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

⭐ 8350 · Python · Last activity on GitHub: 2026-01-05

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

Why it matters for engineering teams

BentoML addresses the practical challenges of deploying and serving machine learning models in production environments. It provides a streamlined, production ready solution for building model inference APIs, managing job queues, and orchestrating multi-model pipelines, which reduces the complexity engineers face when scaling AI applications. This open source tool for engineering teams is particularly suited to machine learning and AI engineering roles focused on model serving and inference. BentoML is mature and reliable enough for production use, with a robust ecosystem and active community support. However, it may not be the best fit for teams seeking a fully managed cloud service or those working with highly custom or experimental inference workflows that require bespoke infrastructure components.

When to use this project

BentoML is a strong choice when teams need a self hosted option for serving machine learning models with minimal overhead and want to maintain control over their deployment environment. Teams should consider alternatives if they require tightly integrated cloud-based MLOps platforms or if their use case demands specialised hardware acceleration beyond BentoML's current capabilities.

Team fit and typical use cases

Machine learning engineers and AI engineering teams benefit most from BentoML as they use it to package, deploy, and serve models in production environments. It is commonly employed in products that require scalable and reliable model inference services, such as recommendation systems, natural language processing applications, and generative AI tools. The platform supports real engineering workflows by simplifying model deployment and enabling integration with existing infrastructure.

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: 2026-01-05. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.