Cloud usage continues to soar, as do its associated costs — particularly, of late, those driven by AI. Gartner analysts predict worldwide end-user spending on public cloud services will swell to $723.4 billion in 2025, up from slightly below $600 billion in 2024. And 70% of executives surveyed in an IBM report cited generative AI as a critical driver of this increase.
At the identical time, China’s DeepSeek made waves when it claimed it took just two months and $6 million to coach its AI model. There’s some doubt whether those figures tell the entire story, but when Microsoft and Nvidia’s still-jolted share prices are any indication, the announcement woke the Western world as much as the necessity for cost-efficient AI systems.
To this point, firms have been in a position to treat mounting AI costs as R&D write-offs. But AI costs — especially those related to successful products and features — will eventually hit firms’ cost of products sold (COGS) and, consequently, their gross margins. AI innovations were at all times destined to face the cold scrutiny of business sense; DeepSeek’s bombshell announcement just shortened that timeline.
Identical to they do with the remainder of the general public cloud, firms might want to manage their AI costs, including each training and consumption costs. They’ll need to attach AI spending with business outcomes, optimize AI infrastructure costs, refine pricing and packaging strategies, and maximize the return on their AI investments.
How can they do it? With cloud unit economics (CUE).
What’s cloud unit economics (CUE)?
CUE comprises the measurement and maximization of cloud-driven profit. Its fundamental mechanism is connecting cloud cost data with customer demand and revenue data, revealing essentially the most and least profitable dimensions of a business and thus showing firms how and where to optimize. CUE applies across all sources of cloud spending, including AI costs.
The muse of CUE is — organizing cloud costs in accordance with who and/or what drives them. Common allocation dimensions include cost per customer, cost per engineering team, cost per product, cost per feature, and price per microservice. Firms using a contemporary cost management platform often allocate costs in a framework that mirrors their business structure (their engineering hierarchy, platform infrastructure, etc.).
Then, the guts of CUE is the , which compares cost data with demand data to indicate an organization their all-in cost to serve. For instance, a B2B marketing company might wish to calculate its “cost per 1,000 messages” sent via its platform. To do that, it could need to track its cloud costs and the variety of messages sent, feed that data right into a single system, and instruct that system to divide its cloud costs by its messages and graph the lead to a dashboard.
For the reason that company began with cost allocation, it could then view its cost per 1,000 messages by customer, product, feature, team, microservice, or whatever other view it deemed reflective of its business structure.
The outcomes:
- Flexible by which they’ll filter their unit cost metric, showing them which areas of their business are driving their cloud costs
- An illuminating that shows them how efficiently they’re meeting customer demand
- The power to make targeted efficiency improvements, like refactoring infrastructure, tweaking customer contracts, or refining pricing and packaging models
CUE within the AI age
Within the CUE model, AI costs are only yet one more source of cloud spending that could be incorporated right into a business’s allocation framework. The best way that AI firms disseminate cost data remains to be evolving, but in principle, cost management platforms treat AI costs in much the identical way as they treat AWS, Azure, GCP, and SaaS costs.
Modern cloud cost management platforms allocate AI costs and show their efficiency impact within the context of unit cost metrics.
Firms should allocate their AI costs in a handful of intuitive ways. One could be the aforementioned cost per team, an allocation dimension common to all sources of cloud spending, showing the prices that every engineering team is answerable for. This is especially useful because leaders know exactly who to notify and hold accountable when a specific team’s costs spike.
Firms may also need to know their — machine learning (ML) models versus foundation models versus third-party models like OpenAI. Or, they may calculate their cost per SDLC stage to know how an AI-powered feature’s costs change because it transitions from development to testing to staging and eventually to production. An organization could get much more granular and calculate its cost per AI development lifecycle stage, including data cleansing, storage, model creation, model training, and inference.
Zooming out from the weeds a bit: CUE means comparing organized cloud cost data with customer demand data after which determining where to optimize. AI costs are only yet one more source of cloud cost data that, with the best platform, fit seamlessly into an organization’s overall CUE strategy.
Avoiding the COGS tsunami
As of 2024, only 61% of firms had formalized cloud cost management systems in place (per a CloudZero survey). Unmanaged cloud costs soon develop into unmanageable: 31% of firms — much like the portion who don’t formally manage their costs — suffer major COGS hits, reporting that cloud costs eat 11% or more of their revenue. Unmanaged AI costs will only exacerbate this trend.
Today’s most forward-thinking organizations treat cloud costs like all other major expenditure, calculating its ROI, breaking that ROI down by their most important business dimensions, and empowering the relevant team members with the info needed to optimize that ROI. Next-generation cloud cost management platforms offer a comprehensive CUE workflow, helping firms avoid the COGS tsunami and bolster long-term viability.
