What is the difference between PyTorch and TensorFlow (with Keras)?

PyTorch runs code eagerly and feels like normal Python, so models are easy to write and debug. TensorFlow with Keras gives a concise high-level API and compiles work into graphs for speed and deployment. In short, PyTorch leans toward flexibility and research, while TensorFlow leans toward mature production and mobile tooling.

Choose PyTorch for research, custom architectures, and LLM work, where most new papers, open models, and libraries like Hugging Face are PyTorch-first. It is also a good pick when your team values easy debugging with standard Python tools. Its distributed tools, such as DDP and FSDP, scale well to large models.

Choose TensorFlow when you already run TensorFlow pipelines, need TF Serving, TFX, or TPU-centric workflows, or deploy to mobile and browsers with LiteRT (formerly TF Lite) and TF.js. Keras also suits teams that want short, readable model code for standard architectures. Existing team skills and infrastructure often decide it.

PyTorch lets researchers change models freely with normal Python control flow and inspect tensors at any line. Most new papers, open-weight LLMs, and tools like Hugging Face Transformers, vLLM, and DeepSpeed are built on it first. That network effect makes reproducing and extending work far easier in PyTorch.

Eager execution runs each operation immediately, like normal Python. Graph execution first builds a computation graph, then optimises and runs it. TensorFlow 2 is eager by default but uses tf.function to trace graphs for speed. PyTorch is eager by default and uses torch.compile to capture and optimise graphs when you want speed.

PyTorch runs your code line by line, so you can use print, pdb, or an IDE debugger to inspect real tensor values. In TensorFlow, code inside tf.function runs as a traced graph, so Python prints run only during tracing and errors can point to graph ops. You can disable tf.function to debug, but it is an extra step.

torch.compile, added in PyTorch 2.0, captures Python code into graphs with TorchDynamo and generates fast kernels, often with Triton on GPUs. tf.function traces Python into a TensorFlow graph that can be optimised, optionally with XLA. Both trade some flexibility and compile time for faster execution.

Keras 3 is multi-backend: the same Keras code can run on TensorFlow, JAX, or PyTorch. This means Keras is no longer tied only to TensorFlow. A team can write high-level Keras models and still pick a PyTorch backend, which blurs the old PyTorch versus Keras divide.

TensorFlow has TF Serving for servers, LiteRT for mobile and edge, and TF.js for browsers, all built around the SavedModel format. PyTorch uses torch.export, ONNX export, ExecuTorch for edge devices, and general servers like NVIDIA Triton or vLLM for LLMs. TensorFlow's path is more integrated; PyTorch's is more mix-and-match.

No. TorchScript is in maintenance mode, and the PyTorch team now points to torch.export for capturing full graphs. Exported programs can feed ExecuTorch for edge devices or AOTInductor for server-side compiled artifacts. Many teams also export to ONNX for runtimes like ONNX Runtime or TensorRT.