JAX vs TensorFlow
JAX
docs.jax.devTensorFlow
tensorflow.orgSide by side
| Figure | JAX | TensorFlow |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Deep Learning Software | 8.3 (#7) | 7.5 (#22) |
| Ecosystem | The documentation lists ecosystem tools including Flax, Keras, Optax, TensorFlow Datasets, Hugging Face Datasets and NumPyro. | The TensorFlow ecosystem includes TensorFlow.js, LiteRT, tf.data, TFX, tf.keras, TensorFlow Datasets, and TensorBoard. |
| Integrations | The documentation lists ecosystem libraries including Flax, Keras, Optax, TensorFlow Datasets, Hugging Face Datasets, NumPyro, and PyMC. | The TFX pipeline tutorial describes exporting pipeline source code that can be orchestrated with Apache Airflow and Apache Beam. |
| Support | The installation guide directs users with problems using prebuilt wheels to the JAX GitHub issue tracker. | TensorFlow directs users to its issue tracker, release notes, Stack Overflow, community forum, and announcement mailing list. |
Plans
In their own words
TensorFlow
TensorFlow is a free, open-source platform for creating machine learning models and running them in different environments. Model-building options include the high-level Keras API, eager execution, and distributed training tools. Models can be deployed on servers, edge devices, and the web: TFX supports production pipelines, TensorFlow Lite supports mobile and edge inference, and TensorFlow.js covers JavaScript environments. The ecosystem also includes LiteRT, tf.data, TensorFlow Datasets, and TensorBoard. TensorFlow provides resources addressing fairness, interpretability, privacy, and security in machine learning workflows, including TF Privacy for privacy-focused training and TF Federated for federated learning. Supported 64-bit environments include Ubuntu, Windows, and macOS; WSL2 GPU support is marked experimental. TensorFlow's install guide states that macOS has no GPU support. Google Colab can run TensorFlow tutorials in browser-based Jupyter notebooks without installation or setup. The API and reference implementation were released under the Apache 2.0 license in November 2015. Listed platforms include Android, iOS, Linux, macOS, self-hosted, web, and Windows.