---
title: "POST /custom-models/create-custom-model"
method: POST
path: "/custom-models/create-custom-model"
---

# POST /custom-models/create-custom-model

`POST /custom-models/create-custom-model`

Creates a new custom model in Amazon Bedrock. After the model is active, you can use it for inference.

You can provide the model data source in one of the following ways:

*   `customModelDataSource` — Specify a SageMaker AI model package ARN. Amazon Bedrock resolves the model package to retrieve the model artifacts. This is the preferred method for new SageMaker AI training outputs.
    
*   `modelSourceConfig` — Specify an Amazon S3 URI pointing to the Amazon-managed Amazon S3 bucket containing your model artifacts.
    

To use the model for inference, you must purchase Provisioned Throughput for it. You can't use On-demand inference with these custom models. For more information about Provisioned Throughput, see [Provisioned Throughput](https://docs.aws.amazon.com/bedrock/latest/userguide/prov-throughput.html).

The model appears in `ListCustomModels` with a `customizationType` of `imported`. To track the status of the new model, you use the `GetCustomModel` API operation. The model can be in the following states:

*   `Creating` - Initial state during validation and registration
    
*   `Active` - Model is ready for use in inference
    
*   `Failed` - Creation process encountered an error
    

**Related APIs**

*   [GetCustomModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetCustomModel.html)
    
*   [ListCustomModels](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListCustomModels.html)
    
*   [DeleteCustomModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_DeleteCustomModel.html)

## Request body

- object
  - `modelName` string, required — A unique name for the custom model.
  - `modelSourceConfig` object — The data source of the model to import.
    - `s3DataSource` object — The Amazon S3 data source of the model to import.
      - `s3Uri` string, required — The URI of the Amazon S3 data source.
  - `customModelDataSource` object — <p>The data source for a custom model. This is a union type that supports the following member:</p> <ul> <li> <p> <code>modelPackageArnDataSource</code> — Specifies a SageMaker AI model package as the data source.</p> </li> </ul>
    - `modelPackageArnDataSource` object — A SageMaker AI model package ARN as the data source for the custom model. When you specify a model package ARN, Amazon Bedrock resolves the model package to retrieve the model artifacts.
      - `modelPackageArn` string, required — <p>The Amazon Resource Name (ARN) of the SageMaker AI model package. The ARN must be for a model package of <code>restricted</code> type.</p> <p>To use a model package ARN, you must have the <code>sagemaker:DescribeModelPackage</code> and <code>sagemaker:AccessModelPackageData</code> permissions on the model package resource.</p>
  - `modelKmsKeyArn` string — <p>The Amazon Resource Name (ARN) of the customer managed KMS key to encrypt the custom model. If you don't provide a KMS key, Amazon Bedrock uses an Amazon Web Services-managed KMS key to encrypt the model. </p> <p>If you provide a customer managed KMS key, your Amazon Bedrock service role must have permissions to use it. For more information see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/encryption-import-model.html">Encryption of imported models</a>. </p>
  - `roleArn` string — <p>The Amazon Resource Name (ARN) of an IAM service role that Amazon Bedrock assumes to perform tasks on your behalf. This role must have permissions to access the Amazon S3 bucket containing your model artifacts and the KMS key (if specified). For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-import-iam-role.html">Setting up an IAM service role for importing models</a> in the Amazon Bedrock User Guide.</p> <p>This field is required when you use <code>modelSourceConfig</code> with an Amazon S3 data source. It is not required when you use <code>customModelDataSource</code> with a model package ARN, because Amazon Bedrock uses its own credentials to access the model artifacts.</p>
  - `modelTags` Tag[] — <p>A list of key-value pairs to associate with the custom model resource. You can use these tags to organize and identify your resources.</p> <p>For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/tagging.html">Tagging resources</a> in the <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-service.html">Amazon Bedrock User Guide</a>.</p>
    - `key` string, required — Key for the tag.
    - `value` string, required — Value for the tag.
  - `clientRequestToken` string — A unique, case-sensitive identifier to ensure that the API request completes no more than one time. If this token matches a previous request, Amazon Bedrock ignores the request, but does not return an error. For more information, see <a href="https://docs.aws.amazon.com/AWSEC2/latest/APIReference/Run_Instance_Idempotency.html">Ensuring idempotency</a>.

## Response `202`

Success

- CreateCustomModelResponse
  - `modelArn` string, required — The Amazon Resource Name (ARN) of the new custom model.

## Other responses

- `480` — ResourceNotFoundException
- `481` — AccessDeniedException
- `482` — ValidationException
- `483` — ConflictException
- `484` — InternalServerException
- `485` — TooManyTagsException
- `486` — ServiceQuotaExceededException
- `487` — ThrottlingException

---

[API](https://skmtc.net/aws/apis/bedrock.md) · [All operations](https://skmtc.net/aws/apis/bedrock/llms.txt) · [OpenAPI document](https://skmtc-service-staging.skmtc.workers.dev/v1/apis/aws/bedrock/versions/3bc874e43ecd/schema)