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

The Latest

Observability Is Becoming the Grid That Powers AI - and It Isn't Ready

For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT Dominates Enterprise Architecture, with Colocation Anchoring Critical Workloads

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

AI is Moving Fast, and Enterprise File Data Is Not Ready

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Move Fast Without the Usual Quality Penalty

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

When the Observability Bill Arrives for Agentic AI

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

AI is Breaking Enterprise Log Management

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

How Edge Intelligence Will Redefine Secure Connectivity

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

Production Inference Shifts Decisively to Private Cloud

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

Why DevOps Teams Build GenAI Tooling While Most Organizations Still Rely on Manual Monitoring

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Why Cloud Reliability Comes Down to People and How to Improve It

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

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

The Latest

Observability Is Becoming the Grid That Powers AI - and It Isn't Ready

For fifteen years, observability lived downstream of everything else. Code shipped, something broke, an engineer went to the dashboards. The job was forensic. The pillars we built, such as logs, metrics, and traces, were designed for that role: tell a human what just happened, fast enough that they can make it stop. That role has quietly ended ...

Hybrid IT Dominates Enterprise Architecture, with Colocation Anchoring Critical Workloads

Hybrid IT has become the standard operating model for enterprises — but that companies are still looking for the right hybrid IT mix, according to the 2026 State of the Data Center Report from CoreSite. After years of cloud migration and hybrid adoption, organizations are shifting their focus from deciding whether to use cloud, colocation or on-premises infrastructure to determining which workloads belong in each environment ...

AI is Moving Fast, and Enterprise File Data Is Not Ready

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Move Fast Without the Usual Quality Penalty

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

When the Observability Bill Arrives for Agentic AI

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

AI is Breaking Enterprise Log Management

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

How Edge Intelligence Will Redefine Secure Connectivity

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

Production Inference Shifts Decisively to Private Cloud

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

Why DevOps Teams Build GenAI Tooling While Most Organizations Still Rely on Manual Monitoring

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Why Cloud Reliability Comes Down to People and How to Improve It

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...