BentoML
A Python library for building production-ready model inference APIs, job queues, and multi-model serving systems for AI applications.
What is BentoML?
BentoML is a Python library for building online serving systems optimized for AI applications and model inference. It turns model inference scripts into production-ready REST APIs with minimal code, handling dependency management, containerization, and performance optimizations. The framework solves the problem of deploying and scaling AI models reliably across different environments.
AI/ML engineers and developers who need to deploy machine learning models (including LLMs, diffusion models, and embeddings) into production serving systems. It's ideal for teams building scalable inference APIs, job queues, or multi-model pipelines.
Developers choose BentoML for its simplicity in creating production APIs from any model, its automatic Dockerization for reproducibility, and its built-in performance features like dynamic batching. It offers a unified framework that works with any ML framework and runtime, reducing deployment complexity.
Overview
The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!
Use Cases
Best For
- Building REST APIs for large language models (LLMs) like Llama or Mistral
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
