When you spin up a Kubernetes cluster, whether on a few Raspberry Pis, your laptop, or a managed service like Amazon EKS, you’re not automatically production-ready. Most clusters start dangerously underprepared for the real-world chaos of internet traffic, security threats, and scaling demands. That’s the hard truth.

So, what does production-ready mean? It’s more than just running pods—it means your cluster can confidently handle unpredictable workloads, resist attacks, recover from failure, and still deliver a seamless user experience. In short, it’s battle-tested and resilient.

In this article, I’ll walk you through the bare minimum tools and configurations every Kubernetes cluster needs before it faces the world. To make this checklist more actionable, we’ll map it against the five pillars of the AWS Well-Architected Framework, covering critical areas like security, reliability, performance, and cost. These aren’t optional—they’re your survival kit for running Kubernetes in production.

Let’s dive in.

The Categories

The categories for the different tools are as follows:

• Reliability and Availability
• Security
• Network, Monitoring & Observability
• Backup/Recovery
• Cost Optimization
• Cluster Visualization

Reliability and Availability

These two terms mean different things; reliability means that a system is doing what is designed to do effectively, while availability is when a system is accessible by the user. These two are very necessary for your applications in your Kubernetes cluster.

Some tools can help your applications be highly available.

Tools

• Horizontal Pod Autoscaler (HPA)
• Karpenter (https://github.com/aws/karpenter).
• Cluster-Autoscaler (https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler).
• Goldilocks (https://github.com/FairwindsOps/goldilocks).

While HPA is for scaling pods based on CPU and Memory metrics, Karpenter and Cluster-Autoscaler scale nodes are based on the pod capacity of the nodes. Goldilocks helps you recommend the appropriate CPU and Memory metrics for your pods based on how much has been consumed over a period of time.

Security

The security of your cluster is a very essential part of running workloads in the Kubernetes cluster. Security in Kubernetes is twofold. First is the security from an external attack on your API server, which is usually secured with certificates, but an extra layer of putting the API server in a private network reduces the surface area of attack because it is not exposed to the public internet.

The other security operations have to do with scanning your cluster for proper configurations to ensure the pods running follow proper security standards. Here are some security tools that can help scan container-based workloads and Kubernetes clusters.

Tools

• Snyk (https://snyk.io)
• Kubelinter (https://github.com/stackrox/kube-linter)
• Kubehunter (https://github.com/aquasecurity/kube-hunter)
• Checkov (https://checkov.io)
• Kube-Scan (https://github.com/octarinesec/kube-scan)

Network, Monitoring & Observability

What is a Kubernetes cluster without its network? The network is used to send and receive traffic within the cluster and outside the cluster. It is important to be able to sketch a flow of traffic within the cluster. It makes it easy to identify bottlenecks, like; slow or unresponsive microservices. This is where a service mesh comes into play.

ServiceMesh Tools

• Istio (https://istio.io)
• Linkrd (https://linkrd.io)
• AWS AppMesh (https://aws.amazon.com/app-mesh)
• Consul (https://www.consul.io/)

Monitoring & Observability
The availability and Mean Time to Recover (MTTR) of your system are highly proportional to the depth of monitoring and observability in the system. Monitoring helps to collect logs and metrics of worker nodes and pods/applications running within the cluster. To know more about logs and metrics, get my book on Amazon CloudWatch. There are many monitoring tools out there. But I will pick about three of them that stand out in different scenarios. Observability on the other end is a combination of Application Performance Management (APM), bug tracing, and application tracing, with the focus on knowing what is going on within the application.

Monitoring Tools/Services

• Prometheus (prometheus.io) — for collecting metrics.
• Grafana (grafana.net) — for data visualization of data from different sources.
• Amazon CloudWatch Container Insights.
• Netdata (https://netdata.cloud).

Observability Tools

• Amazon X-Ray (https://aws.amazon.com/xray/).
• Datadog (https://datadog.com).
• AWS CloudMap (https://aws.amazon.com/cloud-map/).
• Jaegar (https://www.jaegertracing.io/).

Backup/Recovery

Backing up all components of your cluster can be a lifesaver in times of disaster or during a migration process. Deployments, Pods, Secrets, ConfigMaps, ClusterRole, ClusterRoleBinding, and all other resources in Kubernetes. The backup of the cluster is stored as a zip file in any of the popular object storage services such as Amazon S3, Azure Blob Storage, or Google Cloud Storage. This data can also be retrieved at any time or to an identical cluster with a single command, and all resources backed up will be automatically restored. It can also be configured to run a periodic backup of the cluster resources.

Tool(s)

• Velero (https://velero.io)

Cost Optimization

Tool(s)

• Kubecost (https://www.kubecost.com/)

Cluster Visualization

Tool(s)

• Lens (https://k8slens.dev/)
• Rancher (https://rancher.com)

Conclusion

There is no perfect Kubernetes cluster; the tools mentioned here are suggestions of tools that you should have in your cluster to get the best out of it. At least one tool from the five sections mentioned is needed to have proper visibility and governance of your Kubernetes cluster.