Software · head to head
PyTorch vs Keras
PyTorch
Software
Deep learning framework with dynamic computation graphs
- From
- Free
- Rated
- -
The short version
- Each has a real cost: PyTorch dynamic computation graph can be less efficient for production inference than static graphs; Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
- They diverge on capability: PyTorch covers Dynamic computation graphs, Keras covers Sequential and Functional API.
Where they differ
Only the attributes on which PyTorch and Keras actually diverge.
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Unknown).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in PyTorch
- Dynamic computation graphs
- Automatic differentiation
- GPU acceleration
- Distributed training
- TorchScript
- TorchVision
- TorchText
- TorchAudio
Only in Keras
- Sequential and Functional API
- Pre-built neural network layers
- Model training and evaluation
- Transfer learning
- Model serialization
- TensorFlow
- JAX
- PyTorch
Both cover
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
PyTorch
- Machine learning
- Data analysis
- Model training
- Predictive analytics
Keras
- Machine learning
- Data analysis
- Model training
- Predictive analytics
Both are used for machine learning, data analysis, model training, predictive analytics, on those jobs the choice comes down to price and fit rather than capability.
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
PyTorch
- Dynamic computation graph can be less efficient for production inference than static graphs
- Requires more manual code for distributed training compared to some alternatives
- Documentation focused heavily on research use cases rather than production deployment
Keras
- Limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
- Error messages can be vague and unhelpful, making debugging challenging
- Smaller ecosystem and fewer pre-trained models than TensorFlow or PyTorch
Pricing, plan by plan
PyTorch
FreeNo published plan breakdown. See the PyTorch review.
Keras
Free- Open SourceFree
- High-level API
- Pre-built layers
- Model serialization
Which should you pick?
Choose PyTorch if
- You need dynamic computation graphs.
- You want to start without paying.
- You work on Linux, Windows, macOS.
- You also want automatic differentiation.
Choose Keras if
- You need sequential and functional api.
- You want to start without paying.
- You work on Python, Google Colab, Jupyter.
- You also want pre-built neural network layers.
Questions people ask
- Is PyTorch or Keras better?
- Neither clearly leads. PyTorch starts at Free and Keras at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, PyTorch or Keras?
- PyTorch starts at Free and Keras at Free.
- Does PyTorch or Keras run on more platforms?
- PyTorch runs on Linux, Windows, macOS. Keras runs on Python, Google Colab, Jupyter.
- Can I use PyTorch for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is PyTorch best used for?
- PyTorch is most often used for machine learning, data analysis, model training, predictive analytics.
- What can PyTorch do that Keras cannot?
- PyTorch covers Dynamic computation graphs, Automatic differentiation, GPU acceleration, Distributed training. Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning. Both handle Linux support, Mac support, Windows support.
Answered from the vendors’ own pages
PyTorch: Is PyTorch free and open source?
Yes. PyTorch is an open source machine learning framework that is completely free to use. It was originally created and open-sourced by Facebook (now Meta) in 2016.
SourceKeras: What is Keras?
Keras is a high-level deep learning API built on top of TensorFlow that simplifies building and training neural networks. Keras 3 supports multiple backends including TensorFlow, PyTorch, and JAX, making it backend-agnostic.
SourcePyTorch: What platforms does PyTorch support?
PyTorch supports Linux, Windows, and macOS. It provides strong GPU acceleration through CUDA and other backends for high-performance computing.
SourceKeras: What model architectures does Keras support?
Keras supports the Sequential model for linear stacks of layers, the Functional API for arbitrary graph architectures, and model subclassing for custom implementations. All approaches provide access to layers, optimizers, metrics, and callbacks.
SourcePyTorch: Can I use PyTorch for production deployments?
Yes. PyTorch provides graph-based execution, distributed training, mobile deployment, and quantization features to support production deployments.
SourceKeras: Can Keras models run on TPUs and GPUs?
Yes, Keras models can run on TPU Pods or large GPU clusters, be exported to run in browsers or on mobile devices, and be served via web APIs.
SourceKeras: Does Keras offer pre-trained models?
Yes, Keras provides pre-trained models through KerasHub and Keras Applications for common deep learning tasks like image classification, object detection, and NLP.
SourceKeras: Who should use Keras?
Keras is ideal for beginners and rapid prototyping due to its simplicity and user-friendly interface. Advanced users and production deployments may benefit more from lower-level frameworks like TensorFlow or PyTorch for greater customization.
Source