Databricks Preview Releases

Databricks regularly provides previews to give you a chance to evaluate and provide feedback on features before they’re generally available (GA). These preview releases can come in various degrees of maturity, each of which is defined in this topic.

Platform preview releases

Databricks platform features can be released at any of the following preview levels:

Release type Who can use? Maturity Use in production Interface stable SLA Support Notes
Private Preview Invite only Low-Med No No No Engineering team Features are usually not documented in the public Databricks documentation.
Public Preview Everyone Med-High Yes Yes Yes Support team Features are documented in the public Databricks documentation. Public Preview features are stable and intended to advance to GA.
Limited Availability (LA) Invite only High Yes Yes Yes Support team LA features are rare, providing GA-level functionality to a limited set of customers. These features are not documented in the public Databricks documentation.

Databricks Runtime preview releases

Preview releases of Databricks Runtime are always labeled Beta.

Release type Who can use? Maturity Use in production Interface stable SLA Support
Beta Everyone Low-Med No No No Engineering team

Features included in a Databricks Runtime can be released at any of the following preview levels:

Release type Who can use? Maturity Use in production Interface stable SLA Support Notes
Private Preview Invite only Low-Med No No No Engineering team Features are usually not documented in the public Databricks documentation. The API may change.
Experimental Everyone Low-Med No No No Engineering team Features are documented in the public Databricks documentation. The API may change.
Public Preview Everyone Med-High Yes Yes Yes Support team Features are documented in the public Databricks documentation. The API will not change, either during the Public Preview or when the feature becomes GA.

For more information about Databricks Runtime releases, including support lifecycle and long-term-support (LTS), see Databricks Runtime Support Lifecycle.