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The Overconfidence Effect in Cloud Cost Management is Real (and Expensive)

Kash Shaikh
Virtana

"If I had asked people what they wanted, they would have said faster horses." - Henry Ford

In other words, do not try to make horses go incrementally faster, create the automobile (like the Ford Model T). Digital transformation, including its underpinning cloud infrastructure, is meant to deliver innovations that leapfrog the status quo.

The 21st century version uses digital technology and automation to overcome human limitations. Example: Performing calculations in the blink of an eye that would take a person lifetimes to complete. We are often blind to the ways our human limitations can creep into various systems and processes — and this overconfidence in the status quo can be limiting.

According to the results of a recent survey of 350 IT leaders at global organizations, we found evidence of the overconfidence effect in cloud migration.

The overconfidence effect is a well-established cognitive bias where a person's subjective confidence in their judgment is greater than the objective accuracy of those judgments. In short, it is when we systematically overestimate our knowledge and ability to predict on a massive scale.

Here is what we found: 85% of cloud decision makers surveyed feel confident managing their cloud bills, but 82% of respondents have incurred unnecessary cloud costs. Clearly, there is no guarantee that confidence in your cloud cost management capabilities, even among tech-savvy leaders, means you can avoid needless spending.

So, what does this mean for companies in the race to digital transformation?

Obviously, the pandemic accelerated organizations' journey to the cloud to enable agile, on-demand, flexible access to resources, helping them align with a digital business's dynamic needs. We heard from many of our customers at the start of lockdown last year, saying they had to shift to a remote work environment, seemingly overnight, and this effort was heavily cloud-reliant. However, blindly forging ahead can backfire.

This latest survey reveals that in addition to the overconfidence effect, enterprises are facing additional challenges across the hybrid cloud thanks to disjointed point tools, silos, lack of visibility, unexpected costs, lack of programmatic optimization, and the role risk plays in cloud cost management.

Cloud Waste is a Massive Problem

One of the biggest challenges is how to manage workloads operating in the cloud without incurring unexpected and unnecessary costs, which can eat into budgets needed for other areas of transformation. Over the many years we have been working with customers to optimize their cloud infrastructures, we have found that up to a third of their cloud spend is waste.

Clearly this is a rampant problem that continues to plague enterprises. It is important to note that the 82% from the survey represents unnecessary costs that respondents are aware of. It is highly likely that companies are spending far more than they need to without even knowing it.

For example, 56% of respondents lack programmatic cloud cost management capabilities. This can mean either that teams are spending too much time managing cloud costs or that the cloud waste is allowed to fester. Either way, time and money are being spent unwisely. Not only that, it is likely that there is waste that is not being uncovered. And the more the overconfidence effect is at play within an organization, the less likely it is that this waste will be identified.

The lack of visibility across hybrid and multi-cloud environments also blurs the issue. 86% of respondents said they cannot get a global view of cloud costs within minutes, creating delays and potentially reducing agility. 71% of respondents agreed that limited visibility across the hybrid cloud environment hinders their ability to maximize value, creates inefficiencies, and wastes time.

Disjointed tools also present a challenge. 72% of respondents said they are fed up with piecing together disparate management tools to monitor and manage everything from infrastructure performance to migration readiness to cloud cost, and 62% report that they have to cobble together multiple tools, systems, and custom scripts to get a global view of cloud costs. Again, these are the very same respondents who said they are confident in their ability to manage cloud costs.

What Leads to Waste also Impedes Transformation

Lack of programmatic management, limited visibility, and disparate tools affect more than just cloud costs. IT leaders are also grappling with issues that could hamper a successful digital transformation. 68% of survey respondents stated that their teams operate in silos, and 70% said that limited collaboration hinders their ability to adapt quickly and improve business outcomes.

Additionally, 66% of respondents stated that it is hard to understand if they are delivering the service levels the business needs, and 65% agreed that when there is an issue, they are hard-pressed to identify the business impact.

Finally, 77% cited increased performance issues as one of the reasons that pressure on cloud teams is on the rise.

The bottom line is that if you do not know what is going on across your entire infrastructure, you do not know if you are adequately serving the needs of the business and you cannot deliver performance levels that the business demands, which means that you have not met some of the foundational needs of digital transformation.

Clearing the way to cost-effective transformation

Wherever you have "seams" in your cloud monitoring and management — whether that is a result of multiple clouds, siloed teams, disparate tools, or time lags, for example — you have blind spots where unnecessary costs and other problems can hide. Combine that with the overconfidence effect and those risks only go up. A single modular platform can eliminate those seams and create appropriate levels of confidence rooted in data to de-risk cloud migrations; deliver deep precision observability into workloads before, during, and after a move; and optimize and manage efficiently once workloads are in the cloud.

This is underscored by Archana Vankatarman, Associate Research Director of Cloud Data Management at IDC Europe who said, "The duct-taped point tools and silos can make cloud cost management complex. The belief that they are wasting at least 15% of their public cloud spending will drive enterprises to actively invest in cloud cost management to halve cloud waste."

The bottom line is that cloud management is always changing. If you feel confident about your cloud costs based on your gut, think again. Have the right tools to know before you go forward with any initiative. In the end, you will not be upset that you took the time to get it right — and save money.

Methodology: Arlington Research, commissioned by Virtana, surveyed 350 cloud decision makers in April 2021 at US- and UK-based organizations with 250+ employees to better understand multi-cloud deployment experiences.

