BentoML open source analysis

The easiest way to serve AI apps and models - Build Model Inference APIs, Job queues, LLM apps, Multi-model pipelines, and more!

Project overview

⭐ 8268 · Python · Last activity on GitHub: 2025-11-28

GitHub: https://github.com/bentoml/BentoML

Why it matters for engineering teams

BentoML addresses the practical challenge of deploying and serving machine learning models efficiently in production environments. It provides a streamlined approach to building model inference APIs, managing job queues, and orchestrating multi-model pipelines, which are essential tasks for ML engineering and AI teams. The project is mature and reliable, with a strong community and extensive use in production, making it a dependable open source tool for engineering teams focused on AI inference and model serving. However, BentoML may not be the best fit for teams seeking a fully managed cloud service or those with minimal Python expertise, as it requires some familiarity with Python and infrastructure management to operate effectively.

When to use this project

BentoML is a strong choice when teams need a production ready solution for serving diverse machine learning models with flexibility and control. Teams should consider alternatives if they prefer fully managed platforms or require minimal operational overhead without self hosting.

Team fit and typical use cases

Machine learning engineers and AI engineering teams benefit most from BentoML as it enables them to package, deploy, and serve models efficiently. It is commonly used in products involving AI inference, large language model serving, and multi-modal pipelines where a self hosted option for model inference service is required to meet specific production demands.

Best suited for

Topics and ecosystem

ai-inference deep-learning generative-ai inference-platform llm llm-inference llm-serving llmops machine-learning ml-engineering mlops model-inference-service model-serving multimodal python

Activity and freshness

Latest commit on GitHub: 2025-11-28. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.