Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Rising cloud spend, AI complexity, and board scrutiny push CFOs to rethink how cloud investments are governed and optimized

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul.

Cloud Costs Are Rising, and CFOs Are Taking Notice

Nearly nine in ten respondents (88%) report that their cloud spending is increasing, with one-third describing the rise as "significant," underscoring growing pressure to improve financial discipline as cloud usage scales. Only 9% say their cloud spend is staying flat, reinforcing the urgency to address waste as usage continues to grow.

This concern is now reaching the highest levels of governance. Two-thirds (66%) of CFOs say cloud spend has become a board-level issue, signaling a decisive shift in accountability from IT alone to the executive suite and boardroom.

AI Investment Creates a New Financial Tension

While cloud costs continue to climb, CFOs are under growing pressure to invest in innovation. More than half (56%) of CFOs cite AI and automation as their top financial priority, followed by improving cash flow and working capital efficiency (52%), and reducing overall cloud costs (40%). Yet AI adoption is also making cloud cost management more difficult. Over forty percent (43%) of CFOs say AI is adding new layers of workload complexity, complicating forecasting and cost control efforts at a time when predictability is increasingly critical.

This dynamic has created a clear mandate for finance leaders: fund AI initiatives by reducing costs in other areas. With cloud spend accounting for a significant portion of overall IT budgets, organizations are forced to rethink how efficiently their applications consume cloud resources.

CFOs Acknowledge Structural Cloud Waste

As scrutiny increases, finance leaders are also recognizing the scale of inefficiency embedded in many cloud environments. More than two-thirds of CFOs believe that up to 30% of their cloud spend is wasted, representing a significant drag on profitability and financial predictability. Rather than viewing cloud waste as an isolated issue, the report shows CFOs increasingly see it as a structural efficiency problem — one that requires better governance, deeper visibility and more effective optimization across infrastructure and applications.

CFOs Turn to New Optimization Levers

To regain control, organizations are deploying a mix of cloud cost management approaches. The most widely used tools focus on visibility and forecasting, including AI-powered cloud spend analytics (45%) and native cloud provider tools (44%).

Notably, CFOs are beginning to embrace deeper technical levers that directly influence cost efficiency. Sixteen percent of organizations already use Java runtime optimization or JVM tuning. In addition, re-platforming or application modernization initiatives (24%) and workload or infrastructure optimization vendors (29%) are cited as strategies finance leaders are using to manage cloud costs. These modernization efforts help organizations improve performance efficiency, reduce resource consumption, and modernize legacy systems that drive overspending.

Cloud Optimization as an Enabler of Innovation

CFOs do not view cloud optimization purely as a cost-cutting exercise. At the top of the list of main financial benefits they would prioritize, 45% of CFOs say the primary financial benefit of cloud cost optimization is increased budget flexibility to fund innovation, including AI and digital initiatives. Other top benefits include improved margins and profitability (42%), better forecasting and budgeting (39%), and stronger alignment between IT spend and business outcomes (39%). Another 37% cite "higher utilization of existing infrastructure," signaling a desire to extract more value from current systems.

Rather than a tactical cost exercise, cloud optimization is emerging as a strategic financial lever for CFOs to fund innovation, protect margins and ensure cloud investments deliver measurable business returns.

CFO Top Priorities for the Next 12 Months

CFOs and finance leaders shared their top cloud-related priorities for the next year, balancing innovation goals with heightened financial discipline as cloud spending continues to rise. Improving performance and uptime (43%) topped the list, followed by reducing overall cloud costs (39%) and maximizing profitable growth from cloud investments (38%). In addition, gaining visibility into current spending (35%), compliance and governance (34%) and supporting AI/ML initiatives also made the list. Together, these priorities highlight a clear mandate for finance leaders to ensure cloud investments support AI-driven innovation while delivering predictable, measurable returns and protecting margins.

"With nearly nine in ten CFOs seeing cloud costs rise and AI now a top investment priority, finance leaders are being forced to rethink how efficiently their applications consume cloud resources," said Scott Sellers, co-founder and CEO of Azul. "Cloud optimization has become a strategic lever — one that allows organizations to fund AI innovation, protect margins and bring greater predictability and accountability to cloud investments. Organizations that optimize at the infrastructure level, starting with how their software consumes compute resources, gain a meaningful advantage in funding the innovations that drive growth."

