# Amazon SageMaker Runtime API

> Amazon SageMaker Runtime API on Apizie: 2 endpoints (After you deploy a model into production using Amazon SageMaker hosting services, your client applications…, After you deploy a model into production using Amazon SageMaker hosting services, your client applications…) returned as clean JSON through one API key. Send your own provider key in the x-upstream-key header. Call it over REST at https://zieapi.vercel.app/v1/cloud:amazonaws-runtime-sagemaker/{endpoint} or from Claude, ChatGPT and Cursor through the Apizie MCP server. Free plan: 1,000 requests a month.

- Project key: `cloud:amazonaws-runtime-sagemaker`
- Category: Cloud
- Status: byok
- Source: https://github.com/mermade/aws2openapi
- Page: https://zieapi.vercel.app/api/cloud/amazonaws-runtime-sagemaker
- OpenAPI: https://zieapi.vercel.app/api/cloud/amazonaws-runtime-sagemaker/openapi.json

The Amazon SageMaker runtime API.

## Endpoints

### After you deploy a model into production using Amazon SageMaker hosting services, your client applications…
`POST https://zieapi.vercel.app/v1/cloud:amazonaws-runtime-sagemaker/invoke-endpoint` (byok)

| Parameter | Type | Required | Description |
|---|---|---|---|
| region | string | no | The AWS region |
| EndpointName | string | yes | The name of the endpoint that you specified when you created the endpoint using the CreateEndpoint API. |
| X-Amzn-SageMaker-Custom-Attributes | string | no | Provides additional information about a request for an inference submitted to a model hosted at an Amazon SageMaker endpoint. The… |
| X-Amzn-SageMaker-Target-Model | string | no | The model to request for inference when invoking a multi-model endpoint. |
| X-Amzn-SageMaker-Target-Variant | string | no | Specify the production variant to send the inference request to when invoking an endpoint that is running two or more variants. Note that… |
| X-Amzn-SageMaker-Target-Container-Hostname | string | no | If the endpoint hosts multiple containers and is configured to use direct invocation, this parameter specifies the host name of the… |
| X-Amzn-SageMaker-Inference-Id | string | no | If you provide a value, it is added to the captured data when you enable data capture on the endpoint. For information about data capture,… |
| X-Amzn-SageMaker-Enable-Explanations | string | no | An optional JMESPath expression used to override the EnableExplanations parameter of the ClarifyExplainerConfig API. See the… |
| body | object | yes | JSON request body |

### After you deploy a model into production using Amazon SageMaker hosting services, your client applications…
`POST https://zieapi.vercel.app/v1/cloud:amazonaws-runtime-sagemaker/invoke-endpoint-async` (byok)

| Parameter | Type | Required | Description |
|---|---|---|---|
| region | string | no | The AWS region |
| EndpointName | string | yes | The name of the endpoint that you specified when you created the endpoint using the CreateEndpoint API. |
| X-Amzn-SageMaker-Content-Type | string | no | The MIME type of the input data in the request body. |
| X-Amzn-SageMaker-Accept | string | no | The desired MIME type of the inference in the response. |
| X-Amzn-SageMaker-Custom-Attributes | string | no | Provides additional information about a request for an inference submitted to a model hosted at an Amazon SageMaker endpoint. The… |
| X-Amzn-SageMaker-Inference-Id | string | no | The identifier for the inference request. Amazon SageMaker will generate an identifier for you if none is specified. |
| X-Amzn-SageMaker-InputLocation | string | yes | The Amazon S3 URI where the inference request payload is stored. |
| X-Amzn-SageMaker-RequestTTLSeconds | integer | no | Maximum age in seconds a request can be in the queue before it is marked as expired. The default is 6 hours, or 21,600 seconds. |
| X-Amzn-SageMaker-InvocationTimeoutSeconds | integer | no | Maximum amount of time in seconds a request can be processed before it is marked as expired. The default is 15 minutes, or 900 seconds. |

## Example
```
curl "https://zieapi.vercel.app/v1/cloud:amazonaws-runtime-sagemaker/invoke-endpoint" \
  -H "x-api-key: $APIZIE_KEY" \
  -H "x-upstream-key: $PROVIDER_KEY"
```

## FAQ

### How do I call the Amazon SageMaker Runtime API?
Create a free Apizie key, then send POST https://zieapi.vercel.app/v1/cloud:amazonaws-runtime-sagemaker/invoke-endpoint with the header x-api-key. Every endpoint returns { project, data, timestamp }.

### Is the Amazon SageMaker Runtime API free on Apizie?
Yes. The Free plan includes 1,000 requests a month at 100 requests a minute, no credit card. Amazon SageMaker Runtime itself also needs your own key from amazonaws.com.

### Can AI agents use the Amazon SageMaker Runtime API?
Yes. Add the Apizie MCP server (https://zieapi.vercel.app/mcp) to Claude, ChatGPT, Cursor or VS Code, then ask in plain language. The agent calls run_scraper with project key cloud:amazonaws-runtime-sagemaker.

### What is the status of the Amazon SageMaker Runtime API?
Needs your own provider key (bring your own key)