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

⭐ 8204 · Python · Last activity on GitHub: 2025-11-15

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

Why it matters for engineering teams

BentoML addresses the practical challenge of deploying and serving machine learning models in production environments with ease and reliability. It provides a streamlined way for ML engineering and AI teams to build model inference APIs, manage job queues, and create multi-model pipelines, reducing the complexity typically involved in operationalising AI applications. As a mature and production ready solution, it supports a variety of use cases including large language models and generative AI, making it suitable for teams focused on scalable model serving and inference. However, BentoML may not be the best fit for teams seeking a fully managed cloud service or those with minimal infrastructure resources, as it is primarily a self hosted option that requires some operational expertise.

When to use this project

BentoML is a strong choice when engineering teams need a flexible, open source tool for engineering teams to deploy and serve machine learning models with full control over the environment. Consider alternatives if your priority is a fully managed service or if your use case involves very lightweight or experimental models where simpler deployment options suffice.

Team fit and typical use cases

Machine learning engineers and AI engineering teams benefit most from BentoML, using it to package models into scalable APIs and build complex inference workflows. It commonly appears in products that require robust model serving capabilities such as AI-powered applications, real-time inference platforms, and multi-model pipelines. Its self hosted option for model inference service allows teams to maintain control over deployment and integration within 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: 2025-11-15. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.