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Google Cloud AI Platform vs PyTorch comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Google Cloud AI Platform
Ranking in AI Development Platforms
7th
Average Rating
7.8
Number of Reviews
8
Ranking in other categories
No ranking in other categories
PyTorch
Ranking in AI Development Platforms
8th
Average Rating
8.6
Reviews Sentiment
6.4
Number of Reviews
10
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the AI Development Platforms category, the mindshare of Google Cloud AI Platform is 7.3%, down from 7.5% compared to the previous year. The mindshare of PyTorch is 1.5%, down from 2.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Vipul-Kumar - PeerSpot reviewer
An AI platform AI Platform to train your machine learning models at scale, to host your trained model in the cloud, and to use your model to make predictions about new data
I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite.
Arucy Lionel - PeerSpot reviewer
Offers good backward compatible and simple to use
One of the things I really like about PyTorch is that it doesn't break with every update or deletion. That's why I switched from TensorFlow to PyTorch. I can still run the code I wrote three years ago in PyTorch on the latest version. It's very backward compatible, and it's also very simple to use. It's not overly technical, and the flow is pretty intuitive. And now that PyTorch 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.

Quotes from Members

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

Pros

"Since the model could be trained in just a couple of hours and deploying it took only a few minutes, the entire process took less than an hour."
"The initial setup is very straightforward."
"The platform's Google Vision API is particularly valuable."
"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."
"A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up with an operational solution really quick."
"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."
"On GCP, we are exposing our API services to our clients so that they send us their information. It can be single individual records or it can be a batch of their clients."
"The solution is able to read 90% of the documents correctly with a 10% error rate."
"We use PyTorch libraries, which are working well. It's very easy."
"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 framework of the solution is valuable."
"The product's initial setup phase is easy."
"For me, the product's initial setup phase is easy...For beginners, it is fairly easy to learn."
"It's been pretty scalable in terms of using multiple GPUs."
"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."
"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."
 

Cons

"It could be more clear, and sometimes there are errors that I don't quite understand."
"One thing that I found is that Azure ML does not directly provide you with features on Google Cloud AI Platform, whereas Vertex provides some features of the platform."
"I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite."
"The initial setup was straightforward for me but could be difficult for others."
"Improvements in text extraction accuracy and pricing adjustments would be helpful."
"At first, there were only the user-managed rules to identify the best attributes of the individual. Then, we came up with a truth set and developed different machine learning models with the help of that truth set, so now it's completely machine learning."
"Customizations are very difficult, and they take time."
"The solution can be improved by simplifying the process to make your own models."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
"The product has certain shortcomings in the automation of machine learning."
"The training of the models could be faster."
"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."
"I would like to see better learning documents."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"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."
"On the production side of things, having more frameworks would be helpful."
 

Pricing and Cost Advice

"For every thousand uses, it is about four and a half euros."
"The price of the solution is competitive."
"The solution has an attractive starting program, which costs only 300 USD for a duration of three months. During this period, one can accomplish a lot of work on the solution."
"The licenses are cheap."
"The pricing is on the expensive side."
"PyTorch is open source."
"PyTorch is an open-source solution."
"It is free."
"PyTorch is open-sourced."
"It is free."
"The solution is affordable."
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Top Industries

By visitors reading reviews
Computer Software Company
15%
Financial Services Firm
11%
Manufacturing Company
10%
University
9%
Manufacturing Company
30%
Computer Software Company
11%
Healthcare Company
8%
University
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Google Cloud AI Platform?
A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up...
What is your primary use case for Google Cloud AI Platform?
We use Google Cloud AI Platform to extract text from images, such as forms.
What needs improvement with PyTorch?
We faced an issue with PyTorch due to version incompatibility. PyTorch has no latest version after v12.3. We also faced a few version compatibility issues with CUDA drivers.
 

Learn More

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Overview

 

Sample Customers

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Find out what your peers are saying about Google Cloud AI Platform vs. PyTorch and other solutions. Updated: October 2024.
816,660 professionals have used our research since 2012.