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The High Cost of Low Cloud Cost Visibility

Bill Buckley
CloudZero

Cloud spending continues to soar. Globally, cloud users spent a mind-boggling $563.6 billion last year on public cloud services, and there's no sign of a slowdown. In fact, Gartner predicts that spending will soar to $678.8 billion this year.

This skyrocketing spending growth underscores the importance of cloud cost optimization. If done properly, organizations can transform cost data into actionable business insights and coordinates to maximize the ROI of their cloud investments.

CloudZero's State of Cloud Cost Report 2024 found that organizations are still struggling to gain control over their cloud costs and that a lack of visibility is having a significant impact. Among the key findings of the report:

Cloud costs are out of control. Most organizations say they don't have control over their cloud costs. The number of companies reporting that their costs are "way too high" rose in comparison to a similar survey conducted in 2022.

Companies lose productivity due to low visibility. Almost 90% of participants indicated that a lack of cloud cost visibility keeps them from performing their job well. That is an increased level of lost productivity compared to the previous survey.

Cloud cost: Not just for executives. In 2024, the whole leadership hierarchy is interested in cloud costs. It's no longer merely a C-suite issue but has become a company-wide focus of attention.

With engineering ownership comes cost control. The survey data indicates that when the engineering function owns cloud cost management, the result is better business outcomes, such as higher confidence in reporting accuracy. 81% of survey participants noted that when engineering has some level of ownership, their cloud costs are "about where they should be."

Engineering ownership also increases finance-engineering alignment. When engineers take part in cloud cost management, their priorities are essentially indistinguishable from those of the finance team.

The Cost of Low Visibility

It's concerning that less than 50% of organizations said their cloud costs are healthy; in fact, 58% of respondents said their costs are too high. What's more worrisome is the survey data revealing a rise in the number of organizations reporting that their costs are "way too high" — a shift from 11% in 2022 to 14% this year. Though that's not a massive increase, it does reveal an ongoing lack of control with respect to cloud costs.

When asked how effectively survey participants can allocate cloud spend to the various parts of their business, 42% responded that they can only estimate those costs. More surprising still, more than 20% of participants have little to no idea how much those various parts cost. Two-thirds of organizations can't accurately measure unit costs.

Adding insult to injury, two-thirds of organizations noted that looking into rising cloud costs interferes with both finance and engineering workflows. The survey data shows this has a greater effect on companies than in years past.

As for the engineers themselves, 66% noted that their work is disrupted to some degree by a lack of visibility into cloud costs. And 22% of those reported high levels of disruption, double the figure (11%) in 2022.

The Secret Is Engineering Engagement

High-functioning engineering teams want their work to be connected to business and user outcomes. The fact that many of them can't attribute cloud costs to business units reveals a serious problem in cloud software engineering.

Every engineering decision is a buying decision

Cloud cost optimization starts with engineers. Every engineering decision is a buying decision; whenever an engineer spins up a new cloud resource, they incur a new cost. When engineers have thorough visibility into their cloud costs, their purchasing decisions are based on reality, not guesswork — and the survey results validate this idea. Greater visibility yields greater engineering engagement, leading to better business outcomes like cost savings, maximized profits, and increased accountability.

Methodology: This report is based on a survey conducted by CloudZero of 1,000 US engineering and finance workers (50/50 split) in firms with 100 to 9,999 employees and with at least $500,000 annual total cloud spend who use either Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure as their primary cloud service provider. The survey was carried out in January 2024.

Bill Buckley is SVP of Engineering at CloudZero

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Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

The High Cost of Low Cloud Cost Visibility

Bill Buckley
CloudZero

Cloud spending continues to soar. Globally, cloud users spent a mind-boggling $563.6 billion last year on public cloud services, and there's no sign of a slowdown. In fact, Gartner predicts that spending will soar to $678.8 billion this year.

This skyrocketing spending growth underscores the importance of cloud cost optimization. If done properly, organizations can transform cost data into actionable business insights and coordinates to maximize the ROI of their cloud investments.

CloudZero's State of Cloud Cost Report 2024 found that organizations are still struggling to gain control over their cloud costs and that a lack of visibility is having a significant impact. Among the key findings of the report:

Cloud costs are out of control. Most organizations say they don't have control over their cloud costs. The number of companies reporting that their costs are "way too high" rose in comparison to a similar survey conducted in 2022.

Companies lose productivity due to low visibility. Almost 90% of participants indicated that a lack of cloud cost visibility keeps them from performing their job well. That is an increased level of lost productivity compared to the previous survey.

Cloud cost: Not just for executives. In 2024, the whole leadership hierarchy is interested in cloud costs. It's no longer merely a C-suite issue but has become a company-wide focus of attention.

With engineering ownership comes cost control. The survey data indicates that when the engineering function owns cloud cost management, the result is better business outcomes, such as higher confidence in reporting accuracy. 81% of survey participants noted that when engineering has some level of ownership, their cloud costs are "about where they should be."

Engineering ownership also increases finance-engineering alignment. When engineers take part in cloud cost management, their priorities are essentially indistinguishable from those of the finance team.

The Cost of Low Visibility

It's concerning that less than 50% of organizations said their cloud costs are healthy; in fact, 58% of respondents said their costs are too high. What's more worrisome is the survey data revealing a rise in the number of organizations reporting that their costs are "way too high" — a shift from 11% in 2022 to 14% this year. Though that's not a massive increase, it does reveal an ongoing lack of control with respect to cloud costs.

When asked how effectively survey participants can allocate cloud spend to the various parts of their business, 42% responded that they can only estimate those costs. More surprising still, more than 20% of participants have little to no idea how much those various parts cost. Two-thirds of organizations can't accurately measure unit costs.

Adding insult to injury, two-thirds of organizations noted that looking into rising cloud costs interferes with both finance and engineering workflows. The survey data shows this has a greater effect on companies than in years past.

As for the engineers themselves, 66% noted that their work is disrupted to some degree by a lack of visibility into cloud costs. And 22% of those reported high levels of disruption, double the figure (11%) in 2022.

The Secret Is Engineering Engagement

High-functioning engineering teams want their work to be connected to business and user outcomes. The fact that many of them can't attribute cloud costs to business units reveals a serious problem in cloud software engineering.

Every engineering decision is a buying decision

Cloud cost optimization starts with engineers. Every engineering decision is a buying decision; whenever an engineer spins up a new cloud resource, they incur a new cost. When engineers have thorough visibility into their cloud costs, their purchasing decisions are based on reality, not guesswork — and the survey results validate this idea. Greater visibility yields greater engineering engagement, leading to better business outcomes like cost savings, maximized profits, and increased accountability.

Methodology: This report is based on a survey conducted by CloudZero of 1,000 US engineering and finance workers (50/50 split) in firms with 100 to 9,999 employees and with at least $500,000 annual total cloud spend who use either Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure as their primary cloud service provider. The survey was carried out in January 2024.

Bill Buckley is SVP of Engineering at CloudZero

Hot Topics

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...