# BentoML review

> BentoML is a Python library for building online serving systems and inference APIs for AI apps and models.

Devtoolwatch Board, the week of Oct 25–31, 2026. Canonical page: https://index.devtoolwatch.com/tools/bentoml-bentoml/

## TL;DR

BentoML is a Python library that turns model inference scripts into REST API servers using standard Python type hints. BentoML is for developers serving open-source or custom models in production. It packages code, models and dependencies into a deployable artifact called a Bento and generates Docker images automatically.

## BentoML at a glance

- **Category:** [Inference and training](https://index.devtoolwatch.com/categories/inference-and-training/)
- **Maintained by:** BentoML (organization, [@bentoml](https://github.com/bentoml) on GitHub)
- **License:** Apache 2.0
- **Language:** Python, Shell, Jinja
- **Repository:** [github.com/bentoml/BentoML](https://github.com/bentoml/BentoML)
- **Website:** [bentoml.com](https://bentoml.com)
- **PyPI package:** [bentoml](https://pypi.org/project/bentoml/) 1.4.39
- **Latest release:** [v1.4.39](https://github.com/bentoml/BentoML/releases/tag/v1.4.39) on GitHub, May 7, 2026
- **First published:** Apr 2, 2019

## What BentoML does

- BentoML turns a model inference script into a REST API server with a few lines of code and standard Python type hints.
- BentoML manages environments, dependencies and model versions through a config file and automatically generates Docker images.
- BentoML includes serving optimizations such as dynamic batching, model parallelism, multi-stage pipelines and multi-model inference-graph orchestration.
- BentoML lets you implement custom APIs or task queues with your own business logic and multi-model composition.
- BentoML supports any ML framework, modality and inference runtime.
- BentoML deploys to production with Docker containers or Kubernetes after local development and debugging.

### How it works

You define a service class in a service.py file with the @bentoml.service and @bentoml.api decorators. Running bentoml serve starts an HTTP server on localhost:3000. You can call it from a browser or from a Python client. bentoml build packages the code, models and dependency configs into a Bento. bentoml containerize turns the Bento into a Docker image.

## How to install BentoML

**With pip**

```
pip install -U bentoml
```

### Requirements

- Python 3.9 or later
- Docker must be running to generate container images

### First steps

1. Define your APIs in a service.py file using the @bentoml.service and @bentoml.api decorators.
2. Install your model's dependencies, such as torch and transformers, into your Python virtual environment for local runs.
3. Run bentoml serve to serve locally at http://localhost:3000, then call it with bentoml.SyncHTTPClient.
4. Run bentoml build, then bentoml containerize to produce a Docker image for deployment.

## Who BentoML is for

- Python developers turning model inference scripts into REST APIs
- Teams building multi-model pipelines or LLM apps with batching and parallelism
- Developers who want reproducible Docker images for deploying models to Kubernetes

### Limitations

- BentoML is a Python library, so services are written in Python.
- BentoML collects anonymous usage data by default. You can opt out with --do-not-track or BENTOML_DO_NOT_TRACK=True.
- BentoML's containerization step requires a running Docker installation.

## Usage data (the week of Oct 25–31, 2026)

- Board rank: #274 overall, #30 in Inference and training, momentum score 35
- Downloads that week: 51.1k (+29% vs 4-week average)
- New GitHub stars per week (last 12 weeks): Aug 9: 19; Aug 16: 11; Aug 23: 13; Aug 30: 11; Sep 6: 19; Sep 13: 17; Sep 20: 13; Sep 27: 13; Oct 4: 20; Oct 11: 12; Oct 18: 15; Oct 25: 12

## BentoML alternatives

| Rank | Tool | Stars | Downloads/wk |
|---|---|---|---|
| #1 | [dsh-our-free-model](https://index.devtoolwatch.com/tools/ebony-vinyl-dsh-our-free-model/) | 54.3k | – |
| #2 | [magpie](https://index.devtoolwatch.com/tools/yetone-magpie/) | 26.9k | – |
| #3 | [Strata](https://index.devtoolwatch.com/tools/niko1221-strata/) | 37.1k | – |
| #4 | [edge0](https://index.devtoolwatch.com/tools/edge0-ai-edge0/) | 8.9k | – |
| #5 | [OmniRoute](https://index.devtoolwatch.com/tools/diegosouzapw-omniroute/) | 83.2k | 106k |
| #6 | [CLIProxyAPI](https://index.devtoolwatch.com/tools/router-for-me-cliproxyapi/) | 57.1k | – |

## BentoML FAQ

### Is BentoML free and open source?

Yes. BentoML's source code is on GitHub (bentoml/BentoML) under the Apache 2.0 license, so you can use it for free. Its website also links to a pricing page (https://www.modular.com/pricing?utm_source=bentoml_pricing) for any paid plans.

