AI integration scales.
The Cloud Capital survey, which gathered insights from 100 CFOs at SaaS and other IT companies with up to 1,000 employees, found that a third of these firms allocate between 5% and 8% of their annual revenues to cloud services. Another 29% report spending over 13% of their revenues on cloud, with the average spend across all surveyed companies being 10% of revenues. Cloud costs now rank as the second-largest expense for many IT and SaaS companies, trailing only payroll. Spencer Pingry, cofounder and CTO at Cloud Capital, stated, With the AI workloads in particular, as we see the cloud spend go up, a lot of people are dealing with the increase for now, because they think there’s a certain set of payoffs that will be realized. But once we realize those payoffs, I don’t think cloud spending is going to cut back; it’s going to increase instead.
The survey highlighted significant cost volatility, with three-quarters of IT organization CFOs reporting cloud spending forecasts varying between 5% and 10% of company revenues each month. This unpredictability is largely attributed to AI workloads, which Pingry noted are harder to predict than traditional SaaS infrastructure
. Organizations with major AI workloads are also more likely to experience margin declines linked to cloud spending compared to those with moderate AI exposure. A Cloud Capital report further explained, Training spikes, usage-driven inference, and experimentation noise introduce non-linear patterns that break the forecasting assumptions finance relies on. The challenge will intensify as AI’s share of cloud spend continues scaling.
The surge in cloud costs is directly linked to the resource-intensive nature of AI workloads. Ed Frederici, global head of cloud advisory at digital transformation provider UST, commented on the broader issue, stating, Cloud spend climbs toward 10% of revenue when consumption is disconnected from business value and when we confuse developer flexibility with productivity.
He drew a comparison to past practices, noting, In the data center era, no one would have allowed developers to rack and install hardware, yet in the cloud era, we gave them near-infinite infrastructure choices with no economic guidance.
Additionally, Frederici observed that costs escalate when systems are built without a clear understanding of the value they are meant to deliver. According to Gartner‘s forecast in , worldwide public cloud end-user spending was projected to grow 20.4% to total $675.4 billion, primarily driven by generative AI and application modernization.
Cloud Capital currently lacks historical data on cloud spending as a percentage of revenue but plans to track it going forward. While the potential for IT and SaaS vendors to pass on increased cloud costs to their customers is acknowledged, the specific timeline or extent of such price increases remains unspecified.
As AI’s presence in cloud infrastructure continues to grow, the complexities of managing these costs are expected to intensify. Pingry suggests that while companies are currently absorbing these costs in anticipation of AI-driven payoffs, spending is likely to further increase rather than decrease. The growing challenge highlights the importance of financial operations (FinOps) practices to align cloud spending with business value. FinOps principles emphasize collaboration, visibility, and optimization to improve resource efficiency and manage cloud investments effectively. Industry experts like Chris Ortbals, chief product officer at Tangoe, have described unmanaged generative AI cloud costs as potentially lethal
to innovation, with a report indicating a 30% rise in cloud spending due to AI adoption, and 72% of FinOps practitioners finding GenAI cloud spending unmanageable
.
- Assess Current Cloud Spend: Regularly audit cloud expenditures to identify where AI workloads are impacting budgets most significantly.
- Implement FinOps Practices: Foster collaboration between finance, engineering, and business teams to gain real-time visibility and control over cloud costs. The FinOps Foundation outlines core principles for effective cloud financial management.
- Align Consumption with Value: Ensure that cloud resource consumption, especially for AI, is directly tied to measurable business value and outcomes.
- Monitor AI Workload Patterns: Recognize that AI workloads introduce non-linear and less predictable cost patterns, requiring specialized forecasting and management strategies.
- Explore Optimization Tools: Investigate cloud cost management tools that can help track, analyze, and optimize AI-driven cloud spending.
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