With bursty workloads and constantly fluctuating streams of user requests, cloud services have given many a chief financial officer a headache at the end of the month. A monthly charge of $25,000 could suddenly jump to $200,000 the next month. CFOs, as we know, don’t like such volatility.
Most companies, 69%, spend more than $1 million a year on cloud computing, a survey out of Flexera confirms. Nearly a quarter of respondents (24%) are currently spending more than $12 million per year on public cloud. Even among small to medium businesses in the sample, 26% spend more than $1 million annually.
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These costs would be less of a headache to CFOs if they were more predictable. They can be, but there is another wrench being thrown into the mix: Artificial intelligence, which relies heavily on cloud services for processing power and storage capacity. Few organizations are tracking AI costs at this time, new research on 1,245 cloud-consuming companies by the FinOps Foundation, a group affiliated with the Linux Foundation, finds. At the same time, 31% of survey respondents said that the costs of AI/ML are impacting their cloud cost measurement and mitigation efforts.
Enter FinOps — which encourages aligning cloud spending to business goals, based on data-driven decision-making — seen as an approach to the madness. However, there is a conundrum that still needs to be figured out. “Will AI/ML become one of the biggest costs to manage across all levels of cloud spend?” The survey’s author, Mike Fuller, CTO of the FinOps Foundation, asks. “Or will the technology be leveraged to make things easier for practitioners, unlocking more intelligent optimization and automation? The former is already happening for many, while the latter is still a hope.”
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The story is unfolding in two directions:
- AI for FinOps: Using AI/ML for the practice of FinOps itself
- FinOps for AI: Managing AI/ML service costs in your FinOps practice
Managing AI costs is a rising concern for those spending $100 million or more annually on cloud services at this time, Fuller observes. “Organizations with a higher overall cloud spend tend to see AI/ML as a rapidly increasing source of variable costs that need to be managed.” AI costs are not yet a concern for smaller organizations at this time.
“AI, rather than initially helping, is actually starting to negatively impact cloud bills for large spenders and is directly impacting margins due to increased spending in the cloud,” adds J.R. Storment, executive director of the FinOps Foundation.
AI delivers greater and more intelligent automation, which is key to automating the tracking and management of cloud spend. Tellingly, however, “there is a lack of trust in full automation, where action is taken without any human approval,” the report states. “We heard that large spenders, especially those in regulated industries, are more cautious about automation. We also hear that integrating automation into existing systems and workflows is challenging, especially in environments where DevOps teams are distributed and use a mix of tools in their cloud deployments.”
With such a lack of trust, “full automation could take years to build,” they add.
