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Google Vertex AI 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 Vertex AI
Ranking in AI Development Platforms
2nd
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
10
Ranking in other categories
AI Infrastructure (1st)
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 June 2025, in the AI Development Platforms category, the mindshare of Google Vertex AI is 12.5%, down from 21.2% compared to the previous year. The mindshare of PyTorch is 1.6%, up from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Rajeev-Dave - PeerSpot reviewer
Streamlined machine learning with valuable models and good integration
We are using Google Vertex AI for various purposes, especially various chatbots for customer engineers and customers. Additionally, we are working on projects involving AI and machine learning Features like the LLM model are valuable, and we are using Google Vertex AI for these purposes.…
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

"The integration of AutoML features streamlines our machine-learning workflows."
"The most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
"The most valuable features of the solution are that it is quite flexible, and some of the services are almost low-code, with no-code services, so it gives agents flexibility to build the use cases according to the operational needs."
"Vertex comes with inbuilt integration with GCP for data storage."
"We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently."
"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."
"It's been pretty scalable in terms of using multiple GPUs."
"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."
"I like PyTorch's scalability."
"PyTorch is developer-friendly, allowing developers to continuously create new projects."
"PyTorch allows me to build my projects from scratch."
"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."
"We use PyTorch libraries, which are working well. It's very easy."
 

Cons

"The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time."
"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow."
"The tool's documentation is not good. It is hard."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process."
"I'm not sure if I have suggestions for improvement."
"I think the technical documentation is not readily available in the tool."
"The training of the models could be faster."
"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."
"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."
"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."
"We faced an issue with PyTorch due to version incompatibility. PyTorch has no latest version after v12.3."
"On the production side of things, having more frameworks would be helpful."
 

Pricing and Cost Advice

"The Versa AI offers attractive pricing. With this pricing structure, I can leverage various opportunities to bring value to my business. It's a positive aspect worth considering."
"The solution's pricing is moderate."
"I think almost every tool offers a decent discount. In terms of credits or other stuff, every cloud provider provides a good number of incentives to onboard new clients."
"The price structure is very clear"
"PyTorch is an open-source solution."
"It is free."
"The solution is affordable."
"It is free."
"PyTorch is open-sourced."
"PyTorch is open source."
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Top Industries

By visitors reading reviews
Computer Software Company
13%
Financial Services Firm
12%
Manufacturing Company
9%
Educational Organization
6%
Manufacturing Company
30%
Financial Services Firm
9%
University
8%
Comms Service Provider
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Google Vertex AI?
We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for trai...
What is your experience regarding pricing and costs for Google Vertex AI?
They have different pricing models like pay-as-you-go or subscription model, and total cost of ownership. It is comparatively cheaper than Azure.
What needs improvement with Google Vertex AI?
I'm not sure if I have suggestions for improvement. Based on my comparison between the two, Vertex has various additional functionalities that Azure doesn't provide.
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

Find out what your peers are saying about Google Vertex AI vs. PyTorch and other solutions. Updated: June 2025.
856,873 professionals have used our research since 2012.