Model Serving


Apr 2019
Apache-2.0 License
Github open issues
Github stars
5 Nov
Github last commit
Stackoverflow questions

What is BentoML?

BentoML is an end-to-end solution for machine learning model serving. It facilitates Data Science teams to develop production-ready model serving endpoints, with DevOps best practices and performance optimization at every stage.

BentoML offers a flexible and performant framework to serve, manage, and deploy ML models in production. It simplifies the process of building production-ready model API endpoints through its standard and easy architecture. It empowers teams with a powerful dashboard to help organize models and monitor deployments centrally.

How does BentoML Help?

Getting ML models into production is a tough job, and data scientists find it challenging to integrate DevOps best practices resulting in time-consuming and error-prone workflows. BentoML brings an end-to-end solution for model serving backed by standardized ML workflows, best DevOps practices, and performance optimization.

It serves the purpose of connecting data science and DevOps teams. It focuses on the efficient working structure to produce scalable, high-performance API endpoints. With a standardized interface, BentoML encapsulates complexities in integrating model serving workloads with cloud infrastructure. It supports a modular working mechanism with reusable configuration and minimal server downtime.

Key Features of BentoML

Versatile and modular framework

BentoML supports multiple ML frameworks such as PyTorch, Tensorflow, Keras, XGboost, and more. It facilitates model management, model serving, and model packaging with a unified model packaging format.

Flexible and cloud-agnostic

BentoML ensures cloud-native deployments with Kubernetes, Docker, Azure, AWS, and many others. It abstracts away complexities in integrating model serving workloads with cloud infrastructures.


BentoML is a beneficial model serving tool that offers high-performance online API serving and offline batch serving. It supports a flexible workflow with the help of a high-performance model server, and the advanced micro-batching mechanism enables tremendous throughput.  

Superior experience

BentoML delivers a user-friendly experience with powerful web dashboards and APIs for model registry and deployment operations. The UI dashboard empowers users with a centralized way to organize models and monitor deployments.

DevOps best practices baked in

BentoML bridges the gap between data science and DevOps teams. It reinforces best DevOps practices with high-quality prediction services using DevOps languages and integrates aptly with common infrastructure tools.

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