BentoML

Platform for software engineers to build AI applications.

What is BentoML?

BentoML is a framework that helps software engineers build and deploy AI applications without getting bogged down in infrastructure work. You define your AI service once, then run it across different deployment targets, HTTP APIs, gRPC, batch jobs, or event streams, without rewriting code. It handles model versioning, preprocessing, multi-model orchestration, and deployment, so your team can focus on product logic rather than DevOps complexity. The tool works with any pre-trained models and is designed for teams that want to move AI projects from development to production quickly.

Key features

Model management

Version and store models in a standardised format, making it easy to track changes and switch between versions

Service framework

Define business logic, data preprocessing, model inference, and multi-model workflows in one place

Multiple deployment targets

Deploy the same service definition to HTTP servers, gRPC endpoints, batch processors, or event-driven architectures

API generation

Automatically create REST or gRPC APIs from your service definitions without manual boilerplate

Containerisation

Package applications with all dependencies for consistent deployment across environments

Pros & cons

Advantages

Reduces boilerplate code for common AI deployment tasks
Lets you write once and deploy to multiple protocols and platforms
Good for teams moving models from notebooks to production services
Active open-source community provides examples and integrations

Limitations

Requires learning BentoML's framework rather than using standard Python web frameworks
Most powerful features require paid tier; free version has limitations
Smaller ecosystem compared to established alternatives like FastAPI or TensorFlow Serving