When we first started using Kubernetes, I was excited. It promised everything a modern engineering team could want: automation, scalability, and flexibility. We set up our first production cluster, moved services one by one, and waited for the magic to happen.

The deployment worked well. The team was happy. Everything looked great until the first monthly cloud bill arrived. It was nearly double what we expected.

That moment was a turning point. I realized Kubernetes doesn’t make your infrastructure smarter by itself. It simply gives you more control, and with more control comes more room for mistakes. Over the years, we have learned where costs quietly grow and how small choices can make a big difference.

Here are ten Kubernetes mistakes that I have seen repeatedly, and how we learned to fix them.

Top 10 Kubernetes Mistakes And How to Avoid Them

Read below as I cover each of the ten key mistakes that I have seen many teams repeating, and how to manage them.

1. Giving More Resources Than Needed

When you’re unsure, it’s tempting to assign extra CPU and memory to every service. It feels safe. But in Kubernetes, unused resources are still reserved, which means you pay for them even if nothing is running.

We learned to start small and observe actual usage. DevOps tools like Prometheus or Metrics Server help track real consumption. Once we saw how much each pod truly needed, we adjusted the limits and saved a significant amount without affecting performance.

2. Keeping Too Many Nodes Active

Our first cluster had twice the number of nodes it actually needed. We added more during peak testing and never scaled back down. That mistake cost us thousands over time.

Now, we review node usage regularly. Kubernetes Cluster Autoscaler helps add or remove nodes automatically based on workload. We also check idle nodes every week. It sounds simple, but those small audits make a big difference.

3. Forgetting About Old Pods and Namespaces

I still remember the first time we looked into our cluster after a few months. There were old deployments, test namespaces, and leftover containers from experiments. Each one used resources silently.

As a leading DevOps consulting company, we have established a simple rule: if a namespace has no owner, it will be deleted. We also added cleanup scripts that remove unused pods and persistent volumes. It not only reduced costs but also made the cluster easier to manage.

4. Storing Everything in Persistent Volumes

At one point, we used persistent volumes for almost every service, including temporary ones. Over time, those volumes filled with logs, test data, and backups that nobody needed.

Now, we use storage only where it’s truly required. Logs go to external systems like CloudWatch or ELK, and short-term data stays in memory or gets cleared after a set period. That small change reduced storage costs by more than half.

5. Poor Autoscaling Rules

Autoscaling can be both a blessing and a problem. We once configured our cluster to scale aggressively during traffic spikes. The result was that it created new nodes for very short bursts of traffic, which increased the cost without adding much benefit.

We refined our autoscaling settings by increasing the cooldown time and setting realistic CPU thresholds. Kubernetes can handle growth, but it needs proper instructions on when to act.

6. Running Too Many Replicas

In the beginning, we gave every microservice multiple replicas, thinking it would increase reliability. But not every service needs that level of redundancy. Some could easily run with one or two replicas without any noticeable difference.

Now, we decide replica counts based on service importance. Business-critical services get higher availability, while others stay minimal. This balance keeps costs under control and performance consistent.

7. Ignoring Network Costs

For a long time, we treated internal network traffic as free. We later discovered that cross-zone or cross-region communication adds up quickly. Some services were calling APIs across regions unnecessarily, creating invisible expenses.

To fix this, we started grouping services that communicate frequently within the same zone. We also moved heavy data transfers to cheaper paths. The savings were immediate.

8. Using Only On-Demand Instances

Running everything on on-demand instances is the easiest setup but also the most expensive one. We learned this after comparing the costs with reserved and spot instances.

Now, we mix instance types. Critical workloads run on reserved nodes for stability, while flexible tasks use spot instances. This simple mix reduced our compute costs significantly without affecting performance.

9. No Visibility into Costs

In the early days, we had no idea which service or namespace was driving costs. The finance team only saw the total bill. That lack of visibility delayed our ability to fix anything.

Today, we use tools like Kubecost and AWS Cost Explorer to break down usage by team and service. Once developers saw the numbers tied to their workloads, optimization became a shared responsibility instead of just a DevOps concern.

10. Large Container Images

This one surprised us the most. Some of our container images were several gigabytes in size because of unused dependencies and heavy libraries. Each deployment took longer to pull and started more slowly.

We cleaned up the images by using smaller base images, removing unnecessary tools, and organizing layers properly. The results were faster deployments, lower bandwidth usage, and smaller bills.

Final Thoughts

Every one of these mistakes taught us something about balance. Kubernetes gives flexibility, but it also requires attention. When we started tracking, reviewing, and optimizing regularly, our costs came under control. More importantly, our clusters became faster and easier to manage.

Managing Kubernetes efficiently is not about cutting corners. It’s about making smart choices and reviewing them often. Costs are a reflection of how well your clusters are tuned.

At Bacancy, we help teams design, optimize, and manage Kubernetes environments that run efficiently and scale smartly. Our Kubernetes managed services focus on balancing performance with cost, so your infrastructure works for you, not against you.

If your cloud bills are growing faster than your workloads in 2025, it might be time to take a closer look at your Kubernetes setup. Sometimes, the fixes are much simpler than they seem, and having experts by your side is just gonna make your life easier.