It depends what you're looking for. One attraction of k3s is you can get a "real" production-grade K8s running on a single machine, or a small cluster, very qu

It depends what you're looking for. One attraction of k3s is you can get a "real" production-grade K8s running on a single machine, or a small cluster, very quickly and easily. That can be great for learning, development and testing, certainly. K3s is actually targeted at "edge" scenarios where you aren't running in a cloud, and don't have the ability and/or desire to dynamically adjust the cluster size. You can scale a k3s cluster easily enough manually, adding or deleting nodes, but to get cluster autoscaling you'd need to do environment-dependent work to make it automatic. If you do want that, then things will likely be quite a bit easier with a managed cluster like Google's GKE, or even possibly an self-installed cluster using a tool like kops, kubespray etc. (It's a while since I've used any of those, so not sure which is best.) AWS EKS is another choice, although its setup is a bit more complex and I wouldn't really recommend it to someone getting started. And for production scenarios, integration with cloud load balancers, network environment etc. is similarly going to be easier with a provider-managed cluster. It's all possible to do with k3s, but it's more work and a steeper learning curve.