Ideas which I have is:

• periodically export/import mlflow models and experiments https://github.com/mlflow/mlflow-export-import#why-use-mlflow-export-import
• get metadata through API https://docs.databricks.com/dev-tools/api/latest/mlflow.html#operation/get-registered-model
• when you run your experiments in Databricks in notebooks, you can change the tracking server,
• I haven't heard about the availability to change the database server for registered models and experiments in managed Databricks Mlflow (although you can ask your databricks representative),
• It can change in the future. Recently it was announced that it is possible to use external feature stores https://docs.databricks.com/machine-learning/feature-store/online-feature-stores.html, so it could be nice to have similar options with models and experiments.