BentoML

BentoML is a Python library that helps developers turn AI and machine learning models into web APIs that other software can call. Instead of keeping a trained model locked inside a script, you write a short service definition using standard Python code, and BentoML handles the work of spinning it up as a running server that accepts requests and returns results. The README shows this in just a few dozen lines for a text summarization example.

Beyond basic API creation, the library also manages packaging. Running one command bundles your code, model weights, and dependency list into a single unit called a Bento. From there, another command generates a Docker container image from that bundle, so the same service can be shipped to any server or cloud environment without manually reconfiguring dependencies. This is aimed at reducing the common problem of a model working on one machine but failing elsewhere due to version mismatches.

BentoML includes performance features for production deployments, such as dynamic batching, which groups incoming requests together so the model processes multiple inputs at once rather than one at a time. It also supports running multiple copies of a model in parallel and chaining several models together in a pipeline. These features are described in the advanced topics section of the README and linked documentation.

The project offers two deployment paths. The first is self-hosted: you build the container and run it on your own infrastructure. The second is BentoCloud, a paid cloud platform run by the BentoML team where you can deploy and scale services without managing servers yourself. The open-source library is free under the Apache 2.0 license, while BentoCloud is a separate commercial product.

The target audience is software developers and data scientists who have already built or downloaded an AI model and need a practical way to make it accessible to other systems or users via a network endpoint.