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: December 2025).

Google Vertex AI vs. PyTorch

December 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

3rd

Average Rating

8.2

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

AI-Agent Builders (5th)

PyTorch

Ranking in AI Development Platforms

9th

Average Rating

8.6

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of February 2026, in the AI Development Platforms category, the mindshare of Google Vertex AI is 8.1%, down from 16.1% compared to the previous year. The mindshare of PyTorch is 3.2%, up from 1.2% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms Market Share Distribution
Product	Market Share (%)
Google Vertex AI	8.1%
PyTorch	3.2%
Other	88.7%

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.

Read full review

Quotes from Members

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

Pros

"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."

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

"With just one single platform, Google Vertex AI platform, we can achieve everything; we need not switch over to multiple tools, multiple platforms, as everything can be accomplished through this one single platform for integration with existing workflows, systems, tools, and databases."

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

"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."

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

"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."

More Google Vertex AI pros

"We use PyTorch libraries, which are working well. It's very easy."

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

"PyTorch is developer-friendly, allowing developers to continuously create new projects."

"The tool is very user-friendly."

"PyTorch allows me to build my projects from scratch."

"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."

"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 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."

More PyTorch pros

Cons

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

"Google Vertex AI is good in machine learning and AI, but it lacks optimization."

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

"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."

"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."

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

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

More Google Vertex AI cons

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

"I would like to see better learning documents."

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

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

"The product has certain shortcomings in the automation of machine learning."

"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."

"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."

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."

"The price structure is very clear"

"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."

"PyTorch is open-sourced."

"PyTorch is an open-source solution."

"It is free."

"PyTorch is open source."

"The solution is affordable."

"It is free."

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Top Industries

By visitors reading reviews

Computer Software Company

12%

Financial Services Firm

Manufacturing Company

Educational Organization

Manufacturing Company

17%

Comms Service Provider

10%

University

10%

Financial Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	7

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	4
Large Enterprise	4

Questions from the Community

What is your experience regarding pricing and costs for Google Vertex AI?

I purchased Google Vertex AI directly from Google, as we are a partner of Google. I would rate the pricing for Google Vertex AI as low; the price is affordable.

What needs improvement with Google Vertex AI?

We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models. We have to do some fine-tuning, hyperparameter optimization, and othe...

What is your primary use case for Google Vertex AI?

We are developing AI models and agents using Google Vertex AI platform, and we are deploying them using Google Vertex AI platform on Google Cloud Platform, GCP. With just one single platform, Googl...

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

January 2026

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January 2026

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.