As teams lift and shift or develop new applications in the cloud, they rarely pay attention to costs. What they want is to sleep well at night – and if that takes overprovisioning, then so be it.
But these teams are missing out on one decisive way to deliver a high level of performance and reduce costs at the same time: automated optimization.
Let’s take a closer look at how the tech industry approaches the cloud waste problem and what lessons we can all learn from that.
Cloud computing growth in 2026
Cloud Computing Growth in 2026
Cloud computing continues its remarkable expansion in 2026, driven by the accelerating adoption of AI workloads, edge computing, and multi-cloud strategies across enterprises of all sizes.
Hyperscalers like AWS, Microsoft Azure, and Google Cloud are reporting double-digit revenue growth as organizations deepen their reliance on cloud-native infrastructure to power everything from generative AI applications to real-time data analytics.
A defining trend this year is the surge in demand for GPU-dense cloud capacity. As businesses race to deploy and fine-tune large language models, cloud providers are scrambling to expand their AI-optimized data centers, with significant investment flowing into new regions across Southeast Asia, Latin America, and Africa. Sovereign cloud initiatives are also gaining traction, as governments increasingly mandate that sensitive data remain within national borders.
Hybrid and multi-cloud architectures have become the norm rather than the exception, with Kubernetes-based orchestration serving as the connective tissue between on-premises infrastructure and public cloud environments.
Looking ahead, the lines between cloud, edge, and on-device computing are blurring rapidly. Industry analysts project the global cloud market will surpass $1 trillion in annual revenue by the end of 2026, cementing cloud infrastructure as the foundational layer of the modern digital economy.
The volume of Kubernetes cloud waste
After the donation of Kubernetes to the CNCF by Google, we saw a significant shift in the enterprise market to adopt this as the container technology of choice.
We find that many companies rely on their cloud providers to manage their Kubernetes infrastructure, often using managed services such as Amazon EKS, Azure AKS, and Google GKE.
The larger operators of K8s tend to host the service on their own – for instance, PayPal.
Many customers who analyzed their utilization with our platform found that their Kubernetes infrastructure is overprovisioned by even 40%!
Why do so many teams leave cost optimization out of the equation before costs become too much of an issue?
Optimizing cloud resources for Kubernetes is no small feat
The top reason enterprises are late in their optimization efforts is that they rely on too many manual processes. Many cost optimization challenges revolve around understanding where costs come from via accurate observability and reporting.
For teams running Kubernetes, cloud costs often become a black box.
Facing so many different available OSS options to help manage their Kubernetes infrastructure, the process of bringing all the cost data together becomes tedious and time-consuming. Another challenge arises because of how Kubernetes works. Since it’s a fair system, it provisions equally across all the clusters and the underlying virtual machines.
Where does all of this overprovisioning come from?
Here’s one good source: 44% of cloud spend covers non-production resources. Since they’re needed only during the 40-hour workweek, they’re often left idle for other 128 hrs (76%) during one week. Given that you pay for compute resources by the minute or second, a large portion of the time these resources are being paid for but not used.
If we combine this finding with the cost of the public cloud, it means that in 2026, companies will waste $27.1 billion on idle cloud assets.
How to check cloud waste using a free cost monitoring tool
Cast AI offers a cost monitoring module where you can see a list of all cluster workloads with their efficiency details per selected period. Among them, you’ll find $ wasted, which refers to the amount of money wasted in US dollars.
Each entry also contains the requested and used resource hours, which correspond to resources multiplied by hours of usage.
For example, if a workload with requests set to 2 CPUs runs for 48 hours over the chosen time, the total requested CPU hours will be 96. If a workload uses 0.5 CPU on average throughout those 48 hours, its total utilization is 24 CPU hours.
Customers using Cast AI managed mode can also utilize a fast recommendation patch to apply to their workload and adjust workload resource requirements based on our suggestions.
We deduct the workload’s utilized resources from the total number of requested resources to compute the amount of wasted CPU and RAM resources, for example:
96 CPU hours – 24 CPU hours = 72 CPU hours
Take the first step to reducing cloud waste
Rightsizing cloud resources or scaling them down when not needed are two intelligent methods teams use to reduce their cloud bills.
These approaches were traditionally time-consuming. They required team members to constantly watch their cloud infrastructure and analyze the massive provider offer to find virtual machines of the right size and type.
Teams can now avoid this problem thanks to automation.
A solution like Cast AI allows teams to focus on higher priorities, reclaim a healthy portion of the cloud bill, and return their most valuable asset: time!
Start by finding out how much budget you could save with automated cloud capacity management.
CAST AI clients save an average of 63%
on their Kubernetes bills
Book a call to see if you too can get low & predictable cloud bills.
