ONNX Runtime Web

What it is

ONNX Runtime Web is a JavaScript library that enables running ONNX models on the browser, facilitating machine learning inference without server-side dependencies. It's ideal for developers looking to deploy AI models directly within web applications.

ONNX Runtime Web stands out as the only JavaScript library enabling direct ONNX model execution in web browsers, offering unparalleled flexibility and performance for client-side machine learning applications.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited support for ONNX model operations

   Not all ONNX operators are supported, limiting the types of models that can be run in the browser.

2. 02

   Performance may degrade with complex models

   WebAssembly compilation and execution overhead can lead to slower inference times compared to native environments for large or complex ONNX models.

3. 03

   Complex setup process for integrating with web applications

   Requires manual configuration of Webpack, Babel, and other build tools to properly include and run ONNX Runtime Web in a project.

4. 04

   Documentation lacks depth on advanced use cases

   The official documentation focuses mainly on basic usage scenarios and lacks detailed guides for more complex integrations or optimizations.

What it does well

• Runs ONNX models directly in web browsers.

• No server-side dependencies for model inference.

• Optimized performance through WebAssembly.

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.