By Scott Sellers, co-founder and CEO, Azul
CFOs estimate that 10% to 30% of enterprise cloud spend is wasted. Most technology and finance leaders already know where that waste sits: they see the idle workloads, overprovisioned environments, and underused services. Nevertheless, the cost continues to rise which has only been compounded as companies scramble to adopt AI.
Azul’s new CFO Cloud Cost Optimization Report puts numbers to what I hear in nearly every conversation with my fellow tech executives. Almost 9 in 10 CFOs say cloud spending is growing, and a third call the increase significant. Concern is widespread, too: nearly three-quarters worry about current spend levels, and two-thirds say cloud costs have become a board-level issue. In short, awareness is universal, but the problem still isn’t solved.
Visibility Tools Are Working. That’s Not the Lever.
When cloud costs rise, the default response is to demand more visibility. Over the past few years, organizations have invested heavily in tools designed to surface waste and optimize spend. The good news is that there are plenty of great tools out there now that provide accurate, highly granular visibility.
Now the board can see the waste, which is great for identifying areas to go after but it’s especially challenging as it continues to grow. Nearly half of the CFOs in our survey are already using AI-powered cloud spend analysis or native tools like AWS Cost Explorer, and a third now track cloud waste as a primary metric. Finance teams have a clearer cost picture of their cloud environments than ever before, and it is not pretty.
Growth Always Wins
Growth is still the metric that dominates decision-making and shapes trade-offs at every level. Efficiency may be tracked, but it rarely drives decisions when growth is on the table.
This is why visibility alone doesn’t change outcomes. When teams can see waste, they lack the incentive, not the information, to act. Cloud cost discipline becomes a last resort, addressed seriously only when there’s no other choice.
The pattern extends all the way to the board. When a business is growing or under pressure to grow, the instinct is to provision first and optimize later. Holding back feels like falling behind, and the fear of losing ground to competitors consistently outweighs efficiency arguments.
Our survey data clearly reflects this reality. CFOs rank AI and automation as their top financial priority for the next 12 months (56%), ahead of reducing cloud costs (40%), even as 43% say AI adoption is already adding complexity that makes cloud control harder. The same initiatives driving growth are also driving consumption higher.
That’s not a contradiction. It is how priorities behave under competitive pressure. AI investment is driven by the board and the market. Cost discipline is owned by finance. Growth wins that tradeoff almost every time.
Start Where Consumption is Created
If growth keeps being prioritized, the only meaningful way to control cloud spend is to reduce the cost of growth itself. Most organizations still start at the visibility layer. That’s necessary, but it’s not enough. Knowing where waste lives does not eliminate it.
One real leverage point sits at the application layer, where compute consumption is created. This is where the number of servers required, how much memory each one needs, and how hard each server has to work are effectively determined. Inefficiency at this layer doesn’t just inflate one line item — it multiplies across every instance running in production, compounding cost at scale. Reducing that inefficiency means fewer resources consumed per transaction, which translates directly into fewer servers, smaller infrastructure footprints, and lower bills — structurally, not temporarily. Yet only 16% of organizations in our survey are using Java runtime optimization or JVM tuning, techniques that directly reduce compute usage rather than simply reporting on what’s been spent. For companies running Java-based workloads, which represent the majority of production applications in large enterprises, this is where structural efficiency is available and is an essential technique to preserve budget for future AI investments.
Optimization is How You Fund AI for The Future
For CFOs navigating this tension, the most important reframe is that cloud cost optimization and AI investment shouldn’t be competing priorities. In the right sequence, the first funds the second. To make that shift real, organizations should focus on three changes:
- Treat efficiency as an innovation enabler, not a constraint: The organizations getting this right are not choosing between cost discipline and AI ambition. They use cloud efficiency to create budget headroom that enables sustained AI investment.
- Move FinOps from reactive to integrated: For many teams, FinOps still begins after the bill arrives. Instead, cost awareness needs to be embedded in architecture decisions, development workflows, and infrastructure choices from the start.
- Make cost an engineering KPI: Cost can’t remain a finance-only metric reviewed at the end of the quarter. It needs to become an informed, shared KPI that engineering and finance teams optimize together as part of everyday decision-making.
Cloud costs will inevitably keep rising – businesses are moving too fast and pushing for growth to avoid it. AI adoption will further accelerate that. The CFOs who stay ahead of it won’t be the ones with only the best dashboards — they’ll be the ones who moved efficiency upstream, made it an engineering KPI, and turned cloud cost discipline into a competitive advantage. Visibility tells you what’s been spent. Application-layer optimization can fundamentally change what gets spent. That’s the shift most enterprises haven’t made yet. The ones who do will have something their competitors don’t: a self-funding model for AI innovation that doesn’t require limiting growth only because of cost control.
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ABOUT THE AUTHOR
Scott Sellers, President, CEO & Co-Founder
With more than 30 years of successful leadership in building high technology companies and delivering advanced products to market, Scott provides the overall strategic leadership and visionary direction for Azul Systems. Scott has a proven track record of vision, leadership, and success across enterprise, consumer, and scientific markets. Prior to co-founding Azul Systems, Scott founded 3dfx Interactive, a graphics processor company that pioneered the 3D graphics market for personal computers and game consoles. Scott served at 3dfx as Vice President of Engineering, CTO, and a member of the board of directors, and delivered 7 award-winning products and developed 14 graphics processors. After a successful initial public offering, 3dfx was later acquired by NVIDIA Corporation.
