BentoML: Build Production-Grade AI Applications

AI, BentoML, MLOps, Model, Deployment, Serving, ai-infra, deep-learning, generative-ai, inference-api, kubernetes, llmops, lmops, machine-learning, microservices, ml-platform, model-deployment, model-inference, model-management, model-serving, multimodal-deep-learning
pip install bentoml==1.1.10



BentoML: The Unified AI Application Framework

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BentoML is a framework for building reliable, scalable, and cost-efficient AI applications. It comes with everything you need for model serving, application packaging, and production deployment.

👉 Join our Slack community!


🍱 Bento is the container for AI apps

  • Open standard and SDK for AI apps, pack your code, inference pipelines, model files, dependencies, and runtime configurations in a Bento.
  • Auto-generate API servers, supporting REST API, gRPC, and long-running inference jobs.
  • Auto-generate Docker container images.

🏄 Freedom to build with any AI models

🍭 Simplify modern AI application architecture

🚀 Deploy Anywhere

  • One-click deployment to ☁️ BentoCloud, the Serverless platform made for hosting and operating AI apps.
  • Scalable BentoML deployment with 🦄️ Yatai on Kubernetes.
  • Deploy auto-generated container images anywhere docker runs.


🛠️ What you can build with BentoML

Getting Started

Save or import models in BentoML local model store:

import bentoml
import transformers

pipe = transformers.pipeline("text-classification")

    "__call__": {"batchable": True}  # Enable dynamic batching for model

View all models saved locally:

$ bentoml models list

Tag                                     Module                Size        Creation Time
text-classification-pipe:kn6mr3aubcuf…  bentoml.transformers  256.35 MiB  2023-05-17 14:36:25

Define how your model runs in a file:

import bentoml

model_runner = bentoml.models.get("text-classification-pipe").to_runner()

svc = bentoml.Service("text-classification-service", runners=[model_runner])

async def classify(text: str) -> str:
    results = await model_runner.async_run([text])
    return results[0]

Now, run the API service locally:

bentoml serve

Sent a prediction request:

$ curl -X POST -H "Content-Type: text/plain" --data "BentoML is awesome" http://localhost:3000/classify


Define how a Bento can be built for deployment, with bentofile.yaml:

service: ''
name: text-classification-svc
  - ''
  - torch>=2.0
  - transformers

Build a Bento and generate a docker image:

$ bentoml build
Successfully built Bento(tag="text-classification-svc:mc322vaubkuapuqj").
$ bentoml containerize text-classification-svc
Building OCI-compliant image for text-classification-svc:mc322vaubkuapuqj with docker
Successfully built Bento container for "text-classification-svc" with tag(s) "text-classification-svc:mc322vaubkuapuqj"
$ docker run -p 3000:3000 text-classification-svc:mc322vaubkuapuqj

For a more detailed user guide, check out the BentoML Tutorial.


BentoML supports billions of model runs per day and is used by thousands of organizations around the globe.

Join our Community Slack 💬, where thousands of AI application developers contribute to the project and help each other.

To report a bug or suggest a feature request, use GitHub Issues.


There are many ways to contribute to the project:

  • Report bugs and "Thumbs up" on issues that are relevant to you.
  • Investigate issues and review other developers' pull requests.
  • Contribute code or documentation to the project by submitting a GitHub pull request.
  • Check out the Contributing Guide and Development Guide to learn more
  • Share your feedback and discuss roadmap plans in the #bentoml-contributors channel here.

Thanks to all of our amazing contributors!

Usage Reporting

BentoML collects usage data that helps our team to improve the product. Only BentoML's internal API calls are being reported. We strip out as much potentially sensitive information as possible, and we will never collect user code, model data, model names, or stack traces. Here's the code for usage tracking. You can opt-out of usage tracking by the --do-not-track CLI option:

bentoml [command] --do-not-track

Or by setting environment variable BENTOML_DO_NOT_TRACK=True:



Apache License 2.0

FOSSA Status


If you use BentoML in your research, please cite using the following [citation](./CITATION.cff:

author = {Yang, Chaoyu and Sheng, Sean and Pham, Aaron and  Zhao, Shenyang and Lee, Sauyon and Jiang, Bo and Dong, Fog and Guan, Xipeng and Ming, Frost},
license = {Apache-2.0},
title = {{BentoML: The framework for building reliable, scalable and cost-efficient AI application}},
url = {}