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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
8th
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 February 2025, in the AI Development Platforms category, the mindshare of PyTorch is 1.2%, down from 1.9% compared to the previous year. The mindshare of TensorFlow is 3.9%, down from 8.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

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.
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

"We use PyTorch libraries, which are working well. It's very easy."
"For me, the product's initial setup phase is easy...For beginners, it is fairly easy to learn."
"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."
"I like PyTorch's scalability."
"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."
"The tool is very user-friendly."
"PyTorch allows me to build my projects from scratch."
"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."
"TensorFlow is easy to implement and offers inbuilt functions for various tasks."
"The available documentation is extensive and helpful."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"TensorFlow is an efficient product for building neural networks."
"TensorFlow provides Insights into both data and machine learning strategies."
"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."
"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."
"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."
 

Cons

"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."
"The training of the models could be faster."
"I would like to see better learning documents."
"The analyzing and latency of compiling could be improved to provide enhanced results."
"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 product has certain shortcomings in the automation of machine learning."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"We faced an issue with PyTorch due to version incompatibility. PyTorch has no latest version after v12.3."
"Personally, I find it to be a bit too much AI-oriented."
"TensorFlow deep learning takes a lot of computation power. The more systems you can use, the easier it is. That's a good ability, if you can make a system run immediately at the same time on the same task, it's much faster rather than you having one system running which is slower. Running systems in parallel is a complex situation, but it can improve. There is a lot of work involved."
"There are a lot of problems, such as integrating our custom code. In my experience model tuning has been a bit difficult to edit and tune the graph model for best performance. We have to go into the model but we do not have a model viewer for quick access."
"There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves."
"The solution is hard to integrate with the GPUs."
"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."
"For newcomers to the field, the learning curve can be steep, often requiring about a year of dedicated effort."
"It would be cool if TensorFlow could make it easier for companies like us to program for running it across different hyperscalers."
 

Pricing and Cost Advice

"It is free."
"The solution is affordable."
"PyTorch is open-sourced."
"PyTorch is an open-source solution."
"PyTorch is open source."
"It is free."
"It is open-source software. You don't have to pay all the big bucks like Azure and Databricks."
"I did not require a license for this solution. It a free open-source solution."
"The solution is free."
"I am using the open-source version of TensorFlow and it 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."
"We are using the free version."
"I rate TensorFlow's pricing a five out of ten."
"It is an open-source solution, so anyone can use it free of charge."
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Top Industries

By visitors reading reviews
Manufacturing Company
30%
Computer Software Company
10%
Healthcare Company
8%
Educational Organization
7%
Manufacturing Company
15%
Computer Software Company
12%
University
10%
Educational Organization
9%
 

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?
The analyzing and latency of compiling could be improved to provide enhanced results.
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: January 2025.
837,501 professionals have used our research since 2012.