Sagemaker allows users to host any ML model on their own AWS infrastructure.
With portkey you can manage/restrict access, log requests, and more.
Portkey SDK Integration with AWS Sagemaker
1. Install the Portkey SDK
Add the Portkey SDK to your application to interact with Sagemaker’s API through Portkey’s gateway.
npm install --save portkey-ai
2. Initialize Portkey with a Virtual Key
There are multiple ways to integrate Sagemaker with Portkey.
You can use your AWS credentials, or use an assumed role.
In this example we will create a virtual key and use it to interact with Sagemaker.
This helps you restrict access (specific models, few endpoints, etc).
Here’s how to find your AWS credentials: import Portkey from 'portkey-ai'
const portkey = new Portkey({
apiKey: "PORTKEY_API_KEY", // Replace with your Portkey API key
virtualKey: "VIRTUAL_KEY" // Replace with your Sagemaker Virtual Key
})
from portkey_ai import Portkey
portkey = Portkey(
api_key="PORTKEY_API_KEY", # Replace with your Portkey API key
virtual_key="VIRTUAL_KEY" # Replace with your Sagemaker Virtual Key
)
3. Invoke the Sagemaker model
Python SDK
NodeJS SDK
REST API
response = portkey.post(
url="endpoints/{endpoint_name}/invocations",
# You can pass any key value pair required by the model, apart from `url`, they are passed as kwargs to the Sagemaker endpoint
inputs="my_custom_value",
my_custom_key="my_custom_value",
)
print(response)
const response = await portkey.post(
url="endpoints/{endpoint_name}/invocations",
// You can pass any key value pair required by the model, apart from `url`, they are passed as kwargs to the Sagemaker endpoint
inputs="my_custom_value",
my_custom_key="my_custom_value",
)
console.log(response);
curl --location 'https://api.portkey.ai/v1/endpoints/{endpoint_name}/invocations' \
--header 'x-portkey-virtual-key: {VIRTUAL_KEY}' \
--header 'x-portkey-api-key: {PORTKEY_API_KEY}' \
--header 'Content-Type: application/json' \
--data '{
# You can pass any key value pair required by the model, they are passed as kwargs to the Sagemaker endpoint
"inputs": "my_custom_value",
"my_custom_key": "my_custom_value"
}'
Making Requests without Virtual Keys
If you do not want to add your AWS details to Portkey vault, you can also directly pass them while instantiating the Portkey client.
These are the supported headers/parameters for Sagemaker (Not required if you’re using a virtual key):
| Node SDK | Python SDK | REST Headers |
|---|
| awsAccessKeyId | aws_access_key_id | x-portkey-aws-access-key-id |
| awsSecretAccessKey | aws_secret_access_key | x-portkey-aws-secret-access-key |
| awsRegion | aws_region | x-portkey-aws-region |
| awsSessionToken | aws_session_token | x-portkey-aws-session-token |
| sagemakerCustomAttributes | sagemaker_custom_attributes | x-portkey-amzn-sagemaker-custom-attributes |
| sagemakerTargetModel | sagemaker_target_model | x-portkey-amzn-sagemaker-target-model |
| sagemakerTargetVariant | sagemaker_target_variant | x-portkey-amzn-sagemaker-target-variant |
| sagemakerTargetContainerHostname | sagemaker_target_container_hostname | x-portkey-amzn-sagemaker-target-container-hostname |
| sagemakerInferenceId | sagemaker_inference_id | x-portkey-amzn-sagemaker-inference-id |
| sagemakerEnableExplanations | sagemaker_enable_explanations | x-portkey-amzn-sagemaker-enable-explanations |
| sagemakerInferenceComponent | sagemaker_inference_component | x-portkey-amzn-sagemaker-inference-component |
| sagemakerSessionId | sagemaker_session_id | x-portkey-amzn-sagemaker-session-id |
| sagemakerModelName | sagemaker_model_name | x-portkey-amzn-sagemaker-model-name |
Example
from portkey_ai import Portkey
portkey = Portkey(
api_key="PORTKEY_API_KEY", # Replace with your Portkey API key
provider="sagemaker",
aws_region="us-east-1", # Replace with your AWS region
aws_access_key_id="AWS_ACCESS_KEY_ID", # Replace with your AWS access key id
aws_secret_access_key="AWS_SECRET_ACCESS_KEY", # Replace with your AWS secret access key
amzn_sagemaker_inference_component="SAGEMAKER_INFERENCE_COMPONENT" # Replace with your Sagemaker inference component
)
response = portkey.post(
url="endpoints/{endpoint_name}/invocations",
# You can pass any key value pair required by the model, apart from `url`, they are passed as kwargs to the Sagemaker endpoint
inputs="my_custom_value",
my_custom_key="my_custom_value"
)
print(response)
import Portkey from 'portkey-ai'
const portkey = new Portkey({
api_key:"PORTKEY_API_KEY",
provider:"sagemaker",
aws_access_key_id:"AWS_ACCESS_KEY_ID",
aws_secret_access_key:"AWS_SECRET_ACCESS_KEY",
aws_region:"us-east-1",
amzn_sagemaker_inference_component:"SAGEMAKER_INFERENCE_COMPONENT"
})
const response = await portkey.post(
url="endpoints/{endpoint_name}/invocations",
// You can pass any key value pair required by the model, apart from `url`, they are passed as kwargs to the Sagemaker endpoint
inputs="my_custom_value",
my_custom_key="my_custom_value"
)
console.log(response)
curl https://api.portkey.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "x-portkey-api-key: $PORTKEY_API_KEY" \
-H "x-portkey-provider: sagemaker" \
-H "x-portkey-aws-access-key-id: $AWS_ACCESS_KEY_ID" \
-H "x-portkey-aws-secret-access-key: $AWS_SECRET_ACCESS_KEY" \
-H "x-portkey-aws-region: $AWS_REGION" \
-H "x-portkey-amzn-sagemaker-inference-component: $SAGEMAKER_INFERENCE_COMPONENT" \
-d '{
# You can pass any key value pair apart from `url` required by the model, they are passed as kwargs to the Sagemaker endpoint
"inputs": "my_custom_value",
"my_custom_key": "my_custom_value"
}'
Next Steps
The complete list of features supported in the SDK are available on the link below.
You’ll find more information in the relevant sections:
- Add metadata to your requests
- Add gateway configs to your Sagemaker requests
- Tracing Sagemaker requests
Last modified on January 28, 2026
