Hi, @Tomas Peterek​  you can go through the MLflow guide: https://docs.databricks.com/mlflow/index.html

If I have understood it correctly, if you are running mlflow models in jobs runs in that case , each one of the jobs/job-runs gets a dedicated cluster that turns off right after the job finishes. It’s possible running a lot of clusters in parallel in order to execute many independent jobs. In a job cluster a single job run deploys a single cluster which cannot be shared. Please correct me if I am wrong.