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In recent years, businesses have stampeded to move systems to the cloud. But in doing so they often unwittingly introduce multiple layers of virtualization which means they can be paying to run the same processes multiple times.
Can optimization techniques help to improve performance and cut cloud costs? We spoke to Dr. Ignacio M Llorente, CEO and founder of OpenNebula Systems to find out more.
BN: What is ‘cloud waste’ and why is it a problem?
IL: Cloud waste refers to any unnecessary spending on cloud resources due to underutilization or misallocation.
Public cloud pricing is increasing faster than hardware costs, making inefficient cloud usage an expensive burden for organizations to carry. Many companies overcommit to cloud services, paying for resources they cannot use. Or, they sign long-term obligations and lock themselves into costly, inflexible agreements. When their organizational strategy inevitably shifts and they decide to scale down, that contract no longer fits their needs, yet they’re still footing the bill.
Alternatively, when a company’s third-party provider undergoes an organizational shift — say, a merger, acquisition or change in pricing — the customer may find themselves locked into unnecessarily high costs or cloud resources they cannot use. All of these scenarios create cloud waste, which can accumulate quickly.
BN: How do companies get themselves into these situations?
IL: As I mentioned, long-term contracts and poor workload distribution are common causes of cloud waste. Failing to leverage a hybrid cloud strategy is another. Hybrid cloud strategies may seem daunting but they’re crucial to maintaining cloud costs. For instance, many companies run their less critical systems on-premise and then ‘burst’ onto the public cloud for additional processing power only when needed. The result is less waste 90 percent of the time.
Many companies also assume that public cloud costs always remain predictable — that reality gets shattered sooner or later.
A more recent culprit for cloud waste is AI expansion. Many organizations adopt AI capabilities without realizing that these systems require immense computational power, which of course requires additional cloud resources and becomes prohibitively expensive quite quickly. This is particularly troubling because it not only incurs cloud waste, it jeopardizes the success of your AI programs.
That said, it’s important to note that cloud waste is actually extremely common. Many companies are currently struggling with the burden of non-advantageous contracts or improper infrastructure.
BN: What’s the first step in understanding where waste is occurring?
IL: The first step is to conduct a comprehensive assessment of cloud usage and spending thus far. Businesses must analyze their workloads, identify underutilized resources, and evaluate whether to shift certain workloads to private or hybrid cloud environments. (The answer is almost always ‘yes,’ but of course, accomplishing that shift may seem daunting at first.)
Start small by assessing cost patterns and aligning them with actual demand. Do this in-house if you can. Beyond that first step, it’s wise to speak with a third-party expert to determine the correct course of action.
BN: Is it possible to cut costs and also improve performance?
IL: 100 percent, yes, and this is where hybrid cloud strategies become essential. The idea that more computational power equates to better performance is actually part of the underlying issue of cloud waste. It’s not always about unlocking more cloud resources but about scaling judiciously.
By balancing private and public cloud resources, companies can control costs while ensuring they maintain the necessary computing power. AI and data-heavy workloads, in particular, benefit from a mix of on-premises infrastructure and scalable public cloud options. Additionally, investing in confidential computing and automation can enhance security and efficiency while reducing unnecessary cloud expenses.
BN: Can automation help?
IL: Absolutely. Automation can play a crucial role in optimizing cloud usage by dynamically adjusting resources based on demand. Automated orchestration tools can streamline workload distribution, ensuring companies use the most cost-effective infrastructure for each task. AI-driven automation can also improve performance by managing hybrid cloud environments more efficiently, reducing manual oversight and minimizing human error.
Additionally, automated rightsizing can adjust a company’s resource allocation based on actual usage vs. anticipated usage. This is a huge advantage because it’s incredibly difficult to predict cloud usage patterns without tons of historical data. Most organizations simply don’t have the time or resources to process that amount of information — that’s where automation and continuous monitoring become so crucial.
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