What is BentoML used for?

BentoML is an inference platform for deploying, managing, and scaling AI models with support for custom and open-source models, offering deployment automation, observability, and scaling features. It is a good fit for deploying open-source models like Llama 4 and DeepSeek, serving custom fine-tuned models, and running multi-model pipelines.

Is BentoML open source?

Yes. BentoML is open source under the Apache-2.0 licence. Its source code is on GitHub at bentoml/BentoML and is written mainly in Python.

Yes. BentoML is open source, so the software itself is free to use. A managed cloud version is also available.

Can I self-host BentoML?

Yes. BentoML can be self-hosted on your own server or infrastructure.

What is BentoML an alternative to?

BentoML is an open-source alternative to Amazon SageMaker, Arize AI, Fireworks AI and Baseten. Other open-source alternatives to Amazon SageMaker include MLflow, Kubeflow and vLLM.

Is BentoML actively maintained?

Yes. The most recent commit to BentoML was on 5 October 2026, and the latest release is v1.4.39, published on 7 May 2026. The project has 8.9k stars on GitHub.

BentoML: open-source alternative to Amazon SageMaker

About BentoML

Bento is an inference platform designed to deploy and scale AI models. It supports any model architecture or framework, including open-source models like Llama 4 and DeepSeek, and offers a unified framework for packaging and deployment.

The platform includes features for managing inference, such as deployment automation, observability, access control, and performance tuning. It provides compute orchestration with cross-region scaling, auto-scaling, and multi-cloud support, and can be deployed on your own cloud, on-premises, or on Kubernetes.

Key features

Deploy any model with a unified framework
Deployment automation and CI/CD
Comprehensive observability
Fine-grained access control
Elastic auto-scaling and scaling-to-zero
Multi-cloud compute orchestration

Good fit for

Deploying open-source models like Llama 4 and DeepSeek
Serving custom fine-tuned models
Running multi-model pipelines

Built with: Python tools
Tags: inference; ai; model-serving; deployment; scaling

BentoML: questions and answers

What is BentoML used for?: BentoML is an inference platform for deploying, managing, and scaling AI models with support for custom and open-source models, offering deployment automation, observability, and scaling features. It is a good fit for deploying open-source models like Llama 4 and DeepSeek, serving custom fine-tuned models, and running multi-model pipelines.
Is BentoML open source?: Yes. BentoML is open source under the Apache-2.0 licence. Its source code is on GitHub at bentoml/BentoML and is written mainly in Python.
Is BentoML free?: Yes. BentoML is open source, so the software itself is free to use. A managed cloud version is also available.
Can I self-host BentoML?: Yes. BentoML can be self-hosted on your own server or infrastructure.
What is BentoML an alternative to?: BentoML is an open-source alternative to Amazon SageMaker, Arize AI, Fireworks AI and Baseten. Other open-source alternatives to Amazon SageMaker include MLflow, Kubeflow and vLLM.
Is BentoML actively maintained?: Yes. The most recent commit to BentoML was on 5 October 2026, and the latest release is v1.4.39, published on 7 May 2026. The project has 8.9k stars on GitHub.