Kubernetes has taken the software development world by storm. It gives you an excellent framework to deploy your application with and abstracts away the low-level details of the underlying infrastructure.
One of the most powerful features of Kubernetes is autoscaling, as it’s vital that we find the correct balance when scaling resources in our infrastructures. Scale up more than needed, and you will have unused resources which you must pay for.
Regardless of the infrastructure you are running, it is always important to keep an eye on your costs. There have been enough horror stories of cloud billing getting out of control that teams should have some measures in place to keep an eye on the usage of these resources to avoid surprises.
There is no denying the fact that Kubernetes has experienced widespread adoption in the last few years. Its automated deployment and scaling capabilities have made it easier and more convenient for developers to manage and develop advanced applications and services.
Multi-tenancy in Kubernetes can seem like an appealing solution to many problems. Maybe it’s to give your developers their own space and save costs by doing it inside a single cluster.
It can be challenging to manage costs if your developers use Kubernetes clusters running in the cloud, whether they use shared clusters or have their own dedicated clusters.
Virtual Kubernetes clusters are fully functional Kubernetes clusters that run within another Kubernetes cluster. The difference between a regular Kubernetes namespace and a virtual cluster is that a virtual cluster has its own separate Kubernetes control plane and storage backend.
Kubernetes is Kubernetes—what difference does it make which cloud provider you choose? Well, quite a lot, actually. While GKE, EKS, AKS, and DOKS all conform to CNCF Kubernetes Certification standards and are each valid choices, they each have their unique advantages and disadvantages, ranging from pricing to upgrades, to node repair.
When you are using Kubernetes at a larger scale and at different stages (development, testing, production), you will sooner or later face the question of how many clusters you should run.
Running Kubernetes can be very expensive, especially when it is done inefficiently. This is often the case when companies have just started to roll out Kubernetes in their organizations as then the same configuration and setup are often used that worked well for initial test projects or small applications.