Methodology: The report is based on a Censuswide survey of 300 US CFOs and senior finance leaders.

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Cloud Spend Is Rising and Cloud Optimization Is Key to Funding AI and Protecting Margins

Rising cloud spend, AI complexity, and board scrutiny push CFOs to rethink how cloud investments are governed and optimized

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul.

Cloud Costs Are Rising, and CFOs Are Taking Notice

Nearly nine in ten respondents (88%) report that their cloud spending is increasing, with one-third describing the rise as "significant," underscoring growing pressure to improve financial discipline as cloud usage scales. Only 9% say their cloud spend is staying flat, reinforcing the urgency to address waste as usage continues to grow.

This concern is now reaching the highest levels of governance. Two-thirds (66%) of CFOs say cloud spend has become a board-level issue, signaling a decisive shift in accountability from IT alone to the executive suite and boardroom.

AI Investment Creates a New Financial Tension

While cloud costs continue to climb, CFOs are under growing pressure to invest in innovation. More than half (56%) of CFOs cite AI and automation as their top financial priority, followed by improving cash flow and working capital efficiency (52%), and reducing overall cloud costs (40%). Yet AI adoption is also making cloud cost management more difficult. Over forty percent (43%) of CFOs say AI is adding new layers of workload complexity, complicating forecasting and cost control efforts at a time when predictability is increasingly critical.

This dynamic has created a clear mandate for finance leaders: fund AI initiatives by reducing costs in other areas. With cloud spend accounting for a significant portion of overall IT budgets, organizations are forced to rethink how efficiently their applications consume cloud resources.

CFOs Acknowledge Structural Cloud Waste

As scrutiny increases, finance leaders are also recognizing the scale of inefficiency embedded in many cloud environments. More than two-thirds of CFOs believe that up to 30% of their cloud spend is wasted, representing a significant drag on profitability and financial predictability. Rather than viewing cloud waste as an isolated issue, the report shows CFOs increasingly see it as a structural efficiency problem — one that requires better governance, deeper visibility and more effective optimization across infrastructure and applications.

CFOs Turn to New Optimization Levers

To regain control, organizations are deploying a mix of cloud cost management approaches. The most widely used tools focus on visibility and forecasting, including AI-powered cloud spend analytics (45%) and native cloud provider tools (44%).

Notably, CFOs are beginning to embrace deeper technical levers that directly influence cost efficiency. Sixteen percent of organizations already use Java runtime optimization or JVM tuning. In addition, re-platforming or application modernization initiatives (24%) and workload or infrastructure optimization vendors (29%) are cited as strategies finance leaders are using to manage cloud costs. These modernization efforts help organizations improve performance efficiency, reduce resource consumption, and modernize legacy systems that drive overspending.

Cloud Optimization as an Enabler of Innovation

CFOs do not view cloud optimization purely as a cost-cutting exercise. At the top of the list of main financial benefits they would prioritize, 45% of CFOs say the primary financial benefit of cloud cost optimization is increased budget flexibility to fund innovation, including AI and digital initiatives. Other top benefits include improved margins and profitability (42%), better forecasting and budgeting (39%), and stronger alignment between IT spend and business outcomes (39%). Another 37% cite "higher utilization of existing infrastructure," signaling a desire to extract more value from current systems.

Rather than a tactical cost exercise, cloud optimization is emerging as a strategic financial lever for CFOs to fund innovation, protect margins and ensure cloud investments deliver measurable business returns.

CFO Top Priorities for the Next 12 Months

CFOs and finance leaders shared their top cloud-related priorities for the next year, balancing innovation goals with heightened financial discipline as cloud spending continues to rise. Improving performance and uptime (43%) topped the list, followed by reducing overall cloud costs (39%) and maximizing profitable growth from cloud investments (38%). In addition, gaining visibility into current spending (35%), compliance and governance (34%) and supporting AI/ML initiatives also made the list. Together, these priorities highlight a clear mandate for finance leaders to ensure cloud investments support AI-driven innovation while delivering predictable, measurable returns and protecting margins.

