Kubernetes Pitfalls and How to Avoid Them

Kubernetes is now the standard for modern container orchestration. Companies benefit from scalability, automation, and a high degree of flexibility. But it is precisely this complexity that often leads to common operational errors. Many of these problems aren’t caused by Kubernetes itself, but by incorrect configurations, a lack of best practices, or insufficient monitoring. Anyone who’s ever stared at a crashing pod at three in the morning knows: with flexibility comes a fair share of pitfalls. While many teams get off to a good start, they eventually run into the same roadblocks—not because they’re bad developers, but because Kubernetes hides many small pitfalls in its depths. In this article, we’ll examine the most common Kubernetes pitfalls, explain why they occur, and show you exactly how to avoid them.

Why Kubernetes Issues Often Don’t Become Apparent Until They’re in Production

A Kubernetes cluster often works without any problems in test environments. However, typical weaknesses become apparent under real-world load:

• Pods are restarting
• Applications are slow to respond
• Storage is running out
• Deployments fail
• Logs Disappear
• Resources are being allocated incorrectly

Especially in production environments, even minor configuration errors can quickly lead to high costs or outages. That is why clear standards and monitoring are essential.

Common Pitfalls in Kubernetes

No resource limits set

The Trap:

The pod specifications do not list CPU and memory requirements. This is typically because Kubernetes does not require these fields, and workloads can often start and run without them. As a result, this omission is easily overlooked during early configurations or rapid deployment cycles.

In Kubernetes, however, resource requirements and limits are crucial for efficient cluster management. Resource requirements ensure that the scheduler reserves the appropriate amount of CPU and memory for each pod. This ensures that the pod has the resources it needs to run. Resource limits restrict the amount of CPU and memory a pod can use. This prevents a single pod from consuming an excessive amount of resources, which could result in other pods being underserved.

What happens:

A poorly optimized pod drains the node’s resources. Other pods are redistributed or stop responding. The cluster begins to stutter.

This leads to:

• “Noisy Neighbor” problems
• OOMKills (Out of Memory)
• unstable nodes
• difficulty in planning

Here’s how to avoid it:

Set requests (what each container needs) and limits (the maximum it can receive) for each container. Tip: Start with generous values, monitor them with Prometheus or Grafana, and then adjust them.

Incorrectly configured liveness and readiness probes

The risk is that containers are deployed without explicitly specifying how Kubernetes should check their status or operational readiness. This often happens because, by default, Kubernetes classifies a container as “running” as long as the process inside it has not been terminated. Without additional signals, Kubernetes assumes that the application is functioning—even if it is unresponsive, in the process of initializing, or has hung.

Liveness, readiness, and startup probes are mechanisms that Kubernetes uses to monitor the status and availability of containers.

Liveness probes determine whether the application is still active. If a liveness check fails, the container is restarted.

Readiness probes check whether a container is ready to handle traffic. Until the readiness check is passed, the container is removed from the service endpoints.

Start-up tests help distinguish between long startup times and actual failures.

Common Mistakes

• Timeouts that are too aggressive
• incorrect endpoints
• No start delay
• identical liveness and readiness probes

What happens: Endless restart loops, CrashLoopBackOff, or worse: Requests end up at a partially initialized service and fail silently.

Here’s how to avoid it: Clearly separate “liveness” from “readiness.” Keep the check simple. Overly complex checks can cause false positives and unnecessary restarts. Use a readiness check to ensure that traffic doesn’t reach your app until it has booted up.

Logs are not collected centrally

Kubernetes automatically rotates logs. If you’re only familiar with `kubectl logs`, you’ll lose exactly the logs you need in an emergency because the pod has already been restarted.

Consequences

• Errors cannot be traced
• Debugging becomes difficult
• Compliance requirements are being violated

What happens: You have an error, the pod is gone, the logs are gone. Debugging turns into detective work with no clues.

Here’s how to avoid it:

Use a centralized logging stack. Popular options include the EFK stack (Elasticsearch, Fluentd, Kibana) or Loki with Grafana. NWS offers Managed Prometheus & Grafana, which lets you easily combine logs and metrics—without having to operate a logging stack yourself.

