Despite years of efforts to address Kubernetes optimization, underutilized workloads continue to drain cloud budgets in 2025. Our team has met with dozens of platform engineering, DevOps, and FinOps groups, and we see these persistent challenges:

Human & Organizational Factors

Risk Aversion

"The SRE never makes the change because they don't want to be the guy that brings down the application."

Perceived risks often outweigh potential savings.

Resource Priority

"All our DevOps is busy with feature requests and customer requests right now."

"We are okay having extra CPUs not trying to optimize."

When teams are under pressure to deliver features, optimization frequently gets deprioritized.

Technical Constraints

Workload Pattern Complexity

"They bring people together to evaluate what's going on and then decide whether or not it's temporary or if it's something that's going to be happening for a while. And then they add more pods."

Dynamic workloads create significant challenges for predictive resource allocation, often leading to conservative overprovisioning.

Technical Requirements

"They run an app and even if it doesn't look like they need so much memory or CPU, they need a certain amount just to start. Whether they need that after they start or not is a different issue."

Many applications have specific resource requirements that complicate optimization, particularly at startup time.

Infrastructure Evolution

"We bought a bunch of other companies with different technology and so integrating all of that technology together is a bit of a challenge."

Many enterprises maintain heterogeneous environments resulting from mergers, acquisitions, and organic growth, creating additional complexity for optimization efforts.

Solution Challenges

Lack of Trusted Automation

"I've looked at a lot of these automation tools and I've been disappointed."

Teams require solutions they can trust not to impact production performance while continuously optimizing resource allocation.

Across the customers we work with, we typically see 30-40% of Kubernetes resources sitting idle, representing a substantial opportunity for optimization. However, the challenges above explain why this problem persists year after year.

At Sedai, we've developed a fundamentally different approach. Rather than requiring teams to divert resources from strategic initiatives, our autonomous platform continuously learns workload patterns and safely implements optimizations - respecting both application-specific requirements and performance boundaries.

A recent customer achieved a 47% reduction in Kubernetes costs with zero performance impact.

Will 2025 be the year your organization finally puts an end to underutilized Kubernetes workloads? Contact us to learn how.

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