What Backends Does Keras 3 Support?

Keras 3 supports JAX, TensorFlow, PyTorch for full training and inference, plus OpenVINO for inference-only deployment. Backend selection is configured via environment variable or config file before importing Keras.

How Does Keras Compare To PyTorch For Model Building?

Keras provides higher-level abstractions with simpler syntax, while PyTorch offers more granular control. Keras 3 allows running Keras models on PyTorch backend, combining Keras usability with PyTorch ecosystem benefits.

Can Keras Models Be Deployed In Production Environments?

Yes, Keras models export to multiple formats including SavedModel, ONNX, and can leverage TensorFlow Serving, TorchServe, or OpenVINO for production deployment across cloud and edge devices.

What Is KerasHub And How Does It Work?

KerasHub provides pre-trained model architectures including Gemma, Stable Diffusion, and vision models. Models load via from_preset() method and support fine-tuning across all supported backends.

Does Keras Support Distributed Training On Multiple GPUs?

Yes, Keras scales from laptop to datacenter with native support for multi-GPU and TPU training. Distribution strategies integrate with backend-specific parallelism like JAX pmap or TensorFlow distribute.

Listed · Reviewed May 2026 · 7-criteria rubric

AI Code Tools

Keras

Name: Keras
Availability: InStock
Author: Google

Multi-Backend Deep Learning Framework For Building Neural Networks Fast

by Google · Mountain View, CA, USA · Founded 2015

Visit Keras →

Pricing

Free

What is Keras?

Keras is an open-source Python deep learning API designed for building and iterating on neural networks quickly.

It targets researchers, ML engineers, students and prototypers who prefer a readable, Pythonic interface across multiple computation backends; Keras 3 (2026) supports TensorFlow, JAX, PyTorch and OpenVINO so the same model code can run on GPUs, TPUs or alternate runtimes with minimal changes.

The project offers a modular layers API, Functional and subclassing model styles, a familiar compile/fit training loop with callbacks (ModelCheckpoint, EarlyStopping, TensorBoard), data preprocessing and augmentation utilities, mixed-precision and distribution-aware training where supported, and a catalog of pre-trained models via Keras Applications and KerasHub; the broader ecosystem includes KerasCV, KerasNLP and integrations with hyperparameter tools like KerasTuner.

It is free under the Apache 2.0 license and maintained by the TensorFlow community and external contributors. Trade-offs: the high-level abstraction accelerates development but can hide low-level performance details, and backend-specific optimizations or custom kernels often require working in the underlying framework.

Keras — Multi-Backend Deep Learning Framework For Building Neural Networks Fast Whether you're evaluating Keras for your team or comparing it to alternatives in the AI Code Tools category, this in-depth review covers everything: features, pricing, real user reviews, pros and cons, integrations, and direct comparisons against competitors.

Key Features 8

Multi-Backend Deep Learning API Supporting JAX, TensorFlow, PyTorch, and OpenVINO Frameworks

Human-Centric API Design Focused on Debugging Speed, Code Elegance, and Maintainability

KerasHub Provides Pre-Trained Models Like BERT, Gemma, StableDiffusion Across All Backends

Built-In Distribution API Enabling Large-Scale Data Parallelism and Model Parallelism

Cross-Framework NumPy-Compatible Operations via keras.ops for Custom Layers and Models

Progressive Disclosure of Complexity From Simple Sequential Models to Advanced Workflows

Seamless Cross-Framework Model Saving, Exporting, and Deployment Without Backend Lock-In

Compatible With Multiple Data Pipelines Including tf.data, PyTorch DataLoader, and NumPy

Who Is Keras For

1 Machine Learning Engineers

2 Data Scientists

3 AI Researchers

4 Python Developers

5 Computer Vision Specialists

6 NLP Practitioners

Pros & Cons

Pros 4 benefits

Backend-Agnostic Model Portability
Extensive Pre-Trained Model Library
Strong Community Documentation
Simple High-Level Neural API

Cons 3 limitations

Limited Low-Level Customization
Abstraction Hides Backend Optimization
Debugging Complex Models Challenging

Frequently Asked Questions

5 questions

Who is Keras for?

Keras is most useful for Machine Learning Engineers, Data Scientists, AI Researchers and Python Developers.

Keras pricing

Keras is free to use. Free . For the current tier breakdown and any limits, see the pricing section above or check the vendor's pricing page directly — limits and prices change.

What's New

Latest Stable Release 3.11.3

Bug fixes and stability improvements for multi-backend support

Aug 22

Keras 3 Multi-Backend Launch 3.0

Major release introducing JAX, PyTorch, and TensorFlow backend support with unified API

Nov 27

View all updates

Security & Privacy

Local execution

Secure model serialization with .keras format Safe loading protocols for untrusted models Input validation for untrusted data

All Features of Keras