I'm building a cluster using Raspberry Pi 5 devices, each with just 2GB of RAM, to keep energy use low. Initially, I'm going to set up Kubernetes the hard way to get a solid understanding of K8s since it seems like it has high resource demands. After that, I plan to switch to K3s to reduce resource usage for my homelab where I will be developing my own SaaS. I will also use VPS on Hetzner for my SaaS development environment, with 1 server for K3s and likely 2 agents since high availability isn't a requirement for the dev phase. For production, I aim for a highly available K3s setup with 2 or 3 servers and an appropriate number of agent nodes, depending on costs. Is the biggest drawback just that K3s isn't managed and I would have to handle all the management myself? I'm hoping it will be cheaper than using a service like EKS.
5 Answers
Running K3s on just 2GB of RAM might be pushing it! If you're focused on power efficiency, consider that Kubernetes might add too much overhead for your setup. Even though K3s is lighter than K8s, it still might be too heavy for your Raspberry Pi cluster.
Given your equipment constraints, you could also look into Nomad. It offers similar functionalities as K3s but may handle high availability better across your nodes.
Honestly, Kubernetes might not be the best fit for your use case. Check out lighter options if you're not keen on the Kubernetes setup. There are effective non-K8s solutions for self-hosting that might fit your project better.
For a startup or personal project, I’d recommend keeping it simple; maybe stick to a single-node K3s setup and automate your backups. However, if you're dealing with sensitive enterprise data, you'll need to ensure high availability, which K3s can support if set up correctly.
If your budget is tight and you're focusing on a hobby project, consider exploring alternatives like Railway or Fly.io. Kubernetes is generally more suited for larger organizations. For homelab projects, it's great for learning but not necessarily cost-effective for minimal scale deployments.
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