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

TensorFlow pros and cons

Vendor: TensorFlow
4.4 out of 5

Pros & Cons summary

Buyer's Guide

Get pricing advice, tips, use cases and valuable features from real users of this product.
Get the report

Prominent pros & cons

PROS

TensorFlow provides a transparent and end-to-end package for data processing, model training, evaluation, updating, and deployment without switching environments.
It is open-source, free, and supported by an extensive community and comprehensive Google documentation.
TensorFlow offers robust deep learning capabilities and efficiencies for building neural networks.
The framework is flexible, supporting both cluster computing and mobile devices, enabling diverse AI solutions.
TensorFlow's extensive documentation and easy implementation with inbuilt functions enhance its usability for various tasks.

CONS

TensorFlow lacks flexibility in prototyping compared to PyTorch.
Customization is challenging due to its C++ implementation and lacks efficient integration with JavaScript.
Heavy computational power is required, making local machine usage expensive and difficult especially without cloud resources.
There are difficulties integrating custom code and adapting to model tuning, with no model viewer for performance adjustment.
Users frequently face version mismatch errors, and the learning curve for new users is notably steep.
 

TensorFlow Pros review quotes

BI
Computer Vision Engineer at Innopolis University
Nov 17, 2020
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.
JB
Data Scientist at UpWork Freelancer
Nov 21, 2020
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.
HL
AI Expert at lums
Nov 28, 2020
TensorFlow is a framework that makes it really easy to use for deep learning.
Learn what your peers think about TensorFlow. Get advice and tips from experienced pros sharing their opinions. Updated: January 2026.
881,082 professionals have used our research since 2012.
AI
Project Manager at INFOCOM Ltd
Nov 29, 2020
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.
JM
Managing Director at Geeky Bee AI
Nov 29, 2020
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.
GB
Machine Learning Engineer, AI Consultant at intelligentbusiness.hu
Jan 5, 2023
Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training.
GY
Machine Learning Engineer at Upwork
Dec 24, 2020
Google is behind TensorFlow, and they provide excellent documentation. It's very thorough and very helpful.
reviewer1518303 - PeerSpot reviewer
Chief Technology Officer at a tech services company with 51-200 employees
Mar 5, 2021
It's got quite a big community, which is useful.
reviewer1540461 - PeerSpot reviewer
Data Scientist at a university with 5,001-10,000 employees
Mar 29, 2021
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.
ZM
Machine Learning Software Developer at freelancer
Jul 9, 2021
Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers.
 

TensorFlow Cons review quotes

BI
Computer Vision Engineer at Innopolis University
Nov 17, 2020
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.
JB
Data Scientist at UpWork Freelancer
Nov 21, 2020
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.
HL
AI Expert at lums
Nov 28, 2020
JavaScript is a different thing and all the websites and web apps and all the mobile apps are built-in JavaScript. JavaScript is the core of that. However, TensorFlow is like a machine learning item. What can be improved with TensorFlow is how it can mix in how the JavaScript developers can use TensorFlow.
Learn what your peers think about TensorFlow. Get advice and tips from experienced pros sharing their opinions. Updated: January 2026.
881,082 professionals have used our research since 2012.
AI
Project Manager at INFOCOM Ltd
Nov 29, 2020
There are connection issues that interrupt the download needed for the data sets. We need to prepare them ourselves.
JM
Managing Director at Geeky Bee AI
Nov 29, 2020
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.
GB
Machine Learning Engineer, AI Consultant at intelligentbusiness.hu
Jan 5, 2023
It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models.
GY
Machine Learning Engineer at Upwork
Dec 24, 2020
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.
reviewer1518303 - PeerSpot reviewer
Chief Technology Officer at a tech services company with 51-200 employees
Mar 5, 2021
Personally, I find it to be a bit too much AI-oriented.
reviewer1540461 - PeerSpot reviewer
Data Scientist at a university with 5,001-10,000 employees
Mar 29, 2021
It would be nice to have more pre-trained models that we can utilize within layers. I utilize a Mac, and I am unable to utilize AMD GPUs. That's something that I would definitely be like to be able to access within TensorFlow since most of it is with CUDA ML. This only matters for local machines because, in Azure, you can just access any GPU you want from the cloud. It doesn't really matter, but the clients that I work with don't have cloud accounts, or they don't want to utilize that or spend the money. They all see it as too expensive and want to know what they can do on their local machines.
ZM
Machine Learning Software Developer at freelancer
Jul 9, 2021
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.