Reducing Cloud Waste with Artificial Intelligence and Machine Learning

With the migration to the cloud gaining momentum over the past several years, IT and DevOps team members began using a new phrase – ‘cloud waste’. The term refers to the wasteful spending, and unused cloud resources, that frequently occurs with cloud service agreements. Generally, this waste stems from over-provisioned infrastructure that ends up being under-used or not used at all. 

It’s a lot like buying a non-refundable airline ticket then not taking the flight. You pay for your seat whether you’re in it or not. It’s essentially the same with cloud services. You contract for the cloud services you think you’ll need for a given month and pay for that availability, regardless of how much of it your organization actually uses for that month. Cloud waste is the difference between the services you paid for and how much of them you actually used.

A Problem with Growing Visibility

Earlier on, cloud waste was a term used mostly by IT and DevOps team members. But that’s changing due to the size of the problem and its financial impact on organizations. How big is it? Industry analysts estimate that roughly 30% of cloud spending is wasted. In 2020, that added up to $17.6 billion according to the experts at Other market watchers such as Accenture put the rate of waste closer to 40%.

As the source of that much wasteful spending, it’s no surprise that more and more finance departments, CFOs and even Board members are asking about cloud waste and ways to reduce it. When those questions are asked of you and your team – and they surely will be sooner or later – It’s best to be prepared. To that end, we offer the following answers to the question of how to reduce cloud waste.

Artificial Intelligence and Machine Learning to the Rescue

Running cloud-native apps at scale is a complex undertaking. That’s why many developers and DevOps teams, especially those new to this environment, hedge their bets by over-provisioning cloud services. That can be okay early on, but as usage and spending grows, the cloud waste problem gets bigger and bigger.

Due to the complexity and speed involved, trial-and-error application tuning just won’t cut it. With Kubernetes, for example, it’s extremely difficult for developers to look inside containers and figure out the best settings and variable choices for producing desired application behaviors. And it’s impossible for even the best developers to do so fast enough to make a difference.

Fortunately, artificial intelligence (AI) and machine learning (ML) are ideally suited for solving this type of optimization problem. As we all know, AI is a broad category that includes various types of machine intelligence. Machine learning is a specific type of AI that’s geared toward using algorithms and data gained through experience to make continual improvements automatically.

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How Machine Learning Can Reduce Cloud Waste

The good news for developers is that ML gives them an effective and efficient way to first identify cloud waste in their operations, and then significantly reduce it.  New, ML-powered systems (such as our StormForge Platform) leverage established data science methodologies to automate the process of testing, analyzing, and optimizing Kubernetes applications based on the specific performance and cost goals of those applications.

 Through automated experimentation and observation, these platforms allow for efficient exploration of an applications’ parameters. That results in automatically generated configuration recommendations and actions that ensure reliable application deployment and optimal performance.

On the performance testing side, ML-powered systems give developers fast and accurate ways to assess how their applications will perform under varied loads. With ML-driven application optimization, they gain an automated way to discover the optimal configurations for their applications without risking subpar performance levels or downtime.

In short, with automated performance testing and application optimization, ML-powered systems give developers the tools and visibility they need to manage their cloud-native apps so that they consistently deliver the right levels of performance without overprovisioning.

It’s time for developers to stop buying seats on cloud flights that they’ll never take. It’s time to stop throwing that money out the window. New machine learning-based platforms give them a way to do exactly that. (Note: For more detailed information about how machine learning is being applied to reduce cloud waste as a step-by-step guide, visit our blog on 10 Steps to Reduce Cloud Waste.)

Addressing Cloud Waste’s Environmental Issues

Although most people don’t realize it, the cloud industry is a major contributor to climate change. As surprising as it may be, data centers have in fact become one of the largest annual energy consumers and emitters of carbon pollution. They represent about 3% of the world’s total energy consumption and they emit nearly 100 million metric tons of CO2 into the atmosphere each year.

If every company made an effort to reduce their consumption of cloud computing resources in general, and especially their cloud waste, we could make a substantial, positive impact on the environment. 

Machine learning provides us with the tools we need to ‘right-size’ our purchases and usage of cloud resources. Doing so can and will make a difference. From both the business and environmental perspectives, it’s a smart move and the right thing to do. StormForge is leading the way on this front with, among other things, the pledge campaign it is running to reduce cloud waste. Learn how you can get involved.


Padding cloud services orders with twice, three times, or quadruple of what was actually needed used to be okay. But that was back when going cloud-native seemed riskier than it does today, and when Kubernetes was new and teams were scrambling up its learning curve. Plus, in those earlier days, developers lacked the tools they needed to make better, more accurate resourcing decisions for their apps. So, over-provisioning was overlooked. 

But that was then, and this is now. Over-provisioning is no longer a viable strategy. Machine learning-powered platforms illuminate a new, more efficient, and more environmentally conscious path. Isn’t it time we all got on it?