"With nearly nine in ten CFOs seeing cloud costs rise and AI now a top investment priority, finance leaders are being forced to rethink how efficiently their applications consume cloud resources," said Scott Sellers, co-founder and CEO of Azul. "Cloud optimization has become a strategic lever — one that allows organizations to fund AI innovation, protect margins and bring greater predictability and accountability to cloud investments. Organizations that optimize at the infrastructure level, starting with how their software consumes compute resources, gain a meaningful advantage in funding the innovations that drive growth."

Methodology: The report is based on a Censuswide survey of 300 US CFOs and senior finance leaders.

Hot Topics

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When the Database Is the Problem, Is APM Enough?

Production incidents rarely announce themselves as database problems. They appear as slow transactions, timeouts, rising response times, or an application struggling under a workload it previously handled. APM provides an essential starting point. It can identify a slow transaction path, highlight an affected service, and show that a database dependency is consuming more time than expected. But identifying the database as part of the problem is not the same as explaining what is happening inside it ...

From Cleanup to Prevention: The Future of Cloud-Native Cost Optimization

Cloud teams are under constant pressure to reduce spend without slowing development or increasing operational risk. They are deploying autoscalers, rightsizing workloads, enforcing resource requests, reviewing utilization dashboards, and building FinOps processes around cloud-native environments. Yet the results often disappoint ...

The Real Enterprise AI Risk Isn't Hallucinations

Ask most IT leaders about their biggest concern with AI and you'll hear the same answer: hallucinations ... Today, however, the conversation has shifted ... As organizations move beyond chatbots and experiments, they are increasingly deploying AI agents that perform multi-step tasks. These systems retrieve documents, query databases, call APIs, generate reports, write code, and make recommendations. The issue is not whether the model can reason. The issue is whether the organization can see, verify, and govern the decisions being made along the way ...

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While organizations want to take control of their telemetry, building telemetry pipelines from scratch can be a very daunting, complicated task, even when leveraging open-source standards like OpenTelemetry. It requires specialized knowledge across distributed systems, data engineering, and security. This fragmented approach across systems causes higher operational costs; it puts a strain on resources and reduces efficiency as teams have to work with different interfaces and processes ...

The Corporate Network Is Fading - Here's What Replaces It

For decades, enterprise networks were designed around a simple assumption: work happened inside the office. Applications lived in centralized data centers, employees connected through internal infrastructure, and security focused on protecting the perimeter that surrounded everything ... But the way organizations operate today bears little resemblance to that environment. Cloud platforms host critical applications, employees connect from homes and airports as often as they do from offices, and partners collaborate through shared systems that exist far beyond corporate walls. In short, the corporate network no longer resembles the environment it was designed to protect ...

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As an analyst who researches how IT organizations design, build, and operate their networks, I find that network data is a constant source of pain. Network teams struggle with data quality, fragmentation, authority, access, and trust. And these issues undermine everything they try to do. Here are the numbers: Only 45% of network teams are completely confident in the accuracy of their network source of truth, which documents the intent of their network ...

Data Center Outages Are Decreasing but More Costly

The 2026 Global Data Center Survey from Uptime Institute reveals an industry navigating workforce constraints, escalating outage expenses, even as rising costs remain the top concern for management teams ...

Observability Has a Physical Layer Now: What AI Infrastructure Teaches IT Operations

The next observability gap may not be in the code. It may be under the rack. That sounds strange until you think about how AI incidents actually feel in the middle of an investigation ... The application dashboard may be accurate. It may also be stopping at the wrong boundary. AI systems depend on software, but they also depend on a dense physical stack: racks, power paths, thermal margin, maintenance activity and, in many environments, liquid cooling. Those physical dependencies can change slowly before they look like a software incident ...

TLS Certificate Expiration Is Becoming an Observability Problem

Certificate expiration is the rare outage you can see coming. Every TLS certificate carries the date it stops working, so the moment it will begin breaking connections is knowable in advance. That's what makes an expired certificate such a frustrating way to lose a service. What's changing now is how often that date comes around ...

Enterprise Cloud Observability: Choosing the Right Starting Point

Enterprises operate different combinations of workloads across cloud, hybrid and multicloud environments. For business-critical workloads, teams need to consider monitoring and observability early so they can detect health issues, investigate failures, and understand operational impact. Organizations place workloads on cloud platforms based on a combination of technical requirements, economics, existing dependencies, organizational standards, and business priorities. Their monitoring priorities therefore depend on what they operate and where those systems run. Those priorities will not look the same for every organization ...