tensor-js
Deep learning in the browser with WebGL and WebAssembly acceleration.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is tensor-js?
tensor-js is a deep learning library designed to run directly in web browsers. It leverages WebGL for GPU acceleration and WebAssembly for high performance, making it ideal for running complex models on client devices without server-side dependencies.
Key differentiator
“tensor-js stands out as one of the few deep learning libraries optimized for client-side execution in web browsers, offering a unique blend of performance and accessibility.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
tensor-js lacks native support for complex architectures like transformers or GANs, requiring manual implementation
WebGL and WebAssembly performance is highly dependent on the device's graphics capabilities and may not be optimal on older hardware
Compared to larger frameworks like TensorFlow.js, tensor-js has fewer contributors and less third-party support for tools and libraries
Fit analysis
Who is it for?
✓ Best for
Teams building web apps where client-side processing is critical for performance or privacy reasons
Educators looking to demonstrate machine learning principles directly within a browser environment
✕ Not a fit for
Projects requiring heavy computational resources that exceed the capabilities of most browsers
Applications needing real-time, high-throughput inference on large datasets
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
Works well with
Next step
Get Started with tensor-js
Step-by-step setup guide with code examples and common gotchas.