Enterprises are expanding FinOps to manage SaaS and AI costs
Cloud spending has been a black hole for many enterprises, expensive, complex, and hard to control. But the shift is happening. FinOps, originally designed for cloud cost optimization, is now expanding into SaaS and AI. This means making spending predictable, scalable, and efficient.
SaaS has grown into a sprawling, decentralized ecosystem. Enterprises are juggling hundreds of subscriptions across departments, with many overlapping tools and wasted seats. AI is even more challenging, unlike SaaS, where costs are mostly predictable, AI workloads can spike unpredictably based on compute demand. More companies are realizing they need a structured approach to manage these expenses before they spiral out of control.
The numbers back this up. A FinOps Foundation survey of 800 companies found that nearly 66% are moving FinOps into SaaS cost management, and 63% are already applying it to AI, double the percentage from last year. This is an urgent, necessary response to the financial chaos of unchecked digital expansion.
AI cost optimization presents unique challenges
Managing AI costs is not the same as managing traditional cloud spend. It’s about reducing CPU and memory use. AI operates on a completely different scale, one driven by GPUs, training cycles, and real-time inference processing. Companies that don’t adjust their FinOps strategies for AI will be caught off guard by unexpected bills.
AI’s cost structure is complicated. GPUs are expensive, and AI models demand massive processing power. Training a model can take days or weeks, burning through compute resources at an unpredictable rate. Inference, the process of using a trained AI model to generate results, also racks up costs, especially when running at scale. And then there’s token-based pricing, where companies pay based on the volume of data processed by AI models. That means costs are no longer tied to infrastructure alone but to how often and how intensely AI is used.
AI costs from AWS, Google, and Microsoft are now part of regular cloud bills. But optimizing AI spending requires a different approach, one that goes beyond traditional cloud cost levers and digs into new areas like GPU utilization, workload efficiency, and fine-tuning model deployment strategies.
FinOps adoption is widespread among large enterprises
FinOps is no longer just for early adopters. It’s now standard practice among major enterprises. The biggest companies, the ones that have been managing cloud costs the longest, are doubling down on FinOps to bring financial discipline to their tech spending. This is becoming a core pillar of how enterprises operate.
The FinOps Foundation reports that 93 of these top corporations are actively engaged in FinOps programs. That’s a strong indicator of where the industry is heading. Companies that don’t adopt FinOps risk falling behind, in cost management and in financial predictability, efficiency, and operational control.
“The key takeaway? Companies that treat FinOps as an afterthought will struggle with digital expansion.”
Governance is emerging as a priority over cost-cutting
Early-stage FinOps was about one thing: cutting costs. That was step one. But enterprises are realizing that once you’ve squeezed out the obvious inefficiencies, the real value comes from governance, creating policies, automation, and long-term financial discipline.
Optimizations are quick fixes. Governance is what keeps an organization financially disciplined at scale. It’s the difference between reacting to cost overruns and preventing them in the first place. Governance means setting policies on cloud usage, automating spending controls, and making sure that cost efficiency is a core business function.
AI and multi-cloud investments complicate cost management
Enterprises are using a mix of SaaS, public cloud, private cloud, and on-prem data centers. This makes cost management far more complex. Different cloud providers have different billing structures, and private data centers require upfront investments with entirely different cost models. Managing this mix efficiently is one of the biggest challenges in tech finance today.
Multi-cloud strategies add another layer of complexity. Data movement between clouds can trigger egress fees, which are often overlooked but can be a major cost driver. Workloads split between public and private clouds require careful balancing to avoid redundancy and wasted capacity. And AI compounds the issue, its high compute demands make financial tracking across multiple environments even more difficult.
A FinOps Foundation survey found that 69% of enterprises are using SaaS for AI workloads, while 30% are investing in private cloud and data centers. The numbers show a clear trend: companies are moving beyond single-cloud deployments, but many are struggling to optimize costs across multiple platforms.
AI cost management means looking beyond traditional cloud billing models. It’s means understanding pricing models across different vendors and optimizing AI workloads in real time. Companies that master this will have a big advantage in controlling their AI spending.
Final thoughts
FinOps is changing fast. What started as a cloud cost optimization strategy is now becoming the foundation for managing SaaS and AI expenses. Companies that take FinOps seriously, especially in governance and AI cost control, will have a competitive advantage in managing their digital transformation.
The shift is clear. SaaS costs are being brought under control. AI cost management is becoming a major priority. Fortune 100 companies are making FinOps a standard practice. Governance is replacing short-term cost-cutting. And multi-cloud and AI investments are forcing enterprises to rethink how they approach cost management.
Enterprises that get ahead of these changes now will be in a far better position to scale efficiently, innovate without financial bottlenecks, and maintain a clear, controlled growth trajectory. Those that ignore these trends? They’ll be fighting a losing battle against unpredictable, runaway tech spending.
Key executive takeaways
- FinOps is expanding beyond cloud to AI and SaaS: Enterprises are adopting FinOps to control unpredictable AI costs and SaaS sprawl. Leaders should integrate FinOps into financial planning to prevent unchecked digital spending.
- AI cost management requires new strategies: Traditional cloud cost controls don’t work for AI, which relies on costly GPUs, token-based pricing, and resource-heavy training cycles. Executives must implement AI-specific cost tracking and workload optimization to avoid financial overruns.
- FinOps is becoming standard among large enterprises: With 93 of the Fortune 100 engaged in FinOps, it’s now a core business function, not a trend. Companies lagging in FinOps adoption risk inefficiencies and competitive disadvantages in managing digital expansion.
- Governance is overtaking cost-cutting as the priority: Cost optimizations offer diminishing returns, while long-term cost control depends on governance, automation, and policy enforcement. Leaders should shift focus from short-term savings to sustainable financial discipline.
- Multi-cloud and AI investments are driving complexity: Enterprises are deploying AI across SaaS, public cloud, and private infrastructure, making cost management harder. Decision-makers must adopt a unified FinOps approach across all environments to prevent inefficiencies and rising costs.
