We're an ISV. Our product is a DLT pipelines, batch jobs, a Databricks App, and MLflow
models — is installed into each customer's own Databricks workspace as a dedicated
deployment, using Asset Bundles with one target per customer. We maintain the code
centrally; customers never receive the repo. We're packaging it as a wheel and running
it via python_wheel_task.
What we need is integrity, not secrecy. We accept that a workspace admin can read the
code. What matters is that the code we shipped is the code that runs — so that when
results are wrong it's our bug, and when we report outcomes, they're attributable to our
logic rather than to a local change.
How are others handling this when the deployment lives in a workspace the customer
administers? Interested in what has worked in practice, and where you decided the extra
effort stopped being worth it.