I have been looking into running flows using a KubernetesRun like so:

flow.run_config = KubernetesRun(
    job_template_path="my_custom_template.yaml"
)

vs configuring/running a flow this way (using KubernetesJobEnvironment)

flow = Flow(
    "flow_w_jobenv",
    environment=KubernetesJobEnvironment(job_spec_file="job_spec.yaml"),
    storage=Docker()
)

I'm struggling to understand the differences/ tradoffs of configuring a job one way vs another. Both ways you can pass in information that a job spec would need. Then you register the flow and run it and the k8s agent picks it up. Is there any fundamental difference I'm missing?

We're transitioning (slowly) from environment-based configuration to run-config based configuration. Run configs are the new way to do things, environments are the old way.

Thanks, I was getting that impression but that clears it up 🙂