Fast model deployment on any cloud 🚀
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Fast model deployment on any cloud

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bentoctl helps deploy any machine learning models as production-ready API endpoints on the cloud, supporting AWS SageMaker, AWS Lambda, EC2, Google Compute Engine, Azure, Heroku and more.

Join our Slack community today!

Looking deploy your ML service quickly? You can checkout BentoML Cloud for the easiest and fastest way to deploy your bento. It's a full featured, serverless environment with a model repository and built in monitoring and logging.


  • Framework-agnostic model deployment for Tensorflow, PyTorch, XGBoost, Scikit-Learn, ONNX, and many more via BentoML: the unified model serving framework.
  • Simplify the deployment lifecycle of deploy, update, delete, and rollback.
  • Take full advantage of BentoML's performance optimizations and cloud platform features out-of-the-box.
  • Tailor bentoctl to your DevOps needs by customizing deployment operator and Terraform templates.

Getting Started

Supported Platforms:


Custom Operator

Users can built custom bentoctl plugin from the deployment operator template to deploy to cloud platforms not yet supported or to internal infrastructure.

If you are looking for deploying with Kubernetes, check out Yatai: Model deployment at scale on Kubernetes.


pip install bentoctl

| bentoctl designed to work with BentoML version 1.0.0 and above. For BentoML 0.13 or below, you can use the pre-v1.0 branch in the operator repositories and follow the instruction in the README. You can also check out the quickstart guide for 0.13 here.



There are many ways to contribute to the project:

  • Create and share new operators. Use deployment operator template to get started.
  • If you have any feedback on the project, share it with the community in Github Discussions under the BentoML repo.
  • Report issues you're facing and "Thumbs up" on issues and feature requests that are relevant to you.
  • Investigate bugs and reviewing other developer's pull requests.

Usage Reporting

BentoML and bentoctl collects usage data that helps our team to improve the product. Only bentoctl's CLI commands calls are being reported. We strip out as much potentially sensitive information as possible, and we will never collect user code, model data, model names, or stack traces. Here's the code for usage tracking. You can opt-out of usage tracking by setting environment variable BENTOML_DO_NOT_TRACK=True:



Elastic License 2.0 (ELv2)

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