K3s vs K3d

Working with Kubernetes in a local environment is not so easy. It requires several operational efforts to check code functionality, build and tag docker images and create and deploy configurations.

For developers and operation engineers, however, like to run Kubernetes locally allows them to test their Kubernetes applications and assess their performance before deploying to a production environment.

To achieve this, professionals rely on tools and distributions designed to deploy lightweight Kubernetes distributions on local machines. Two of such tools are k3s and k3d.

Professionals that are also just starting out with Kubernetes also find it easier by running it on their local system without incurring any overhead cost which may be the case running it on cloud platforms.

This article explains the capabilities of k3s and k3d and their subtle differences.

k3s

k3s is an efficient, lightweight tool designed to run production-level Kubernetes on local machines. It removes some unnecessary components such as legacy, alpha, and non-default features and replaces some of Kubernetes’ complex networking, load balancing, storage, and DNS server with lightweight alternatives to provide a minified version of Kubernetes that runs efficiently on local systems.

k3s is a CNCF certified Rancher project that offers a fully compliant, open-source Kubernetes distribution packaged in a binary less than 40MB. This makes it easy to install and runs a k3s cluster in local computers and low-resourced environments such as Raspberry Pi, IoT, and edge devices.

k3s clusters provide an environment that simulates the standard k8s, allowing you to test your knowledge of Kubernetes and your application similar to a production environment.

You can install it on your local computer via virtual machines like VirtualBox or VMWare. It is also optimized to run on ARM64 and ARMv7 environments.

It is one of the most widely used tools for a well-optimized, secure, and simple Kubernetes environment in low-resource devices.

You can install k3s through a simple script that will download and configure a Linux library and the k3s CLI.

k3d

k3d is an open-source utility designed to easily run highly available lightweight k3s clusters in a docker container. With k3d you can easily create single and multi-node k3s clusters for seamless local development and testing on Kubernetes. By running in Kubernetes, k3d also helps you to scale your workload up and down without more effort.

k3d wraps k3s, thereby implementing many of its features. It also offers additional features such as hot reload of code, building deploying and testing Kubernetes applications using Tilt, and full cluster lifecycle for simple and multi-server clusters.

k3d also enables you to easily manage your Docker-based k3s cluster through its powerful commands.

k3s vs k3d: What is the difference?

Both k3s and k3d are lightweight tools that allow you to deploy and run Kubernetes on your local machine with less operational effort compared to deploying k8s.

k3d is a wrapper of k3s but one of the apparent differences is that k3s deploys a virtual machine-based Kubernetes cluster while k3d deploys Docker-based k3s Kubernetes clusters.

Also, k3s does not provide prompt support for multiple clusters. To create multiple k3s clusters, you need to manually configure additional virtual machines or nodes. k3d on the other is specifically created to set up highly available, multiple k3s clusters without demanding more resources.

By leveraging Docker containers, k3d offers a more scalable version of k3s which might make it preferable to the standard k3s.

Another difference is in their use cases. Even though it is lightweight, k3s is designed to be easily deployable in production environments which makes it one of the most favorite options for simulating Kubernetes for production-level workloads in local environments. k3d, however, is more suitable for use in even smaller environments like Raspberry Pi, IoT, and Edge devices.

k3d appears to be a more flexible and improved version of k3s even though their features and usage are similar. Choosing one of the two will depend on your preferences, considering the facts we've provided so far.