Model versions treat shared dbt models like versioned APIs

The extract clarifies three different uses of “version” in dbt docs, then focuses on model versions as a Mesh feature for tracking model change over time. It states the producer–consumer tension of API versioning and maps it onto shared final models: producers need to modify structure; consumers need queries that do not break without warning.

For data platform teams, the page’s claim is that model versioning is how you manage that cost tradeoff explicitly—planned migration instead of unplanned breakage—while accepting the problem cannot be erased. Considerations note that access, contracts, and versions add structure that can make rollbacks harder and raise maintenance if governance lands before models stabilize.

Shared meaning across systems depends on stable interfaces. Treating a published model like an API, with versioned change and deprecation paths, is how the extract connects Mesh governance to trustworthy cross-team references.