Best Practices

Use centralized logging solutions such as:

• Elasticsearch + Kibana
• Loki + Grafana
• OpenSearch
• Fluent Bit or Fluentd

Important Rules

• Store logs locally only temporarily and aggregate them centrally
• Use structured JSON logs
• Define Log Retention
• mask sensitive data

Poorly Planned Storage – PVCs Without a Strategy

Persistent storage in Kubernetes is not a simple topic. Many teams create PersistentVolumeClaims (PVCs) on an ad hoc basis without giving much thought to StorageClasses, access modes, or backup strategies.

Common Mistakes

• Incorrect StorageClasses
• missing backups
• inappropriate access methods
• No monitoring of storage usage

What happens: A PVC with `accessMode: ReadWriteOnce` can only be mounted by one node. If a node crashes and migration isn’t possible, only the pod is migrated, not the PVC. Or worse: Data is lost because no one has set up a backup.

Here’s how to avoid it:

Plan for storage from the very beginning:

• Choose the appropriate StorageClass for your use case (SSD vs. HDD, local vs. network-based)
• Be sure to check the accessMode: ReadWriteMany for shared volumes, ReadWriteOnce for single-node
• Use snapshots and automated backups

A Careless Approach to Security and RBAC

The Trap:

Deploying workloads with insecure configurations, such as running containers as the root user, using mutable image tags, disabling security contexts, or assigning overly broad RBAC roles such as “cluster-admin.” These practices persist because Kubernetes enforces few security policies by default, and the platform is designed for flexibility rather than rigid constraints. Without explicit security policies, clusters are exposed to risks such as container escapes, unauthorized privilege escalation, or accidental production changes due to unpatched images.

Risk

A compromised container can:

• Read “Secrets”
• Modify deployments
• Manipulate Clusters

Here’s how to avoid it:

Use RBAC to define roles and permissions within Kubernetes. For more complex or external policy requirements, consider solutions such as OPA Gatekeeper or custom webhooks using policy languages like CEL.

Best Practices

Use consistently:

• RBAC
• Least Privilege Principle

Grant only the exact permissions that are needed.

In addition:

• separate service accounts
• Do not use default accounts
• conduct regular audits

No namespace concept

A single namespace for everything—development, staging, production—sounds simple at first. It is. And that’s the problem.

Problems

• lack of separation
• Security Risks
• unorganized resources

Conflicts During Deployments

What happens: A developer accidentally deletes a deployment in the wrong namespace.

Here’s how to avoid it:

Use namespaces consistently to organize your code. No service account should have more permissions than it needs:

The principle of least privilege applies in Kubernetes just as it does everywhere else in IT.

Best Practices

Namespaces by:

• Teams
• Applications
• Environments

organize.

No Pod Disruption Budgets Defined

Kubernetes can drain nodes for maintenance or upgrades. Without Pod Disruption Budgets (PDBs), it does so aggressively, and your application suddenly goes offline.

What happens: Cluster upgrade, node drain, all pods in a deployment are terminated simultaneously. Outage.

Here’s how to avoid it: Define a PodDisruptionBudget. This specifies how many pods of an application (e.g., as a minimum number) must always be running simultaneously during planned disruptions (such as Node Drain).

Images Without Versioning—The “Latest” Curse

The Trap:

For container images, :latest is used by default. Kubernetes may fetch a new version without your knowledge (depending on the imagePullPolicy). A rollout involving pod restarts can therefore lead to unexpected breaking changes.

What happens: Two nodes pull “latest” at different times and are running different versions. Rollback? Which image was it yesterday, anyway?

Here’s how to avoid it: Always use specific image tags, ideally including a Git commit hash, semantic versioning, or the checksum set by the registry. This makes deployments reproducible and rollbacks manageable.

ConfigMaps and Secrets mounted directly in the deployment and never rotated

Configuration data and secrets in Kubernetes are often set up once and then forgotten. This is both a security risk and an operational issue.

