All-purpose clusters are solely for ad-hoc, interactive analysis with no capability for production jobs, and Databricks Runtime versions are automatically selected by the platform without user intervention.

All-purpose clusters support collaborative, interactive data processing and analysis across multiple users and notebooks, whereas job clusters are ephemeral, created for specific job executions, and terminate post-completion. Users have the flexibility to manually select the Databricks Runtime version for both cluster types to ensure compatibility and meet specific workload requirements.

Job clusters are designed for both development and production environments, offering more flexibility than all-purpose clusters, which are restricted to interactive analysis. Databricks Runtime versions are automatically updated to the latest version, with no option for manual selection.

There is no functional difference between all-purpose clusters and job clusters; however, job clusters provide an option to manually select the Databricks Runtime version, while all-purpose clusters are limited to automatic version updates.