CreateEndpoint - Amazon SageMaker
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).


Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.

Use this API to deploy models using SageMaker hosting services.


You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.

The endpoint name must be unique within an Amazon Region in your Amazon account.

When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.


When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads, the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

When SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.

If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Security Token Service to download model artifacts from the S3 path you provided. Amazon STS is activated in your Amazon account by default. If you previously deactivated Amazon STS for a region, you need to reactivate Amazon STS for that region. For more information, see Activating and Deactivating Amazon STS in an Amazon Region in the Amazon Identity and Access Management User Guide.


To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.

  • Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.

  • Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:

    "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]

    "Resource": [




    For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.

Request Syntax

{ "DeploymentConfig": { "AutoRollbackConfiguration": { "Alarms": [ { "AlarmName": "string" } ] }, "BlueGreenUpdatePolicy": { "MaximumExecutionTimeoutInSeconds": number, "TerminationWaitInSeconds": number, "TrafficRoutingConfiguration": { "CanarySize": { "Type": "string", "Value": number }, "LinearStepSize": { "Type": "string", "Value": number }, "Type": "string", "WaitIntervalInSeconds": number } }, "RollingUpdatePolicy": { "MaximumBatchSize": { "Type": "string", "Value": number }, "MaximumExecutionTimeoutInSeconds": number, "RollbackMaximumBatchSize": { "Type": "string", "Value": number }, "WaitIntervalInSeconds": number } }, "EndpointConfigName": "string", "EndpointName": "string", "Tags": [ { "Key": "string", "Value": "string" } ] }

Request Parameters

For information about the parameters that are common to all actions, see Common Parameters.

The request accepts the following data in JSON format.


The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.

Type: DeploymentConfig object

Required: No


The name of an endpoint configuration. For more information, see CreateEndpointConfig.

Type: String

Length Constraints: Maximum length of 63.

Pattern: ^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}

Required: Yes


The name of the endpoint.The name must be unique within an Amazon Region in your Amazon account. The name is case-insensitive in CreateEndpoint, but the case is preserved and must be matched in InvokeEndpoint.

Type: String

Length Constraints: Maximum length of 63.

Pattern: ^[a-zA-Z0-9](-*[a-zA-Z0-9]){0,62}

Required: Yes


An array of key-value pairs. You can use tags to categorize your Amazon resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Resources.

Type: Array of Tag objects

Array Members: Minimum number of 0 items. Maximum number of 50 items.

Required: No

Response Syntax

{ "EndpointArn": "string" }

Response Elements

If the action is successful, the service sends back an HTTP 200 response.

The following data is returned in JSON format by the service.


The Amazon Resource Name (ARN) of the endpoint.

Type: String

Length Constraints: Minimum length of 20. Maximum length of 2048.

Pattern: arn:aws[a-z\-]*:sagemaker:[a-z0-9\-]*:[0-9]{12}:endpoint/.*


For information about the errors that are common to all actions, see Common Errors.


You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created.

HTTP Status Code: 400

See Also

For more information about using this API in one of the language-specific Amazon SDKs, see the following: