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BentoML

The easiest way to serve AI apps and models — build inference APIs, job queues, LLM apps, and multi-model pipelines.

★ 8,770 GitHub Apache-2.0 mlopsmodel-servingllminferencepython LLM & Chat

Overview

BentoML is an open-source platform for building, shipping, and scaling AI applications. Turn any Python model into a production-ready inference API with just a few lines of code. Supports LLM apps, vision models, multi-model pipelines, and background job queues with adaptive batching and distributed serving. Native integrations with PyTorch, TensorFlow, and Hugging Face Transformers. Deploy anywhere: Docker, Kubernetes, or BentoCloud.

Requirements

Min vCPU
2
Min RAM
4096 MB
Min Disk
10 GB
Rec vCPU
4
Rec RAM
8192 MB
Rec Disk
20 GB

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Affiliate disclosure

Docker Compose

# Generated by Run This Ai — docker-compose.yml
services:
  bentoml:
    image: bentoml/model-server:latest
    restart: unless-stopped
    ports:
      - 8080:8080
    volumes:
      - ./data/bentoml:/data

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Guides & articles

BentoML Tutorial: Deploy Your First LLM Inference API with Docker

Step-by-step: build a Bento, containerize it, and serve an LLM API with BentoML and Docker. Includes resource requirements and monitoring setup.

Aug 9, 2026

BentoML Guide: How to Serve AI Models in Production

BentoML turns any Python model into a production-ready inference API. Learn how adaptive batching, job queues, and multi-model pipelines make AI serving simple.

Aug 9, 2026