What happens: Secrets never rotate, so an old database password remains in production for years. Or: A ConfigMap changes, but the pods don’t notice because no rollout is triggered.

Here’s how to avoid it:

• Use external secret managers such as Vault or the Kubernetes External Secrets Operator
• For ConfigMap changes: Explicitly trigger a rollout using `kubectl rollout restart deployment`
• Plan for regular Secret rotations from the very beginning

No monitoring and no alerts

“The cluster is running fine”—until it isn’t. Without monitoring, you’re reacting instead of taking a proactive approach.

Common Mistake

Do not implement monitoring until after the go-live.

Monitoring has been part of the platform from the very beginning.

Key Performance Indicators

The following should be monitored:

• CPU
• RAM
• Network
• Pod Restarts
• API Server Latency
• Node Status
• Storage Utilization

What happens: A storage volume is filling up, a node has been degraded for hours, and a deployment has had an increased error rate since the last rollout. No one knows about it until a customer calls.

Here’s how to avoid it:

The go-to solution in the Kubernetes environment is the Prometheus + Grafana stack. It lets you collect metrics from the cluster, build dashboards, and set up alerts. To get started, you can use pre-built Helm charts like kube-prometheus-stack, which come with useful default alerts right out of the box.

NWS offers Managed Prometheus & Grafana as a turnkey service—here’s how you can use monitoring without having to manage it yourself.

Recommended Tools

• Prometheus
• Grafana
• Alert manager

Lack of a Backup Strategy

Many companies mistakenly assume:
“Kubernetes is highly available, so we don’t need backups.”

What Needs to Be Secured

• etcd
• Persistent Volumes
• Configurations
• Secrets
• Helmet Releases

Important

Test your backups regularly.

An untested backup isn’t a real backup.

Never tested the cluster—forgot about chaos engineering

You’ve set everything up properly, configured probes, defined limits, and monitoring is running. But do you really know how your cluster will behave if a node fails? Or if a pod crashes?

What happens: The first real failure is also the first test. It rarely goes well.

Here’s how to avoid it:

Conduct regular, controlled chaos tests in non-production environments. Tools like Chaos Monkey, Litmus Chaos, or simply manually draining nodes can help you understand how your cluster behaves under stress before a real-world emergency occurs.

Treat development and production exactly the same

The Trap:

Deploying the same Kubernetes manifests with identical settings across development, staging, and production environments. This often happens when teams prioritize consistency and reusability but overlook the fact that environment-specific factors can vary significantly. Without customization, configurations optimized for one environment can lead to instability, poor performance, or security vulnerabilities in another.

Here’s how to avoid that:

Use Environment Overlays or Kustomize to maintain a common baseline while customizing resource requirements, replicas, or configurations for each environment. Store environment-specific configurations in ConfigMaps and/or Secrets. To manage sensitive data, you can use specialized tools such as “Sealed Secrets.”

Checklist for Stable Kubernetes Clusters

Infrastructure

• Resource limits set
• Autoscaling Configured
• Storage is being monitored

Security

• RBAC enabled
• Network Policies Defined
• Scanned images

Operation

• Monitoring in place
• Centralized Logging
• Backups Tested

Deployments

• Rollbacks are possible
• CI/CD integrated
• GitOps Implemented

Conclusion: Kubernetes pitfalls are instructive, but avoidable

Most of these Kubernetes pitfalls don’t just appear out of nowhere. They arise when clusters grow rapidly, teams are under time pressure, or best practices get lost in the hustle and bustle of day-to-day operations. The good news is: if you’re aware of them, you can avoid them from the start. Resource limits, clean probes, centralized logging, and a well-thought-out storage strategy aren’t luxury features—they’re the foundation for a Kubernetes operation that lets you sleep soundly at night.

If you’re not sure whether your cluster is built on a solid foundation, a managed Kubernetes cluster like NETWAYS Managed Kubernetes® is a good option: auto-healing, auto-scaling, and a highly available architecture are already built in—so you can focus on your applications.

Which of these pitfalls have you fallen into before? Feel free to leave a comment or share this article with someone who’s just setting up their first Kubernetes cluster.