🧩 AI FrameworksOpen SourceFree

BentoML

Unified ML model serving — any model type, production-ready

4.4/ 5⭐ 10,000 GitHub starsPythonApache-2.0

📋 Overview

BentoML is a unified framework for serving any ML model type — LLMs, embeddings, image models, and traditional ML — in production. Unlike SGLang (which focuses on LLM speed) or TensorFlow Serving (which is TF-only), BentoML provides a consistent Python API for packaging, deploying, and scaling any model. It includes built-in support for Docker, Kubernetes, and cloud deployment.

✨ Key Features

•Unified serving for any model type
•Python decorators for service definition
•Auto-generated REST APIs
•Docker and Kubernetes support
•Built-in batching and caching
•Apache 2.0 licensed
•Auto-generated Kubernetes configs
•REST API generation

🎯 The Problem It Solves

Serving different model types requires different tools — LLMs need SGLang, sklearn needs Flask, PyTorch needs TorchServe. BentoML unifies them all behind a single framework.

🔧 How It Works

You define a BentoML service with Python decorators — define input/output types, load your model, and BentoML handles the rest. It auto-generates REST APIs, Docker images, and Kubernetes configs. Built-in support for batching, caching, and scaling.

🚀 Installation & Quick Start

Installation

See website

Quick Start

See documentation

✅ Pros

•One framework for all model types
•Simple Python API
•Production-ready
•Apache 2.0 licensed
•Auto-generated Kubernetes configs
•REST API generation

❌ Cons

•Smaller community than SGLang
•Abstraction can feel limiting
•Limited model types

💬 Practitioner Verdict

“BentoML is the most flexible ML serving framework — the unified API is genuinely useful. The trade-off: smaller community than SGLang for LLMs, and the abstraction can feel limiting. For teams serving multiple model types, BentoML is the default.”