BentoML

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

8.4k

Stars

+246

Gained

3.0%

Growth

Python

Language

View on GitHub → ↑0.3% this week

💡 Why It Matters

BentoML addresses the complexities of deploying machine learning models by providing a streamlined platform for serving AI applications. This open source tool for engineering teams simplifies the creation of model inference APIs, job queues, and multi-model pipelines, making it particularly beneficial for ML/AI teams focused on production-ready solutions. With a steady growth of 246 stars over 96 days, it demonstrates stable community interest and maturity, indicating that it is a reliable choice for production use. However, teams should consider alternatives if they require extensive customisation or have specific needs that BentoML may not address.

🎯 When to Use

BentoML is a strong choice when teams need to quickly deploy AI models and require a self-hosted option for flexibility and control. Teams should consider alternatives if they are looking for a highly customisable solution or if their projects involve unique deployment environments.

👥 Team Fit & Use Cases

Data scientists, machine learning engineers, and AI developers will find BentoML particularly useful in their workflows. It is commonly integrated into products and systems that require AI-driven features, such as recommendation engines, chatbots, and real-time data analysis applications.

🎭 Best For

Machine Learning and AI Engineer

🏷️ Topics & 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

Latest commit: 2026-02-11. Over the past 97 days, this repository gained 246 stars (+3.0% growth). Activity data is based on daily RepoPi snapshots of the GitHub repository.