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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

"PyTorch allows me to build my projects from scratch."
"The framework of the solution is valuable."
"I like PyTorch's scalability."
"PyTorch is developer-friendly, allowing developers to continuously create new projects."
"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."
"We use PyTorch libraries, which are working well. It's very easy."
"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."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"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."
"Our clients were not aware they were using TensorFlow, so that aspect was transparent. I think we personally chose TensorFlow because it provided us with more of the end-to-end package that you can use for all the steps regarding billing and our models. So basically data processing, training the model, evaluating the model, updating the model, deploying the model and all of these steps without having to change to a new environment."
"TensorFlow provides Insights into both data and machine learning strategies."
"The most valuable feature of TensorFlow is deep learning. It is the best tool for deep learning in the market."
"TensorFlow improves my organization because our clients get a lot of investment from their investors and we are progressively improving the products. Every six months we release new features."
"The available documentation is extensive and helpful."
"It is easy to use and learn."
 

Cons

"I do not have any complaints."
"The training of the models could be faster."
"The product has breakdowns when we change the versions a lot."
"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."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
"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 analyzing and latency of compiling could be improved to provide enhanced results."
"The product has certain shortcomings in the automation of machine learning."
"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."
"It currently offers inbuilt functions, however, having the ability to implement custom libraries would enhance its usefulness for enterprise-level applications."
"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."
"Personally, I find it to be a bit too much AI-oriented."
"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."
"The process of creating models could be more user-friendly."
"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."
"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."
 

Pricing and Cost Advice

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