💵 Kubernetes Cost Optimization: Here’s How to Stop Wasting Millions

Kubernetes has revolutionized cloud-native infrastructure, but poor resource management is costing some companies millions in wasted cloud spending. According to Sysdig’s 2023 report, 69% of CPU resources and 49% of memory allocations in Kubernetes clusters are unused, while 59% of containers lack CPU limits entirely. If your team is overspending on idle resources, it’s time to meet KRR (Kubernetes Resource Recommender) – an open-source tool designed to slash costs without sacrificing performance.

The Kubernetes Cost Optimization Crisis

Kubernetes complexity often leads to overprovisioning, lack of visibility, and misconfigured limits. Here’s why it matters:

1. Wasted Resources: 69% of purchased CPU goes unused, and nearly half of containers run without memory limits.

2. Throttling & Evictions: Missing CPU limits cause performance throttling, while unset memory limits risk OOM crashes.

3. Cost Sprawl: Large enterprises waste millions on idle resources, with smaller teams overspending by 40% on average.

These issues stem from poor capacity planning and the “set it and forget it” mindset. But tools like KRR and OpenCost are changing the game.

KRR vs. OpenCost: Two Sides of Kubernetes Cost Optimization

In my previous post on OpenCost, I highlighted how this open-source tool provides real-time cost visibility by tracking Kubernetes spending across clusters, namespaces, and labels. While OpenCost answers “Where is my money going?”, KRR tackles the next critical question: “How do I reduce these costs?”

- OpenCost: Focuses on cost allocation, monitoring, and reporting (visibility layer).

- KRR: Focuses on actionable recommendations to rightsize resources (optimization layer).

Together, they form a powerful duo: Use OpenCost to identify wasteful workloads, then run KRR to optimize their resource requests/limits. For example, if OpenCost flags a namespace burning $5k/month on idle CPU, KRR can recommend slashing those allocations without impacting performance.

How KRR Optimizes Kubernetes Resources Usage

KRR

KRR Features

KRR automates resource rightsizing by analyzing historical usage data (latest 14 days) and recommending optimal CPU/memory requests and limits. Here’s why it’s a must-try:

- Avoid Overcommitment: Prevents Kubernetes from over-allocating resources, reducing throttling and eviction risks.

For example, if a container uses 300MB memory on average but requests 1GB, KRR flags it for optimization – potentially cutting costs by 70% per workload.

- Seamless Integration: Works with existing monitoring tools like Prometheus, Thanos, Victoria Metrics, enabling quick adoption.

- You can use it as k9s plugin !

Resource Limits Recommendation Algorithm

By default, KRR uses a simple strategy to calculate resource recommendations. It is calculated as follows:

• For CPU, it set a request at the 95th percentile with no limit. Meaning, in 95% of the cases, your CPU request will be sufficient. For the remaining 5%, it set no limit. This means your pod can burst and use any CPU available on the node - e.g. CPU that other pods requested but aren’t using right now.
• For memory, it takes the maximum value over the past week and add a 15% buffer.

3 Strategies to Maximize Savings with KRR

1. Rightsize Requests & Limits

KRR’s recommendations help enforce Guaranteed QoS by aligning requests with limits, ensuring critical pods aren’t evicted. Combine this with LimitRanges to auto-apply defaults.

2. Adopt Autoscaling

Pair KRR with horizontal autoscaling (HPA, Keda) to dynamically adjust pods based on demand. Sysdig found that optimized clusters reduce idle resources by 40% when combining rightsizing and autoscaling.

3. Enforce ResourceQuotas

Use KRR’s insights to set namespace-level quotas, preventing resource hogging in multi-tenant environments.

Why This Matters in 2025

With cloud costs rising and container lifetimes shrinking, real-time optimization is non-negotiable. Tools like KRR and OpenCost bridge the gap between FinOps and DevOps, turning cost control into a competitive advantage.

Let’s make every resource count. 💸