ONNX Runtime Web

Run ONNX models directly in web browsers with this JavaScript library.

EstablishedOpen SourceLow lock-in

Pricing

Free tier

Flat rate

Adoption

Rising

License

Open Source

Data freshness

Verified · Jul 15, 2026

Overview

What is ONNX Runtime Web?

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.

Key differentiator

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.

Capability profile

Capability Radar

Ease of StartEcosystemValueMaturityFlexibilityScale Ready

Honest assessment

Strengths & Weaknesses

↑ Strengths

Runs ONNX models directly in web browsers.medium

No server-side dependencies for model inference.medium

Optimized performance through WebAssembly.medium

↓ Weaknesses

Limited support for ONNX model operationshigh

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

Performance may degrade with complex modelsmedium

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

Complex setup process for integrating with web applicationshigh

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

Documentation lacks depth on advanced use casesmedium

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

Fit analysis

Who is it for?

✓ Best for

Developers building web applications with machine learning capabilities who need to perform model inference on the client side.

Teams working on interactive AI applications that require fast response times and minimal latency.

Projects aiming for offline or low-bandwidth scenarios where server-side dependencies are not feasible.

✕ Not a fit for

Applications requiring real-time streaming of large datasets, as it is optimized for model inference rather than data processing.

Scenarios with strict security requirements that cannot be met by client-side execution of models.

Cost structure

Pricing

Free Tier

Available

Open source — free to use

Starts at

$0

Model

Flat rate

Enterprise

None

Performance benchmarks

How Fast Is It?

Ecosystem

Relationships

Next step

Get Started with ONNX Runtime Web

Step-by-step setup guide with code examples and common gotchas.

View Setup Guide →