### How do I install BentoML?

Run `pip install -U bentoml`. The bentoml package needs Python 3.9 or newer. The install section above has the other options from the project's README.

### Is BentoML still maintained?

Yes. The bentoml/BentoML repository was last updated on Oct 20, 2026, and the latest release, v1.4.39 on GitHub, came out on May 7, 2026.

### How popular is BentoML?

BentoML has 8.9k GitHub stars as of Nov 1, 2026. It gained 12 new stars in the week of Oct 25–31, 2026, against a 12-week average of 15 a week. Its main package had 51.1k downloads that week, +29% against its own 4-week average. That made it #30 among LLM inference and training tools on the Devtoolwatch Board.

### BentoML vs MLflow: how do they compare?

BentoML and MLflow are both tracked by Devtoolwatch. BentoML has 8.9k GitHub stars and MLflow has 28.6k. In the week of Oct 25–31, 2026, BentoML's main package had 51.1k downloads and MLflow's had 5.2M. MLflow is an open source AI engineering platform for tracing, evaluating, monitoring, and managing agents, LLM applications, and ML models. [MLflow review](https://index.devtoolwatch.com/tools/mlflow/)

### BentoML vs vLLM: how do they compare?

BentoML and vLLM are both tracked by Devtoolwatch as LLM inference and training tools. BentoML has 8.9k GitHub stars and vLLM has 96k. In the week of Oct 25–31, 2026, BentoML's main package had 51.1k downloads and vLLM's had 677.2k. vLLM is an open-source Python library and engine for high-throughput, memory-efficient inference and serving of large language models. [vLLM review](https://index.devtoolwatch.com/tools/vllm/)

### What are the best alternatives to BentoML?

Other LLM inference and training tools on the Devtoolwatch Board in the week of Oct 25–31, 2026, by momentum: dsh-our-free-model (#1), magpie (#2), Strata (#3), edge0 (#4), OmniRoute (#5). The alternatives table above has their numbers.

### What is BentoML used for?

BentoML is used to build model inference APIs and multi-model serving systems. The project lists examples for LLMs, image generation, embeddings, audio and computer vision models. It also supports job queues and LLM apps.

### Does BentoML collect usage data?

BentoML collects anonymous usage data about its internal API calls only. It excludes user code, model data, model names and stack traces. You can opt out with the --do-not-track CLI option or by setting BENTOML_DO_NOT_TRACK=True.

### How does BentoML deploy models?

BentoML packages code, models and dependency configs into a Bento with bentoml build. bentoml containerize then generates a Docker image. You can run that image with Docker or deploy it to Kubernetes.

### Does BentoML support GPU inference and batching?

BentoML supports GPU inference and adaptive batching. Its API decorator accepts batchable=True. Services can request GPU resources, such as a specific GPU type.

## Sources

1. [bentoml/BentoML on GitHub](https://github.com/bentoml/BentoML)
2. [BentoML README](https://github.com/bentoml/BentoML/blob/main/README.md)
3. [bentoml on PyPI](https://pypi.org/project/bentoml/)
4. [BentoML website](https://bentoml.com)
5. [Release v1.4.39 (May 7, 2026)](https://github.com/bentoml/BentoML/releases/tag/v1.4.39)
6. [Release v1.4.38 (Apr 2, 2026)](https://github.com/bentoml/BentoML/releases/tag/v1.4.38)
7. [Release v1.4.37 (Mar 25, 2026)](https://github.com/bentoml/BentoML/releases/tag/v1.4.37)
8. [Hacker News: LLM Inference Handbook](https://news.ycombinator.com/item?id=44527947)
9. [Hacker News: Navigating the World of Large Language Models](https://news.ycombinator.com/item?id=39786943)
10. [Hacker News: Show HN: BentoML goes 1.0 – A faster way to ship your models to production](https://news.ycombinator.com/item?id=32088537)
11. [Devtoolwatch Board, the week of Oct 25–31, 2026](https://index.devtoolwatch.com/)