I have been running a three-node Kubernetes cluster at home for about a year. The unglamorous take: it has been worth it, but not for the reasons I expected going in.
What I thought I would get: scalability, high availability, orchestration for my personal workloads.
What I actually got: a testbed for production-equivalent infrastructure patterns, practical familiarity with the Kubernetes operational surface, and a place to run experiments where the orchestration layer matches what I see in work contexts.
What I did not need: high availability. My personal workloads fail occasionally, and that is fine. I do not run an SLA-bound service at home. The HA story was oversold for my use case.
What surprised me: the operational overhead is non-trivial. Upgrades, certificate rotation, storage provisioning, network debugging. I spend about 2 hours a month on cluster maintenance. For a bigger lab this would pay for itself; for a small one it is a tax you pay to live in K8s-land.
Would I do it again? Yes, but I would probably start with k3s rather than full k8s. The resource overhead of a full control plane is heavier than I needed. k3s gives you most of the operational pattern with a fraction of the overhead.
The broader point: running a home cluster is about learning, not about the cluster. If the learning maps to your professional context, it pays for itself. If it does not, it is a hobby project, and that is also fine.
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