Overprovisioned AKS Clusters in Production: A $6,000 Lesson in Right-Sizing

"Why are we spending so much on our Kubernetes cluster?"

That question from our CFO kicked off a deep dive into our Azure Kubernetes Service (AKS) spending. We knew we needed AKS to handle our application's traffic, but the monthly bill was consistently higher than expected, adding an extra $6,000 drain on our budget.

It turned out our cluster was provisioned to handle peak traffic… 24/7. We were paying for resources we simply didn't need most of the time.

An abstract image depicting manual server adjustments, symbolized by gears and levers.

The Initial (and Ineffective) Attempts

Our first thought was to manually adjust the node pool sizes. We'd scale down during off-peak hours and back up during peak times. This was a tedious, error-prone process. We were constantly reacting, and sometimes we'd still miss the mark, leading to either performance issues or wasted resources.

The Utilization Revelation

EazyOps stepped in and performed a thorough analysis of our AKS cluster utilization patterns. Their platform revealed precisely how our resources were being consumed over time. The visual representation brought clarity: we had significant periods of low utilization, confirming our suspicions of overprovisioning.

A visualization of data analysis, represented by abstract lines and graphs, highlighting peaks and valleys.
An abstract image representing automated scaling, symbolized by interconnected nodes expanding and contracting.

Right-Sizing and Auto-Scaling: The EazyOps Solution

Based on the utilization data, EazyOps recommended right-sizing our node pools. They helped us determine the optimal baseline of resources needed to maintain performance. Even better, they implemented dynamic auto-scaling policies that automatically adjusted our node pool size based on real-time demand. This meant our AKS cluster would scale up during traffic spikes and seamlessly scale down during quieter periods.

The Results: Savings Without Sacrifice

The impact was immediate and impressive. We achieved a 45% reduction in our AKS costs, saving us that $6,000 per month without any performance degradation or downtime. The auto-scaling worked like a charm, ensuring we had the resources when needed and weren't paying for idle capacity when demand was low.

A conceptual image illustrating cost savings, using geometric shapes and a downward-sloping line to depict reduction.

Key Takeaways

  • Data-driven right-sizing is essential for efficient cloud spending.
  • Auto-scaling provides agility and cost control.
  • Continuous monitoring of AKS resource utilization is crucial for staying on top of costs.

The Future of AKS Cost Optimization

Looking ahead, we plan to further refine our auto-scaling policies and leverage spot instances for additional cost savings. With EazyOps' continuous monitoring and optimization recommendations, we are confident in our ability to maintain cost efficiency while scaling our AKS infrastructure to meet future demands.

About Shujat

Shujat is a Senior Backend Engineer at EazyOps, working at the intersection of performance engineering, cloud cost optimization, and AI infrastructure. He writes to share practical strategies for building efficient, intelligent systems.