AI models maintenance policy

This page describes the model maintenance policy for the Foundation Model APIs pay-per-token, Foundation Model APIs provisioned throughput, and Batch Inference with ai_query offerings.

To continue supporting the most state-of-the-art models, Databricks manages models through a lifecycle that progresses from legacy status to deprecation to eventual retirement.

• Legacy: When a model is marked legacy, it is no longer recommended for new workloads, but remains available in workspaces with existing usage of the model. Workspaces that are not using the model when it enters legacy status no longer have access to the model. Customers using such a model should evaluate newer models and consider migration.
• Deprecation: When a model is deprecated, a retirement date of 30 days or 90 days in the future is announced. It is not recommended for new workloads, but remains available in workspaces with existing usage of the model. Workspaces that are not using the model at the time of deprecation do not have access to the model. Customers using such a model should evaluate newer models and plan to migrate their workloads before the retirement date.
• Retirement: When a model is retired, it is no longer accessible and support for the model is fully discontinued. Any workload using the model no longer works.

Model lifecycle policy

The following section summarizes the model lifecycle policy. See Deprecated models for a list of deprecated models and retirement dates.

Many factors are considered when determining the retirement timeline, including model popularity, suitable replacement model, and amount of production usage. Databricks announces retirement dates for models when they are deprecated, following the notification timelines outlined below.

Foundation Model APIs

The following table summarizes the model lifecycle policy for Foundation Model APIs products: pay-per-token, provisioned throughput, and ai_query (batch inference).

Partner model retirement policy

Partner models are models that third-party partners — specifically OpenAI, Anthropic, and Google — provide through Foundation Model APIs. For these partner models, Databricks generally follows the same deprecation timelines and policies described above.

However, partners might provide retirement dates shorter than the one-month transition period that Databricks publishes. In these cases, Databricks attempts to bridge the gap by temporarily redirecting models to a similar version, so customers receive the full transition time.

For example, if a partner model retirement is announced with two weeks' lead time instead of one month, Databricks redirects the model for an additional two weeks to prevent immediate breakage and allow time for migration. Queries fail at the end of the full one-month period.

Deprecated and retired models

The following sections list models that are deprecated (no longer recommended for new workloads) or retired (end-of-life and no longer available). Retirement dates for deprecated models are announced at least one month in advance.

Foundation Model APIs retirements

The following table shows model retirements, their retirement dates, and recommended replacement models to use for Foundation Model APIs pay-per-token and provisioned throughput serving workloads. Databricks recommends that you migrate your applications to use replacement models before the indicated retirement date.

If you require long-term support for a specific model version, Databricks recommends using Foundation Model APIs provisioned throughput for your serving workloads.

Find workloads that use retired models

Use the following query to find workloads that are using deprecated models and identify their owners.

SELECT
   eu.requester,
   se.endpoint_name,
   se.entity_name,
   COUNT(*) AS request_count,
   SUM(eu.input_token_count) AS total_input_tokens,
   SUM(eu.output_token_count) AS total_output_tokens,
   MIN(eu.request_time) AS first_request,
   MAX(eu.request_time) AS last_request
 FROM system.serving.endpoint_usage eu
 JOIN system.serving.served_entities se
   ON eu.served_entity_id = se.served_entity_id
 WHERE LOWER(se.entity_name) LIKE '%<retired-model-name>%'
 GROUP BY eu.requester, se.endpoint_name, se.entity_name
 ORDER BY request_count DESC