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Kind vs K3s

Kind and K3s are Kubernetes tools that leverage Docker containers to provide flexible and scalable Kubernetes distributions compared to their competitors. This article highlights the feature of both tools and the subtle difference between them.

Running the standard k8s clusters in local environments requires much operational effort and system resources. This is why Developers, DevOps engineers, and other professionals who, for reasons such as development, testing, or learning, prefer to run Kubernetes local refer to tools and distributions built for this purpose. In the previous series, we’ve compared many of such tools, including microK8s and k3s.

This article highlights and compares two other reliable tools, kind and k3d, to help you run lightweight Kubernetes in local and remote environments.


Kind (Kubernetes in Docker) is a CNCF certified project that installs highly available Kubernetes clusters. As its name suggests, kind spins up k8s clusters in Docker containers called nodes. This results in faster Kubernetes set up compared to VM-based Kubernetes like minikube and microk8s.

It is a tool initially designed for testing Kubernetes but has established itself as a suitable option for running Kubernetes clusters in local environments and CI pipelines.

Using kind, you can multiple Kubernetes clusters with more efficiency and speed compared to VM-based Kubernetes.

One of the unique features of kind is that it allows you to load your local container images into the local Kubernetes cluster, saving you time and effort to set up a registry and push the images repeatedly.

It provides simple commands like kind create cluster to spin up a kind cluster and so on.

Say there is a new version of Kubernetes, you can use kind to test it locally before deploying to ensure that it doesn’t break anything in the production environment. You can create a kind cluster using the version of Kubernetes you want to test and see if it doesn’t conflict with your other Kubernetes logging, monitoring, and management tools before deploying it in the production environment.


Like kind, k3d set up local Kubernetes clusters inside Docker containers. However, k3d implements instead of k8s in kind's case. k3s is a VM-based, lightweight Kubernetes distribution developed by Rancher that allows you to run Kubernetes on local and low-resourced environments.

k3d is a wrapper that allows you to create faster and highly available k3s clusters in Docker containers. k3d covers many of the shortcomings of k3s like speed, difficulty in creating multiple clusters, and scalability. k3d allows you to easily create single and multi-node k3s clusters for seamless local development and testing of Kubernetes applications while enabling easy scaling of workloads. It also provides simple commands that ease the management.

kind vs k3s: what is the difference

kind leverages container runtimes to provide flexible k8s clusters for use in local machines, likewise k3d.

Kind offers support for various features such as multi-node clusters, building Kubernetes release builds from its source, loading images directly into the cluster without taking the stress configuring a registry, and support for Linux, macOS, and Windows operating systems. k3d also offers various features, including hot reload of code, building deploying and testing Kubernetes applications using Tilt, and full cluster lifecycle for simple and multi-server clusters.

Both solutions are lightweight, fast, and easily scalable, which are some of the most important features when searching for a local Kubernetes distribution.

However, the difference between the two is that kind implements containerized k8s clusters while k3d implements containerized k3d clusters.

Also, kind is more suitable for running Kubernetes clusters on local machines and also suitable for production environment through CI pipelines. k3d, on the other hand, is ideal for use in small settings like Raspberry Pi, IoT, and Edge devices in addition to local computers.

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Aymen El Amri

Founder, FAUN

Founder of FAUN, author, maker, trainer, and polymath software engineer (DevOps, CloudNative, CloudComputing, Python, NLP)
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