Keras.js vs TensorFlow.js

Side-by-side — pricing, strengths, tradeoffs, and fit.

Keras.js

Run Keras models in the browser with GPU support via WebGL 2.

TensorFlow.js

WebGL accelerated JavaScript library for ML in the browser.

At a glance

Open SourceFree Tier
◌ Declining·From $0
Open SourceFree Tier
· Not enough data·From $0

Overview

One-liner

Run Keras models in the browser with GPU support via WebGL 2.

One-liner

WebGL accelerated JavaScript library for ML in the browser.

Maturity

Declining

Maturity

Not enough data

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Developers building web applications that need to perform machine learning inference directly within the browser.
Educators and researchers who want to demonstrate neural network operations in real-time without server dependencies.

✓ Best for

Developers looking to integrate machine learning into web applications without server-side dependencies.
Teams that need real-time model inference directly in the browser.
Projects requiring WebGL-accelerated computation for performance-critical tasks.

Not ideal for

✕ Not for

Projects requiring high-performance GPU computations beyond what WebGL can provide.
Applications where model size or complexity exceeds the capabilities of client-side processing.

✕ Not for

Applications needing high-performance training on large datasets, as this is better suited to GPU-powered servers.
Scenarios where offline functionality without internet access is required, since TensorFlow.js relies on the web browser environment.

Strengths

↑ Strengths

Runs Keras models in the browser with GPU acceleration via WebGL 2.
Enables real-time inference directly on client devices.
Supports a wide range of model architectures and layers supported by Keras.

↑ Strengths

WebGL acceleration for fast computation in the browser
Supports both training and inference of ML models
Compatibility with TensorFlow models, allowing easy transfer learning

Weaknesses

↓ Weaknesses

Limited support for complex models and operations
Performance can degrade on low-end devices
Debugging and error handling are challenging in browser environment

↓ Weaknesses

Limited performance for complex models due to browser constraints
Small community compared to TensorFlow's Python counterpart
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