Over the past 15+ years, close engagements with organizations across sectors have revealed a consistent pattern that cloud costs have a tendency to escalate quietly. In many cases, businesses only take notice when a sudden spike in the monthly bill triggers concern. Despite the promise of scalability and efficiency, cloud environments can become financially unpredictable without the right visibility, governance, and cost optimization strategies in place.
Cloud has transformed the way we operate. From faster deployments to global scalability, it has unlocked tremendous flexibility. But the flip side is just as real. Behind the convenience lies a growing challenge that many have yet to fully confront i.e. cloud waste.
The term might sound harsh, but the reality is sobering. Cloud waste refers to spending on resources that are not serving any real purpose. These could be underutilised instances, idle databases, orphaned volumes, or even forgotten development environments that continue running long after the sprint ends.
Recent industry surveys paint a stark picture. More than 75 percent of organisations report an increase in cloud waste as their usage grows. In many cases, businesses are unknowingly spending up to 40–50 percent of their cloud budget on resources that are idle, underutilised, or entirely unnecessary. In one study, as much as $44.5 billion of enterprise cloud infrastructure spend was estimated to be wasted in a single year.
Cloudkeeper’s internal analysis across more than 2000 customer AWS accounts revealed that - 97% of teams were missing out on key savings opportunities. Even those with mature FinOps or cloud governance frameworks were leaving nearly 23 percent of potential savings untouched.
As one CTO candidly informed, “We knew there was some waste. But when we saw the numbers, it hit us just how much was slipping through the cracks.”
This isn't due to negligence. Most teams genuinely believe they have already optimised their environment. The real issue lies in visibility. With so much happening across environments - deployments, scaling events, new services, regional expansions - it becomes difficult to keep track. Engineers are busy shipping features, managing incidents, and ensuring uptime. In the middle of it all, cost optimization tends to take a backseat.
Cloud overspend doesn’t just impact the finance team. It affects the business at every level. Left unchecked, it can limit experimentation, slow down innovation, and eventually eat into the funds earmarked for growth. After speaking to business leaders who were surprised to see cloud infrastructure become the second-largest IT cost after salaries. And unlike salaries, cloud costs can and should be managed dynamically.
More than once, it’s observed that internal misalignment widens the problem. Engineering teams don’t always have access to billing insights. Finance teams may not understand the technical rationale behind provisioning decisions. Leadership often assumes things are under control, simply because there haven’t been any major cost spikes. But the real issue is rarely a single spike. It’s the slow drip that goes unnoticed.
It’s helpful to think of cloud cost efficiency like personal fitness. One can’t just optimize once and expect lasting results. It needs consistent attention, periodic assessments, and corrective action. What worked last quarter might no longer be relevant today.
Cloud fitness is about asking the right questions regularly. Are we using the right instance types for our current workloads? Are we shutting down development environments during off-hours? Have we identified and removed all orphaned resources? Are we making use of reserved instances or savings plans wherever possible?
In some of the most cloud-savvy organisations, cost efficiency is not a separate task rather embedded into development workflows. Engineers are encouraged to provision responsibly. DevOps teams are measured not just on reliability, but also on cost hygiene. And cross-functional teams come together during planning cycles to forecast and discuss cloud spend just like any other strategic investment.
Having seen different strategies play out, it can be said that there is no silver bullet. But there are some consistent best practices that deliver results.
Rightsizing resources based on usage trends. If an instance has run under 40 percent CPU and memory usage for four weeks, there is likely a more appropriate size available.
Automating non-production environment schedules. Turning off development and testing resources outside business hours can dramatically reduce monthly costs.
Tagging resources properly. It helps identify unused assets and link costs to teams or projects, bringing accountability into the equation.
Using budgeting and alerting tools to monitor spending in real time and avoid surprises.
Cleaning up orphaned resources such as unattached volumes, idle load balancers, and expired snapshots.
Reviewing architectural decisions periodically. What made sense six months ago may not be the most cost-effective setup today.
AI and machine learning are quickly becoming central to how cloud waste is detected and addressed. Here are some ways AI is already delivering value:
Predictive Resource Sizing: AI models analyse historical usage data to suggest the most efficient instance types and configurations for upcoming workloads.
Idle Resource Detection: Intelligent algorithms flag underutilised or forgotten resources across environments such as volumes, VMs, or load balancers, thereby reducing manual effort.
Automated Scheduling: AI can automatically turn off non-production environments during off-hours based on usage patterns, saving costs without disrupting workflows.
Anomaly Detection: Machine learning systems can identify unusual spikes or dips in cloud spend, helping teams catch billing issues or misconfigurations early.
Reservations Management: Based on real-time usage trends, AI can suggest when to purchase reserved instances or savings plans - and even recommend the optimal commitment levels.
Cost-Aware Deployment Guidance: Some platforms now integrate AI-powered insights directly into CI/CD pipelines, enabling engineers to make cost-informed choices at deployment time.
Optimising cloud costs requires buy-in from the leadership, participation from engineering, and support from finance. The companies that get this right are the ones that treat cost as a shared responsibility, not just a finance metric to be reviewed post-facto.
The emergence of FinOps as a discipline is a direct response to this need. It encourages collaboration, transparency, and continuous improvement. And in doing so, it helps businesses get the full value out of their cloud investments.
Global cloud spending is projected to exceed $700 billion by 2025. If current trends continue, nearly $200 billion of that could go to waste. That’s not just inefficiency. It is a lost opportunity.
Gartner predicts that by 2026, more than 60 percent of enterprises will have active FinOps teams, up from just 20 percent in 2022. This trend is encouraging, but also highlights the urgency to act now.
Cloud waste is not a problem that can be solved once and forgotten. It is an ongoing risk that grows silently with scale and complexity. But it is also one of the biggest opportunities for businesses today - an area where relatively small process changes and automation can unlock meaningful financial and operational benefits.
Those who embrace this shift through better visibility, smarter tools, and a collaborative mindset, will be far better prepared for the future of technology and cloud computing.
[Disclaimer: The views expressed are solely of the author and Analytics Insight does not necessarily subscribe to it. Analytics Insight shall not be responsible for any damage caused to any person/organization directly or indirectly.]