---
id: bentoml
version: "1.4.39"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# bentoml — BentoML: The easiest way to serve AI apps and models

License: permissive · Maintenance: active · Downloads: 274.7K/mo

## What it is and what it does

BentoML is a production-ready framework for turning ML model inference code into scalable REST APIs. You define a service class with decorated methods, and BentoML handles HTTP routing, request batching, containerization, and deployment orchestration. It abstracts away boilerplate for common serving patterns—dynamic batching, multi-model composition, GPU utilization, and observability—while remaining agnostic to the underlying ML framework (PyTorch, TensorFlow, scikit-learn, etc.).

The framework is designed for both local development and cloud deployment. Locally, you run `bentoml serve` to test your API. For production, you package your service into a standardized Bento artifact, then generate a Docker image with `bentoml containerize`. It also integrates with BentoCloud for managed hosting. The dependency footprint is substantial (42 runtime packages including aiohttp, pydantic, and opentelemetry), reflecting its full-featured nature as an application framework rather than a lightweight library.

Use it for:

- Deploy a fine-tuned LLM or vision model as a REST API without writing HTTP boilerplate or managing async concurrency yourself.
- Build a multi-model inference pipeline where requests flow through sequential or parallel model stages with automatic batching.
- Package a model service with all dependencies and environment config into a reproducible Docker image for on-premise or cloud deployment.
- Add observability and monitoring to model inference with built-in OpenTelemetry and Prometheus instrumentation.
- Optimize GPU utilization across multiple concurrent inference requests using adaptive batching and worker parallelization.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

BentoML is a Python framework for building and deploying REST APIs that serve AI and ML model inference at scale, with support for dynamic batching, multi-model orchestration, and containerized deployment.

Yes. BentoML is actively maintained, permissively licensed, and widely adopted. It solves a real problem—reducing boilerplate for production model serving—and has no known vulnerabilities. The 42 dependencies are expected for a full-featured serving framework. Install if you need to deploy ML models as scalable APIs; skip if you only need lightweight HTTP routing or are committed to a different serving paradigm.

## Install

pip install bentoml
uv add bentoml
poetry add bentoml

## Installing bentoml

Before you install: Low install friction with a pure Python wheel. Active maintenance with recent commits and a large community. Requires Python ≥3.9 and brings 42 runtime dependencies including aiohttp, pydantic, and opentelemetry instrumentation—manageable for a framework of this scope.

License in practice: Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for proprietary applications.

Quickstart:

pip install bentoml

import bentoml

@bentoml.service()
class MyModel:
    @bentoml.api()
    def predict(self, data: str) -> str:
        return f"Result: {data}"

# Run: bentoml serve

Requires Python ≥3.9. Local testing requires the ML framework (e.g., PyTorch, TensorFlow) and any model dependencies to be installed separately in your environment.

Verify before relying:

- Whether the 42 runtime dependencies are all mandatory or some are optional/conditional based on use case.
- Performance characteristics under high concurrency or GPU load compared to other serving frameworks.
- Stability and breaking-change frequency of the API across minor versions.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 274.7K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

model serving framework, ml inference api, ai model deployment, batch serving, docker containerization ml, model-serving, mlops, inference-api

[View on SkillFed](https://skillfed.io/packages/bentoml) · [View on PyPI](https://pypi.org/project/bentoml/)