| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Deep Learning Software | 8.3 (#7) | 7.3 (#29) |
| Ecosystem | The documentation lists ecosystem tools including Flax, Keras, Optax, TensorFlow Datasets, Hugging Face Datasets and NumPyro. | The site identifies Captum, PyTorch Geometric, and skorch as ecosystem projects or tools. |
| Hardware | The same code can execute on multiple backends, including CPU, GPU, and TPU. | The installer lists CPU, CUDA, and ROCm compute platform options. |
| Support | The installation guide directs users with problems using prebuilt wheels to the JAX GitHub issue tracker. | The Foundation directs users with technical questions to the PyTorch discussion community. |
| What it does | JAX is a Python library for accelerator-oriented array computation and program transformation, designed for high-performance numerical computing and large-scale machine learning. | PyTorch is an end-to-end machine learning framework for fast experimentation and production. |
| Who it is for | The project describes its core library as focused on the fundamentals of machine learning and numerical computing at scale. | The Foundation says its open-source projects serve developers, researchers, and enterprises building and deploying AI. |