Try our new research platform with insights from 80,000+ expert users

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

"It's been pretty scalable in terms of using multiple GPUs."
"yTorch is gaining credibility in the research space, it's becoming easier to find examples of papers that use PyTorch. This is an advantage for someone who uses PyTorch primarily."
"For me, the product's initial setup phase is easy...For beginners, it is fairly easy to learn."
"I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code."
"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."
"The tool is very user-friendly."
"The framework of the solution is valuable."
"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."
"It is also totally Open-Source and free. Open-source applications are not good usually. but TensorFlow actually changed my view about it and I thought, "Look, Oh my God. This is an open-source application and it's as good as it could be." I learned that TensorFlow, by sharing their own knowledge and their own platform with other developers, it improved the lives of many people around the globe."
"TensorFlow is a framework that makes it really easy to use for deep learning."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"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."
"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."
"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."
"It's got quite a big community, which is useful."
"The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market."
 

Cons

"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."
"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."
"We faced an issue with PyTorch due to version incompatibility. PyTorch has no latest version after v12.3."
"I do not have any complaints."
"The training of the models could be faster."
"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."
"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."
"Enhancements could include increasing use cases and improving the accuracy of previously built models in TensorFlow. For instance, when we run certain models, the computing power of laptops becomes high."
"It currently offers inbuilt functions, however, having the ability to implement custom libraries would enhance its usefulness for enterprise-level applications."
"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."
"The solution is hard to integrate with the GPUs."
"In terms of improvement, we always look for ways they can optimize the model, accelerate the speed and the accuracy, and how can we optimize with our different techniques. There are various techniques available in TensorFlow. Maintaining accuracy is an area they should work on."
"For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort."
 

Pricing and Cost Advice

"It is free."
"PyTorch is open source."
"The solution is affordable."
"It is free."
"PyTorch is an open-source solution."
"PyTorch is open-sourced."
"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."
"I am using the open-source version of TensorFlow and it is free."
"TensorFlow is free."
"It is an open-source solution, so anyone can use it free of charge."
"The solution is free."
"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."
"We are using the free version."
report
Use our free recommendation engine to learn which AI Development Platforms solutions are best for your needs.
844,944 professionals have used our research since 2012.
 

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.
844,944 professionals have used our research since 2012.