Amazon Bedrock now exposes Hugging Face open-weight models—including Gemma, Llama 3, Mistral, and more—through a single catalog. You invoke them with the same Bedrock APIs you already use for Titan, Anthropic, Cohere, etc. Under the hood, Bedrock Marketplace model endpoints are managed by Amazon SageMaker AI. With Bedrock Marketplace, you can now combine the ease of use of SageMaker JumpStart with the fully managed infrastructure of Amazon Bedrock, including compatibility with high-level APIs such as Agents, Knowledge Bases, Guardrails and Model Evaluations.
| Requirement | |
|---|---|
| AWS account in a Bedrock Region | Marketplace is regional; switch the console to one of the 14 supported Regions first, for example us-east-1. |
| Permissions | For a quick trial, attach AmazonBedrockFullAccess and AmazonSageMakerFullAccess. |
| Service quotas | The SageMaker endpoint uses GPU instances (for example ml.g5). Verify you have quota or request it. |
| JumpStart-only | If you choose path B, create a SageMaker Studio domain and user profile first (Console ▸ SageMaker ▸ Domains). Open Studio before continuing. |
When registering your Sagemaker Jumpstart endpoints in Amazon Bedrock, you only pay for the SageMaker compute resources and regular Amazon Bedrock APIs prices are applicable.
There are two equivalent paths to use a Hugging Face model with Amazon Bedrock Marketplace.
Path A is from the Bedrock Model Catalog:
Path B is from SageMaker JumpStart for the model that shows “Use with Bedrock”:
To test the model interactively in the console, select the model under Marketplace deployments, open it in the playground, and send a prompt in Chat/Text mode to verify the model’s response.
Alternatively, you can programmatically access your endpoint.
import boto3
bedrock = boto3.client("bedrock-runtime")
# Paste the endpoint ARN you copied above
endpoint_arn = "arn:aws:sagemaker:<region>:<account‑id>:endpoint/<name>"
inference_cfg = {"maxTokens": 256, "temperature": 0.1, "topP": 0.95}
extra = {"parameters": {"repetition_penalty": 1.05}}
response = bedrock.converse(
modelId=endpoint_arn, # <- SageMaker endpoint ARN
messages=[{
"role": "user",
"content": [{"text": "Give me three taglines for a serverless AI startup"}]
}],
inferenceConfig=inference_cfg,
additionalModelRequestFields=extra,
)
print(response["output"]["message"]["content"][0]["text"])Heads‑up: the same modelId=endpoint_arn works with InvokeModel, Knowledge Bases (RetrieveAndGenerate), Agents, and Guardrails—no code changes.
| Resource | How to delete |
|---|---|
| SageMaker endpoint | Console → Marketplace deployments → select → Delete (also de‑registers it) • or boto3.client("sagemaker").delete_endpoint(...) |
| Optional extras | Delete Knowledge Base, Guardrail, or S3 vectors if you created them. |
For more information, refer to the Bedrock documentation.
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