Databricks provides a managed version of the MLflow tracking server and the Model Registry, which host the MLflow REST API. You can invoke the MLflow REST API using URLs of the form


replacing <databricks-instance> with the <account> domain name of your Databricks deployment.

MLflow compatibility matrix lists the MLflow release packaged in each Databricks Runtime version and a link to the respective documentation.

Rate limits

The MLflow APIs are rate limited as four groups, based on their function and maximum throughput. The following is the list of API groups and their respective limits in qps (queries per second):

  • Low throughput experiment management (list, update, delete, restore): 7 qps
  • Search runs: 7 qps
  • Log batch: 47 qps
  • All other APIs: 127 qps

If the rate limit is reached, subsequent API calls will return status code 429. All MLflow clients (including the UI) automatically retry 429s with an exponential backoff.