Development and Support: Developed by Facebook's AI Research lab and supported by Facebook.
Ecosystem: Has a growing ecosystem but is generally considered to be more research-friendly, with a focus on flexibility and speed.
Deployment: Historically lagged behind TensorFlow in deployment-ready solutions, but the gap is closing with the introduction of TorchServe for model serving.
Dynamic Computation Graph: Known for its dynamic computation graph ("define by run"), which allows for more flexibility in building complex architectures and for the graph to change with every iteration.
APIs: Has a cleaner and more pythonic API, often considered to be more user-friendly, particularly for beginners and for rapid development.
Performance: May have performance trade-offs due to dynamic graphs, but these are often negligible, and it's continually being optimized.