| Written in Python, C++ and CUDA. | Written in Python, C++, CUDA and is based on Torch (written in Lua). |
| Developed by Google. | Developed by Facebook (now Meta AI). |
| API level: High and Low | API level: Low |
| Complex GPU installation. | Simple GPU installation. |
| Debugging: Difficult to conduct debugging and requires the TensorFlow debugger tool. | Debugging: Easy to debug as it uses dynamic computational process. |
| Architecture: TensorFlow is difficult to use/implement but with Keras, it becomes bit easier. | Architecture: Complex and difficult to read and understand. |
| Learning Curve: Steep and bit difficult to learn. | Learning Curve: Easy to learn. |
| Distributed Training: To allow distributed training, you must code manually and optimize every operation run on a specific device. | Distributed Training: By relying on native support for asynchronous execution through Python it gains optimal performance in the area of data parallelism. |
| APIs for Deployment/Serving Framework: TensorFlow serving. | APIs for Deployment/Serving Framework: TorchServe |
| Key Differentiator: Easy-to-develop models. | Key Differentiator: Highly 'Pythonic' and focuses on usability with careful performance considerations. |
| Widely used at the production level in Industry. | PyTorch is more popular in the research community. |
| Tools: TensorFlow Serving, TensorFlow Extended, TF Lite, TensorFlow.js, TensorFlow Cloud, Model Garden, MediaPipe and Coral. | Tools: TorchVision, TorchText, TorchAudio, PyTorch-XLA, PyTorch Hub, SpeechBrain, TorchX, TorchElastic and PyTorch Lightning. |
| Utilization: Large-scale deployment. | Utilization: Research-oriented and rapid prototype development. |
