---
id: sagemaker-serve
version: "1.19.0"
license: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for… (full text in the JSON record)
license_treatment: permissive
maintenance: active
---

# sagemaker-serve — SageMaker Serve package for model serving and deployment

License: permissive · Maintenance: active · Downloads: 1.3M/mo

## What it is and what it does

SageMaker Serve is a Python package for deploying and serving machine learning models on Amazon SageMaker. It abstracts the complexity of model hosting by providing a unified interface to SageMaker's inference infrastructure, working alongside sagemaker-core and sagemaker-train to complete the ML lifecycle from training through production serving.

The package depends on boto3 for AWS API interaction, torch and onnxruntime for model inference, and includes testing infrastructure (pytest, tqdm, psutil) and monitoring tools (mlflow, tritonclient). It targets developers building end-to-end ML pipelines on AWS who need to move trained models into production endpoints without managing the underlying SageMaker deployment details directly.

Use it for:

- Deploy trained PyTorch or ONNX models to SageMaker endpoints for real-time inference
- Manage model serving infrastructure and endpoint lifecycle on AWS
- Integrate model serving with SageMaker training pipelines for automated MLOps workflows
- Monitor and track model performance in production using MLflow integration
- Test model serving configurations locally before deploying to SageMaker

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides model serving and deployment functionality for machine learning models on Amazon SageMaker, integrating with SageMaker's core training and inference infrastructure.

Yes, if you are building ML workflows on AWS SageMaker and need a unified serving layer. The package is actively maintained, has low install friction, carries a permissive Apache 2.0 license, and integrates with the SageMaker ecosystem. However, verify that its API and deployment model match your specific serving requirements before committing, as the fact sheet does not detail its exact interface or whether it supports your model types.

## Install

pip install sagemaker-serve
uv add sagemaker-serve
poetry add sagemaker-serve

## Installing sagemaker-serve

Before you install: Low install friction with a pure Python wheel. Active maintenance status with a release 3 days old. Depends on 14 runtime packages including sagemaker-core, sagemaker-train, boto3, and ML frameworks (torch, onnxruntime), which may require additional system dependencies or AWS credentials.

License in practice: Apache License 2.0 (permissive) allows commercial use, modification, and distribution with minimal restrictions—primarily requiring license and copyright notice preservation in derivative works.

Quickstart:

pip install sagemaker-serve

from sagemaker_serve import ...
# Deploy and serve ML models on SageMaker

Requires Python 3.10 or later; AWS credentials and SageMaker access for actual deployment; torch and onnxruntime may require system-level dependencies.

Verify before relying:

- Specific API surface and main classes/functions available in the package
- Whether the package supports local testing or requires live SageMaker endpoints
- Integration patterns with sagemaker-core and sagemaker-train
- Performance characteristics and scalability limits for model serving

## Package facts

- License: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 1.3M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

sagemaker model serving, ml model deployment aws, sagemaker inference, model serving framework, aws sagemaker deploy, machine learning model hosting, sagemaker endpoint management, sagemaker, model-serving, aws-ml

[View on SkillFed](https://skillfed.io/packages/sagemaker-serve) · [View on PyPI](https://pypi.org/project/sagemaker-serve/)