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Hugging Face vs PyTorch comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 4, 2024

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Categories and Ranking

Hugging Face
Ranking in AI Development Platforms
4th
Average Rating
8.2
Reviews Sentiment
7.0
Number of Reviews
12
Ranking in other categories
No ranking in other categories
PyTorch
Ranking in AI Development Platforms
7th
Average Rating
8.6
Reviews Sentiment
7.2
Number of Reviews
13
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2025, in the AI Development Platforms category, the mindshare of Hugging Face is 13.5%, up from 7.6% compared to the previous year. The mindshare of PyTorch is 1.4%, down from 1.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

SwaminathanSubramanian - PeerSpot reviewer
Versatility empowers AI concept development despite the multi-GPU challenge
Regarding scalability, I'm finding the multi-GPU aspect of it challenging. Training the model is another hurdle, although I'm only getting into that aspect currently. Organizations are apprehensive about investing in multi-GPU setups. Additionally, data cleanup is a challenge that needs to be resolved, as data must be mature and pristine.
Rohan Sharma - PeerSpot reviewer
Enabled creation of innovative projects through developer-friendly features
The aspect I like most about PyTorch is that it is really developer-friendly. Developers can constantly create new things, and everyone around the world can use it for free because it's an open-source product. What I personally like is that PyTorch has enabled users to use Apple's M1 chip natively for GPU users. Unlike other libraries using CUDA, PyTorch utilizes Metal Performance Shaders (MPS) to enable GPU usage on M1 chips.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"I appreciate the versatility and the fact that it has generalized many models."
"The most valuable features are the inference APIs as it takes me a long time to run inferences on my local machine."
"I would rate this product nine out of ten."
"My preferred aspects are natural language processing and question-answering."
"I like that Hugging Face is versatile in the way it has been developed."
"The solution is easy to use compared to other frameworks like PyTorch and TensorFlow."
"What I find the most valuable about Hugging Face is that I can check all the models on it and see which ones have the best performance without using another platform."
"Hugging Face provides open-source models, making it the best open-source and reliable solution."
"PyTorch is developer-friendly, allowing developers to continuously create new projects."
"Its interface is the most valuable. The ability to have an interface to train machine learning models and construct them with the high-level interface, without excess busting and reconstructing the same technical elements, is very useful."
"PyTorch allows me to build my projects from scratch."
"It's been pretty scalable in terms of using multiple GPUs."
"For me, the product's initial setup phase is easy...For beginners, it is fairly easy to learn."
"We use PyTorch libraries, which are working well. It's very easy."
"The tool is very user-friendly."
"PyTorch is developer-friendly, allowing developers to continuously create new projects."
 

Cons

"I believe Hugging Face has some room for improvement. There are some security issues. They provide code, but API tokens aren't indicated. Also, the documentation for particular models could use more explanation. But I think these things are improving daily. The main change I'd like to see is making the deployment of inference endpoints more customizable for users."
"The solution must provide an efficient LLM."
"Access to the models and datasets could be improved."
"It can incorporate AI into its services."
"Most people upload their pre-trained models on Hugging Face, but more details should be added about the models."
"Hugging Face could improve by implementing a search engine or chat bot feature similar to ChatGPT."
"Initially, I faced issues with the solution's configuration."
"Access to the models and datasets could be improved. Many interesting ones are restricted."
"I would like a model to be available. I think Google recently released a new version of EfficientNet. It's a really good classifier, and a PyTorch implementation would be nice."
"PyTorch needs improvement in working on ARM-based chips. They have unified memory for GPU and RAM, however, current GPUs used for processing are slow."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"On the production side of things, having more frameworks would be helpful."
"I do not have any complaints."
"PyTorch could make certain things more obvious. Even though it does make things like defining loss functions and calculating gradients in backward propagation clear, these concepts may confuse beginners. We find that it's kind of problematic. Despite having methods called on loss functions during backward passes, the oral documentation for beginners is quite complex."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
"I would like to see better learning documents."
 

Pricing and Cost Advice

"The solution is open source."
"The tool is open-source. The cost depends on what task you're doing. If you're using a large language model with around 12 million parameters, it will cost more. On average, Hugging Face is open source so you can download models to your local machine for free. For deployment, you can use any cloud service."
"I recall seeing a fee of nine dollars, and there's also an enterprise option priced at twenty dollars per month."
"So, it's requires expensive machines to open services or open LLM models."
"We do not have to pay for the product."
"Hugging Face is an open-source solution."
"PyTorch is open-sourced."
"It is free."
"PyTorch is an open-source solution."
"It is free."
"The solution is affordable."
"PyTorch is open source."
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Top Industries

By visitors reading reviews
Computer Software Company
11%
Financial Services Firm
11%
University
10%
Manufacturing Company
10%
Manufacturing Company
32%
Computer Software Company
9%
Financial Services Firm
8%
University
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Hugging Face?
My preferred aspects are natural language processing and question-answering.
What needs improvement with Hugging Face?
Access to the models and datasets could be improved. Many interesting ones are restricted. It would be great if they provided access for students or non-professionals who just want to test things.
What is your primary use case for Hugging Face?
This is a simple personal project, non-commercial. As a student, that's all I do.
What is your experience regarding pricing and costs for PyTorch?
I haven't gone for a paid plan yet. I've just been using the free trial or open-source version.
What needs improvement with PyTorch?
PyTorch needs improvement in working on ARM-based chips. Although they have unified memory for GPU and RAM, they are unable to utilize these GPUs for processing efficiently. They take so much time....
 

Comparisons

 

Overview

Find out what your peers are saying about Hugging Face vs. PyTorch and other solutions. Updated: March 2025.
845,040 professionals have used our research since 2012.