helm VS k3d

Frappe Cloud is by far the easiest way to run an ERPNext instance of you're okay with shared hosting. It's a Frappe framework-based app hosting service from the developers of ERPNext, and it supports data file imports from other instances of ERPNext:

- Frappe Cloud: https://frappecloud.com

In addition to the Frappe Bench setup process, Frappe procides instructions dfor swlf-hosting ERPNext with Docker and Kubernetes:

- Docker: https://github.com/frappe/frappe_docker

- Kubernetes: https://helm.erpnext.com

There's also a third-party Docker image from pipech that's easier to setup, but it's not always up to date:

- Third-party Docker (pipech): https://hub.docker.com/r/pipech/erpnext-docker-debian

Bitnami packages ERPNext in single-tier (AWS, Azure, or GCP) and virtual machine (local computer) offerings:

https://bitnami.com/stack/erpnext

To anyone reading this, if you onpy need software for accountind and not an entire enterprise resource planning (ERP) suite, do yourself a favor and follow the advice in TFA to use GnuCash.

Despite the ERPNext helm chart creating a PVC, we actually want to avoid using their PVC because the accessMode is hardcoded to RWX - ReadWriteMany.

K3D: is a lightweight distribution of Kubernetes designed for resource-constrained environments. It is an excellent option for running Kubernetes on virtual machines or cloud servers.

k3d is a lightweight wrapper that makes running Kubernetes (specifically, the lightweight k3s distribution) in Docker straightforward and efficient. It's designed to provide developers with a quick and easy way to test Kubernetes without the overhead of setting up a full cluster.

If you customer prefers to run the standard docker engine you could use k3d

You don’t need to run Rancher from a Kubernetes cluster, the rancher/rancher image works fine with Docker (it uses k3d, aka « k3s in docker » : https://k3d.io/).

K3d and Skaffold for local development

If you are a developer and want to learn how to deploy applications to a cluster, getting a cluster up an running can be a daunting task in it's own rights. There are many ways to do it: spinning up local virtual machines and configuring from scratch or using tools like minikube, etc. You may not care for the pain of setting up and configuring a cluster, and if that is you, then the quickest way that I have found is using k3d.