Google Vertex AI vs PyTorch comparison

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Google Vertex AI

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Executive SummaryUpdated on Dec 4, 2024

PyTorch and Google Vertex AI are both prominent in the machine learning sector. Based on the comparison, Google Vertex AI appears to hold an advantage due to its comprehensive features offering greater overall value.

Features: PyTorch offers a dynamic computational graph, ease of debugging, and customizable models suitable for research. Google Vertex AI provides end-to-end managed services, automated machine learning, and built-in model management. It delivers a cohesive environment for scalable model deployment.

Room for Improvement: PyTorch could improve its deployment process and support integration, requiring less manual intervention. Enhanced documentation and usability for novices could be beneficial. Google Vertex AI might work on reducing complexity in its comprehensive offerings, improve cost transparency, and offer more flexibility for custom solutions.

Ease of Deployment and Customer Service: PyTorch requires manual deployment efforts but supports diverse platform integration. In contrast, Google Vertex AI simplifies the process with its cloud-native capabilities and provides extensive support, facilitating easier implementation without requiring deep technical knowledge.

Pricing and ROI: PyTorch, as an open-source platform, is initially more budget-friendly, appealing to users with cost considerations. Nonetheless, Google Vertex AI includes features that enhance scalability and efficiency, promising long-term cost savings and superior ROI by reducing labor and maximizing investment potential in AI solutions.

To learn more, read our detailed Google Vertex AI vs. PyTorch Report (Updated: July 2025).

Google Vertex AI vs. PyTorch

July 2025

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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

1st

Average Rating

8.4

Reviews Sentiment

6.7

Number of Reviews

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

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2025, in the AI Development Platforms category, the mindshare of Google Vertex AI is 11.1%, down from 20.9% compared to the previous year. The mindshare of PyTorch is 2.8%, up from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Hamada Farag

Technology Consultant at Beta Information Technology

Customization and integration empower diverse AI applications

We are familiar with most Google Cloud services, particularly infrastructure services, storage, compute, AI tools, containerization, GCP containerization, and cloud SQL. We are familiar with approximately eighty percent of Google's services, primarily related to infrastructure, AI, containers, backup, storage, and compute. We are familiar with Gemini AI and Google Vertex AI, and we have completed some exercises and cases with our customers for Google AI. We use automation in machine learning. I work with a team where everyone has specific responsibilities. We have design and development processes in place. Based on my experience, I would rate Google Vertex AI a 9 out of 10.

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Rohan Sharma

AI/ML Co-Lead at Developer Student Clubs - GGV

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.

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Quotes from Members

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

Pros

"It provides the most valuable external analytics."

"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."

"The integration of AutoML features streamlines our machine-learning workflows."

"Google Vertex AI is an out-of-the-box and very easy-to-use solution."

"Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."

"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."

"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."

More Google Vertex AI pros

"For me, the product's initial setup phase is easy...For beginners, it is fairly easy to learn."

"The tool is very user-friendly."

"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."

"The product's initial setup phase is 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."

"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."

"I like PyTorch's scalability."

More PyTorch pros

Cons

"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."

"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."

"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."

"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."

"It takes a considerable amount of time to process, and I understand the technology behind why it takes this long, but this is something that could be reduced."

"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."

"I think the technical documentation is not readily available in the tool."

More Google Vertex AI cons

"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 do not have any complaints."

"I would like to see better learning documents."

"On the production side of things, having more frameworks would be helpful."

"We faced an issue with PyTorch due to version incompatibility. PyTorch has no latest version after v12.3."

"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."

"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 analyzing and latency of compiling could be improved to provide enhanced results."

More PyTorch cons

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."

"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 solution's pricing is moderate."

"The price structure is very clear"

"PyTorch is open-sourced."

"It is free."

"PyTorch is open source."

"PyTorch is an open-source solution."

"The solution is affordable."

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Top Industries

By visitors reading reviews

Computer Software Company

14%

Financial Services Firm

11%

Manufacturing Company

Educational Organization

Manufacturing Company

23%

Comms Service Provider

University

Educational Organization

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?

Google Vertex AI is one of the best in the market, followed by Azure AI. It can be rated at eight or nine out of ten. It is not completely mature and needs some features and functions. The interfac...

What do you like most about PyTorch?

The tool is very user-friendly.

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....

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Product Reports

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Google Vertex AI

August 2025

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August 2025

Overview

Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.

Google

We've built this course as an introduction to deep learning. Deep learning is a field of machine learning utilizing massive neural networks, massive datasets, and accelerated computing on GPUs. Many of the advancements we've seen in AI recently are due to the power of deep learning. This revolution is impacting a wide range of industries already with applications such as personal voice assistants, medical imaging, automated vehicles, video game AI, and more.

In this course, we'll be covering the concepts behind deep learning and how to build deep learning models using PyTorch. We've included a lot of hands-on exercises so by the end of the course, you'll be defining and training your own state-of-the-art deep learning models.