Service limits in Azure Machine Learning

This section lists basic limits and throttling thresholds in Azure Machine Learning.


Azure Machine Learning doesn't store or process your data outside of the region where you deploy.


Limit Value
Workspace name 2-32 characters


Limit Value
Name 256 characters
Description 5,000 characters
Number of tags 50
Length of tag key 250 characters
Length of tag value 1000 characters
Artifact location 1024 characters


Limit Value
Runs per workspace 10 million
RunId/ParentRunId 256 characters
DataContainerId 261 characters
DisplayName 256 characters
Description 5,000 characters
Number of properties 50
Length of property key 100 characters
Length of property value 1,000 characters
Number of tags 50
Length of tag key 100
Length of tag value 1,000 characters
CancelUri / CompleteUri / DiagnosticsUri 1,000 characters
Error message length 3,000 characters
Warning message length 300 characters
Number of input datasets 200
Number of output datasets 20

Custom environments

Limit Value
Number of files in Docker build context 100
Total files size in Docker build context 1 MB


Limit Value
Metric names per run 50
Metric rows per metric name 1 million
Columns per metric row 15
Metric column name length 255 characters
Metric column value length 255 characters
Metric rows per batch uploaded 250


If you are hitting the limit of metric names per run because you are formatting variables into the metric name, consider instead to use a row metric where one column is the variable value and the second column is the metric value.


Limit Value
Number of artifacts per run 10 million
Max length of artifact path 5,000 characters


Limit Value
Number of models per workspace 5 million model containers/versions (including previously deleted models)
Number of artifacts per model version 1,500 artifacts (files)

Limit increases

Some limits can be increased for individual workspaces. To learn how to increase these limits, see "Manage and increase quotas for resources"

Next steps