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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 would rate this product nine out of ten."
"The solution is easy to use compared to other frameworks like PyTorch and TensorFlow."
"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."
"There are numerous libraries available, and the documentation is rich and step-by-step, helping us understand which model to use in particular conditions."
"My preferred aspects are natural language processing and question-answering."
"I like that Hugging Face is versatile in the way it has been developed."
"It is stable."
"Hugging Face provides open-source models, making it the best open-source and reliable solution."
"The tool is very user-friendly."
"The framework of the solution is valuable."
"It's been pretty scalable in terms of using multiple GPUs."
"PyTorch is developer-friendly, allowing developers to continuously create new projects."
"It’s reliable, secure and user-friendly. It allows you to develop any AIML project efficiently. PySearch is the best option for developing any project in the AIML domain. The product is easy to install."
"For me, the product's initial setup phase is easy...For beginners, it is fairly easy to learn."
"The product's initial setup phase is easy."
"PyTorch allows me to build my projects from scratch."
 

Cons

"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."
"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."
"Access to the models and datasets could be improved. Many interesting ones are restricted."
"Initially, I faced issues with the solution's configuration."
"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."
"It can incorporate AI into its services."
"Regarding scalability, I'm finding the multi-GPU aspect of it challenging."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"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."
"I do not have any complaints."
"The analyzing and latency of compiling could be improved to provide enhanced results."
"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."
"On the production side of things, having more frameworks would be helpful."
"The training of the models could be faster."
 

Pricing and Cost Advice

"So, it's requires expensive machines to open services or open LLM models."
"Hugging Face is an open-source solution."
"The solution is open source."
"We do not have to pay for the product."
"I recall seeing a fee of nine dollars, and there's also an enterprise option priced at twenty dollars per month."
"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."
"It is free."
"It is free."
"The solution is affordable."
"PyTorch is open-sourced."
"PyTorch is an open-source solution."
"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.
844,944 professionals have used our research since 2012.