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PyTorch vs TensorFlow 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

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
TensorFlow
Ranking in AI Development Platforms
6th
Average Rating
8.8
Reviews Sentiment
7.4
Number of Reviews
20
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of April 2025, in the AI Development Platforms category, the mindshare of PyTorch is 1.4%, down from 1.5% compared to the previous year. The mindshare of TensorFlow is 3.8%, down from 7.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

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.
Ashish Upadhyay - PeerSpot reviewer
A robust tools for model visualization and debugging with superior scalability and stability, and an intuitive user-friendly interface
The one feature we find most valuable at our company is its robust and flexible machine-learning capabilities. It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions. The ability to develop and fine-tune models, such as risk assessment for detection and market protection, as well as the creation of recommendation systems, is paramount. This versatility extends to providing personalized identity-relevant applications for our enterprise clients, delivering valuable insights to the market. Its exceptional support for deep learning and its efficient resource utilization enable us to undertake complex financial and data analyses. The flexibility it provides is crucial for meeting industrial requirements and crafting solutions tailored to our client's specific needs.

Quotes from Members

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

Pros

"The product's initial setup phase is easy."
"I like PyTorch's scalability."
"It's been pretty scalable in terms of using multiple GPUs."
"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."
"For me, the product's initial setup phase is easy...For beginners, it is fairly easy to learn."
"The framework of the solution is valuable."
"We use PyTorch libraries, which are working well. It's very easy."
"PyTorch is developer-friendly, allowing developers to continuously create new projects."
"It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models."
"TensorFlow is an efficient product for building neural networks."
"TensorFlow is a framework that makes it really easy to use for deep learning."
"The most valuable features are the frameworks and the functionality to work with different data, even when we have a certain quantity of data flowing."
"The available documentation is extensive and helpful."
"TensorFlow is easy to implement and offers inbuilt functions for various tasks."
"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."
"It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it."
 

Cons

"The product has breakdowns when we change the versions a lot."
"I do not have any complaints."
"The analyzing and latency of compiling could be improved to provide enhanced results."
"The product has certain shortcomings in the automation of machine learning."
"The training of the models could be faster."
"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."
"Personally, I find it to be a bit too much AI-oriented."
"For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort."
"It doesn't allow for fast the proto-typing. So usually when we do proto-typing we will start with PyTorch and then once we have a good model that we trust, we convert it into TensorFlow. So definitely, TensorFlow is not very flexible."
"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."
"However, if I want to change just one thing in the implementation of TensorFlow functions I have to copy everything that they wrote and I change it manually if indeed it can be amended. This is really hard as it's written in C++ and has a lot of complications."
"We encountered version mismatch errors while using the product."
"I know this is out of the scope of TensorFlow, however, every time I've sent a request, I had to renew the model into RAM and they didn't make that prediction or inference. This makes the point for the request that much longer. If they could provide anything to help in this part, it will be very great."
"It currently offers inbuilt functions, however, having the ability to implement custom libraries would enhance its usefulness for enterprise-level applications."
 

Pricing and Cost Advice

"PyTorch is open source."
"The solution is affordable."
"PyTorch is an open-source solution."
"It is free."
"It is free."
"PyTorch is open-sourced."
"I did not require a license for this solution. It a free open-source solution."
"It is open-source software. You don't have to pay all the big bucks like Azure and Databricks."
"I rate TensorFlow's pricing a five out of ten."
"TensorFlow is free."
"We are using the free version."
"It is an open-source solution, so anyone can use it free of charge."
"The solution is free."
"I think for learners to deploy a project, you can actually use TensorFlow for free. It's just amazing to have an open-source platform like TensorFlow to deploy your own project. Here in Russia no one really cares about licenses, as it is totally open source and free. My clients in the United States were also pleased to learn when they enquired, that licensing is free."
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Top Industries

By visitors reading reviews
Manufacturing Company
32%
Computer Software Company
9%
Financial Services Firm
8%
University
8%
Manufacturing Company
14%
Computer Software Company
13%
University
9%
Educational Organization
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

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....
What do you like most about TensorFlow?
It empowers us to seamlessly create and deploy machine learning models, offering a versatile solution for implementing sophisticated environments and various types of AI solutions.
What is your experience regarding pricing and costs for TensorFlow?
I am not familiar with the pricing setup cost and licensing.
What needs improvement with TensorFlow?
Providing more control by allowing users to build custom functions would make TensorFlow a better option. It currently offers inbuilt functions, however, having the ability to implement custom libr...
 

Comparisons

 

Overview

 

Sample Customers

Information Not Available
Airbnb, NVIDIA, Twitter, Google, Dropbox, Intel, SAP, eBay, Uber, Coca-Cola, Qualcomm
Find out what your peers are saying about PyTorch vs. TensorFlow and other solutions. Updated: March 2025.
845,040 professionals have used our research since 2012.