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

Google Cloud AI Platform vs PyTorch 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

Google Cloud AI Platform
Ranking in AI Development Platforms
8th
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
9
Ranking in other categories
No ranking in other categories
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
 

Mindshare comparison

As of April 2025, in the AI Development Platforms category, the mindshare of Google Cloud AI Platform is 4.3%, down from 6.6% compared to the previous year. The mindshare of PyTorch is 1.4%, down from 1.5% 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.
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.

Quotes from Members

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

Pros

"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."
"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."
"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."
"The platform's Google Vision API is particularly valuable."
"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 solution is able to read 90% of the documents correctly with a 10% error rate."
"The feedback left about these tools was really helpful and informative for us"
"The initial setup is very straightforward."
"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."
"The tool is very user-friendly."
"The framework of the solution is valuable."
"PyTorch is developer-friendly, allowing developers to continuously create new projects."
"PyTorch is developer-friendly, allowing developers to continuously create new projects."
"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

"The solution can be improved by simplifying the process to make your own models."
"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."
"The initial setup was straightforward for me but could be difficult for others."
"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 model management on Google Cloud AI Platform could be better."
"Improvements in text extraction accuracy and pricing adjustments would be helpful."
"Customizations are very difficult, and they take time."
"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."
"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."
"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."
"I would like to see better learning documents."
"I do not have any complaints."
"The product has certain shortcomings in the automation of machine learning."
"We faced an issue with PyTorch due to version incompatibility. PyTorch has no latest version after v12.3."
"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."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
 

Pricing and Cost Advice

"For every thousand uses, it is about four and a half euros."
"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."
"The price of the solution is competitive."
"PyTorch is an open-source solution."
"It is free."
"PyTorch is open-sourced."
"The solution is affordable."
"It is free."
"PyTorch is open source."
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
Computer Software Company
16%
Financial Services Firm
11%
Manufacturing Company
9%
University
8%
Manufacturing Company
32%
Computer Software Company
9%
Financial Services Firm
8%
University
8%
 

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 experience regarding pricing and costs for Google Cloud AI Platform?
For the most part, the pricing is perfect sinceit grows with the use of my app. In some cases, they could be more specific about the pricing, especially for some AI features.
What is your primary use case for Google Cloud AI Platform?
I use Google Cloud AI Platform due to Firebase and the many APIs that are available with it.
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....
 

Overview

 

Sample Customers

Carousell
Information Not Available
Find out what your peers are saying about Google Cloud AI Platform vs. PyTorch and other solutions. Updated: March 2025.
844,944 professionals have used our research since 2012.