Overprovisioning of kubernetes workloads
Enterprise cloud spending is out of control. Organizations continue to allocate far more compute capacity than necessary, just to play it safe. The result? Wasted resources and soaring costs. Cast AI’s research found that most Kubernetes clusters were running at just 10% of their allocated CPU and under 25% of their memory capacity throughout 2024. That’s an insane level of inefficiency.
Laurent Gil, President and Co-founder of Cast AI, points out that overprovisioning is a cloud-wide issue. Companies are spending on compute resources they don’t use because they fear the alternative: resource shortages, downtime, and customer dissatisfaction. Instead of taking a strategic, data-driven approach to scaling, many enterprises default to over allocating just to avoid worst-case scenarios.
This is where leadership decisions need to be sharper. Overprovisioning might seem like the safest route, but it’s the most expensive one. Companies should invest in automation and predictive scaling to align cloud provisioning with actual workload demands. The goal is simple: match supply with demand, and stop paying for what you don’t use.
Cloud cost management remains a major challenge
Cloud costs keep rising, even as AWS, Microsoft, and Google Cloud push competitive pricing. That doesn’t make sense—until you look at how enterprises buy cloud resources. Companies are spending more not because prices are going up, but because they aren’t managing their consumption effectively.
The global cloud infrastructure market grew 22% year-over-year in 2024, hitting $330 billion, according to Synergy Research Group. While hyperscalers are offering lower per-unit costs, enterprises are still dealing with unpredictable bills. Why? The lack of real-time visibility into cloud usage and an over-reliance on long-term contracts that trade flexibility for short-term discounts.
CIOs and CFOs need to rethink their approach. The right move is to optimize consumption. Smarter workload placement, real-time cost tracking, and a clear cloud strategy can bring costs under control without compromising performance.
Savings programs can backfire if resources go unused
Cloud savings programs sound great—until you realize they often lead to wasted spending. AWS, Microsoft, and Google Cloud offer tiered pricing, spot-instance discounts, and long-term commitment plans that can cut prices by up to 75%. But there’s a catch: if you don’t use what you commit to, those savings turn into sunk costs.
Procurement teams typically buy more than they need to avoid shortages. This makes sense in a traditional IT setting, but in the cloud, it’s a flawed approach. Unlike physical infrastructure, cloud resources can be scaled dynamically. Yet many enterprises lock in long-term commitments to secure lower rates, only to realize later they’re not using half of what they’ve paid for.
Executives should focus on flexibility. The best cloud strategy is one that maximizes discounts without sacrificing adaptability. Shorter commitments, real-time monitoring, and a mix of on-demand and spot instances can make sure cloud resources match business needs—not the other way around.
AI workloads are intensifying cloud cost pressures
AI is driving cloud demand to new levels, and companies are struggling to keep up. The problem isn’t only that AI workloads need more compute power—it’s that many organizations don’t have the right strategy to optimize these workloads.
Cast AI found that 6% of workloads exceeded their requested memory at least once in a 24-hour period, leading to service disruptions. AI models require high GPU and CPU power, but provisioning these resources inefficiently results in two problems: either systems experience performance issues, or companies overallocated and wasted money.
Laurent Gil highlights a key issue: even when there’s excess CPU, AI workloads can still run out of memory. This imbalance leads to instability, forcing teams to provision even more resources as a safeguard. That’s an expensive fix. The better approach? Smarter workload balancing, real-time monitoring, and cost-efficient GPU scaling.
Strategic cost optimization can yield big savings
The companies that win in the cloud space aren’t the ones who chase discounts. They’re the ones who master cost optimization.
AWS spot-instance discounts can cut costs by up to 90%, but prices fluctuate an average of 197 times per month. That means businesses need an adaptive strategy, not a set-it-and-forget-it approach. Cast AI’s research shows that Azure AI customers can reduce GPU costs by 90% using Microsoft’s spot instance pricing, while AWS and Google Cloud offer savings of 67% and 66%, respectively. That’s a massive opportunity—but only for those who can dynamically shift workloads to the lowest-cost options.
Another overlooked strategy is workload placement. Moving workloads to lower-cost regions and availability zones can slash expenses by a factor of six. Smart organizations are already leveraging these approaches. The ones that aren’t will continue to pay the price.
“Executives should push for automated cost management, real-time workload optimization, and flexible cloud strategies. The future of cloud computing means using what you have, better.”
Key executive takeaways
- Kubernetes overprovisioning is draining cloud budgets: Enterprises are allocating far more cloud resources than needed, with Kubernetes clusters using just 10% of their CPU and under 25% of memory. Leaders should implement automated scaling and real-time monitoring to eliminate waste and optimize spending.
- Cloud costs keep rising despite competitive pricing: The cloud market grew 22% in 2024, yet enterprises still struggle with runaway costs due to inefficient consumption. Decision-makers must focus on consumption-based cost control rather than just securing vendor discounts.
- Savings programs can backfire without smart usage: Commitment-based cloud discounts can cut costs by up to 75%, but unused resources turn those savings into wasted spend. Executives should balance long-term contracts with flexible, on-demand capacity to avoid overcommitment.
- AI workloads are intensifying cloud cost pressures: AI applications demand high compute power, leading to resource imbalances that drive up costs and disrupt services. Leaders must optimize AI workload placement and leverage cost-efficient GPU scaling to prevent budget overruns.
- Smart cloud optimization unlocks massive savings: Spot-instance pricing and strategic workload placement can reduce cloud expenses by up to 90%. Executives should prioritize dynamic provisioning and automated cost management to stay agile while cutting unnecessary cloud spend.
