BentoML Documentation

What BentoML is

BentoML is a Python library for building online serving systems for AI and ML models. You write a service as a Python class: load the model in its constructor and mark methods with @bentoml.api, using normal type hints for inputs and outputs. bentoml serve runs it locally as a REST API, bentoml build packages code, model and dependencies into a deployable unit called a Bento, and bentoml containerize turns that into a Docker image.

Why use it instead of plain FastAPI?

FastAPI is the best way to learn the moving parts of serving, and many models never need more. BentoML earns its place when models bring needs a web framework does not handle for you:

• Adaptive batching: mark an API as batchable and concurrent requests are grouped into one model call, which matters most on GPUs.
• Several models in one service: compose pipelines, such as a preprocessing model followed by a classifier, with each part scaled separately.
• Reproducible images: the Python version, packages and system dependencies are declared in code, and the image is generated for you.
• Any framework: scikit-learn, PyTorch, XGBoost, Transformers or your own inference code.

Ownership and licence

In February 2026 Modular, the company behind MAX and the Mojo language, announced that it had acquired BentoML. Both said BentoML stays open source under the Apache 2.0 licence. BentoCloud, the managed platform for deploying Bentos, is the paid part; check the official site for current plans.

How long it takes

The quickstart gets a model serving locally in under an hour. Batching, multi-model services and containerisation make a good weekend project, ideally with a model you have already registered in MLflow.

Prerequisites

Solid Python, a trained model you can load, and basic Docker. Knowing how an HTTP API works helps you read the request and response examples.

How it fits an MLOps workflow

It sits at the serving stage: load the champion model from your registry, expose a validated API, batch requests, build an image in CI and deploy it to any container platform or Kubernetes. Pair it with Prometheus metrics and your drift monitoring.

Common questions

Is it only for LLMs? No. It serves classic models, vision, audio and LLMs alike; for high-throughput LLM serving it is often combined with an engine such as vLLM.

Do I need BentoCloud? No. The images run anywhere Docker runs.

What can I do after it? Serve one or more models behind a batched, containerised API, measure the throughput gain from batching, and deploy it with confidence.

Best for: ML engineers who have outgrown a single FastAPI endpoint, especially with GPU models or multi-step inference.