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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
5th
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
8th
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 February 2025, in the AI Development Platforms category, the mindshare of Hugging Face is 13.2%, up from 6.6% compared to the previous year. The mindshare of PyTorch is 1.2%, down from 1.9% 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.
Jithin James - PeerSpot reviewer
User-friendly, easy to learn, performs well, and is more advanced than other tools
The most valuable feature would be the solution’s performance. The product is more advanced than the other libraries that I have used. Since every functionality is production-ready, I can easily write code. I don't have to rewrite the code for production. It has production-ready code from the start. The tool is very user-friendly. It took us a week to learn how to use it. It's straightforward to learn.

Quotes from Members

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

Pros

"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."
"I like that Hugging Face is versatile in the way it has been developed."
"The tool's most valuable feature is that it shows trending models. All the new models, even Google's demo models, appear at the top. You can find all the open-source models in one place. You can use them directly and easily find their documentation. It's very simple to find documentation and write code. If you want to work with AI and machine learning, Hugging Face is a perfect place to start."
"The most valuable features are the inference APIs as it takes me a long time to run inferences on my local machine."
"The product is reliable."
"Hugging Face provides open-source models, making it the best open-source and reliable solution."
"The tool's most valuable feature is that it's open-source and has hundreds of packages already available. This makes it quite helpful for creating our LLMs."
"I would rate this product nine out of ten."
"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 is developer-friendly, allowing developers to continuously create new projects."
"For me, the product's initial setup phase is easy...For beginners, it is fairly easy to learn."
"I like PyTorch's scalability."
"PyTorch is developer-friendly, allowing developers to continuously create new projects."
"The framework of the solution is valuable."
"PyTorch allows me to build my projects from scratch."
"We use PyTorch libraries, which are working well. It's very easy."
 

Cons

"Implementing a cloud system to showcase historical data would be beneficial."
"I've worked on three projects using Hugging Face, and only once did we encounter a problem with the code. We had to use another open-source embedding from OpenAI to resolve it. Our team has three members: me, my colleague, and a team leader. We looked at the problem and resolved it."
"Most people upload their pre-trained models on Hugging Face, but more details should be added about the models."
"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."
"Access to the models and datasets could be improved. Many interesting ones are restricted."
"Access to the models and datasets could be improved."
"Regarding scalability, I'm finding the multi-GPU aspect of it challenging."
"Initially, I faced issues with the solution's configuration."
"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."
"On the production side of things, having more frameworks would be helpful."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
"The product has certain shortcomings in the automation of machine learning."
"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."
"The training of the models could be faster."
"The product has breakdowns when we change the versions a lot."
 

Pricing and Cost Advice

"So, it's requires expensive machines to open services or open LLM models."
"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."
"The solution is open source."
"We do not have to pay for the product."
"Hugging Face is an open-source solution."
"I recall seeing a fee of nine dollars, and there's also an enterprise option priced at twenty dollars per month."
"PyTorch is open source."
"The solution is affordable."
"It is free."
"PyTorch is an open-source solution."
"It is free."
"PyTorch is open-sourced."
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Top Industries

By visitors reading reviews
Computer Software Company
11%
Manufacturing Company
11%
University
10%
Financial Services Firm
10%
Manufacturing Company
30%
Computer Software Company
10%
Healthcare Company
8%
Educational Organization
7%
 

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?
The analyzing and latency of compiling could be improved to provide enhanced results.
 

Comparisons

 

Overview

Find out what your peers are saying about Hugging Face vs. PyTorch and other solutions. Updated: January 2025.
837,501 professionals have used our research since 2012.