Kash Shaikh is CEO and President of Virtana

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The Overconfidence Effect in Cloud Cost Management is Real (and Expensive)

Kash Shaikh
Virtana

"If I had asked people what they wanted, they would have said faster horses." - Henry Ford

In other words, do not try to make horses go incrementally faster, create the automobile (like the Ford Model T). Digital transformation, including its underpinning cloud infrastructure, is meant to deliver innovations that leapfrog the status quo.

The 21st century version uses digital technology and automation to overcome human limitations. Example: Performing calculations in the blink of an eye that would take a person lifetimes to complete. We are often blind to the ways our human limitations can creep into various systems and processes — and this overconfidence in the status quo can be limiting.

According to the results of a recent survey of 350 IT leaders at global organizations, we found evidence of the overconfidence effect in cloud migration.

The overconfidence effect is a well-established cognitive bias where a person's subjective confidence in their judgment is greater than the objective accuracy of those judgments. In short, it is when we systematically overestimate our knowledge and ability to predict on a massive scale.

Here is what we found: 85% of cloud decision makers surveyed feel confident managing their cloud bills, but 82% of respondents have incurred unnecessary cloud costs. Clearly, there is no guarantee that confidence in your cloud cost management capabilities, even among tech-savvy leaders, means you can avoid needless spending.

So, what does this mean for companies in the race to digital transformation?

Obviously, the pandemic accelerated organizations' journey to the cloud to enable agile, on-demand, flexible access to resources, helping them align with a digital business's dynamic needs. We heard from many of our customers at the start of lockdown last year, saying they had to shift to a remote work environment, seemingly overnight, and this effort was heavily cloud-reliant. However, blindly forging ahead can backfire.

This latest survey reveals that in addition to the overconfidence effect, enterprises are facing additional challenges across the hybrid cloud thanks to disjointed point tools, silos, lack of visibility, unexpected costs, lack of programmatic optimization, and the role risk plays in cloud cost management.

Cloud Waste is a Massive Problem

One of the biggest challenges is how to manage workloads operating in the cloud without incurring unexpected and unnecessary costs, which can eat into budgets needed for other areas of transformation. Over the many years we have been working with customers to optimize their cloud infrastructures, we have found that up to a third of their cloud spend is waste.

Clearly this is a rampant problem that continues to plague enterprises. It is important to note that the 82% from the survey represents unnecessary costs that respondents are aware of. It is highly likely that companies are spending far more than they need to without even knowing it.

For example, 56% of respondents lack programmatic cloud cost management capabilities. This can mean either that teams are spending too much time managing cloud costs or that the cloud waste is allowed to fester. Either way, time and money are being spent unwisely. Not only that, it is likely that there is waste that is not being uncovered. And the more the overconfidence effect is at play within an organization, the less likely it is that this waste will be identified.

The lack of visibility across hybrid and multi-cloud environments also blurs the issue. 86% of respondents said they cannot get a global view of cloud costs within minutes, creating delays and potentially reducing agility. 71% of respondents agreed that limited visibility across the hybrid cloud environment hinders their ability to maximize value, creates inefficiencies, and wastes time.

Disjointed tools also present a challenge. 72% of respondents said they are fed up with piecing together disparate management tools to monitor and manage everything from infrastructure performance to migration readiness to cloud cost, and 62% report that they have to cobble together multiple tools, systems, and custom scripts to get a global view of cloud costs. Again, these are the very same respondents who said they are confident in their ability to manage cloud costs.

What Leads to Waste also Impedes Transformation

Lack of programmatic management, limited visibility, and disparate tools affect more than just cloud costs. IT leaders are also grappling with issues that could hamper a successful digital transformation. 68% of survey respondents stated that their teams operate in silos, and 70% said that limited collaboration hinders their ability to adapt quickly and improve business outcomes.

Additionally, 66% of respondents stated that it is hard to understand if they are delivering the service levels the business needs, and 65% agreed that when there is an issue, they are hard-pressed to identify the business impact.

Finally, 77% cited increased performance issues as one of the reasons that pressure on cloud teams is on the rise.

The bottom line is that if you do not know what is going on across your entire infrastructure, you do not know if you are adequately serving the needs of the business and you cannot deliver performance levels that the business demands, which means that you have not met some of the foundational needs of digital transformation.

Clearing the way to cost-effective transformation

Wherever you have "seams" in your cloud monitoring and management — whether that is a result of multiple clouds, siloed teams, disparate tools, or time lags, for example — you have blind spots where unnecessary costs and other problems can hide. Combine that with the overconfidence effect and those risks only go up. A single modular platform can eliminate those seams and create appropriate levels of confidence rooted in data to de-risk cloud migrations; deliver deep precision observability into workloads before, during, and after a move; and optimize and manage efficiently once workloads are in the cloud.

This is underscored by Archana Vankatarman, Associate Research Director of Cloud Data Management at IDC Europe who said, "The duct-taped point tools and silos can make cloud cost management complex. The belief that they are wasting at least 15% of their public cloud spending will drive enterprises to actively invest in cloud cost management to halve cloud waste."

The bottom line is that cloud management is always changing. If you feel confident about your cloud costs based on your gut, think again. Have the right tools to know before you go forward with any initiative. In the end, you will not be upset that you took the time to get it right — and save money.

Methodology: Arlington Research, commissioned by Virtana, surveyed 350 cloud decision makers in April 2021 at US- and UK-based organizations with 250+ employees to better understand multi-cloud deployment experiences.

Kash Shaikh is CEO and President of Virtana

Hot Topics

The Latest

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 ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...