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Google Vertex AI vs NVIDIA DGX Cloud comparison

 

Comparison Buyer's Guide

Executive Summary

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
Average Rating
8.2
Reviews Sentiment
6.3
Number of Reviews
15
Ranking in other categories
AI Development Platforms (3rd), AI-Agent Builders (5th)
NVIDIA DGX Cloud
Average Rating
9.0
Number of Reviews
1
Ranking in other categories
AI Infrastructure (2nd)
 

Mindshare comparison

While both are Artificial Intelligence (AI) solutions, they serve different purposes. Google Vertex AI is designed for AI Development Platforms and holds a mindshare of 8.1%, down 16.1% compared to last year.
NVIDIA DGX Cloud, on the other hand, focuses on AI Infrastructure, holds 14.2% mindshare, down 31.8% since last year.
AI Development Platforms Market Share Distribution
ProductMarket Share (%)
Google Vertex AI8.1%
Hugging Face7.2%
Azure OpenAI6.5%
Other78.2%
AI Development Platforms
AI Infrastructure Market Share Distribution
ProductMarket Share (%)
NVIDIA DGX Cloud14.2%
Amazon Bedrock13.7%
GroqCloud Platform13.1%
Other59.0%
AI Infrastructure
 

Featured Reviews

Hamada Farag - PeerSpot reviewer
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.
reviewer2309676 - PeerSpot reviewer
Team Lead, High-Performance Computing (HPC) at a manufacturing company with 1,001-5,000 employees
Versatile, well-built, and powerful
The initial setup of the DGX server was quite straightforward. We treated it like any other server during deployment. It went to the data center, where they set it up, placed it in the rack, and enabled it. The deployment process was familiar, using our standard tools like Foreman and Ansible. Since the operating system is supported, we didn't encounter any specific challenges. For deploying the DGX server, we typically need two people for software tasks and sometimes vendor assistance for hardware setup. The process takes about four hours, with NVIDIA firmware updates taking the most time (around two hours), and the rest dedicated to OS and Ansible deployment. Maintaining the DGX server is pretty straightforward. We treat it like any other server, with around 10% downtime, while the rest of the cluster remains up.

Quotes from Members

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

Pros

"The support is perfect and fantastic."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"Vertex comes with inbuilt integration with GCP for data storage."
"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."
"It provides the most valuable external analytics."
"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 monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."
"The most valuable thing about DGX Systems is their super-fast connection."
 

Cons

"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."
"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
"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."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"Google Vertex AI is quite complex to navigate and to start services with, as I need to do a lot of iterations to finally activate the services, which is one major flaw, although it is powerful."
"We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models."
"One thing that could be better in DGX Systems is their power consumption."
 

Pricing and Cost Advice

"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"
"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."
Information not available
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Top Industries

By visitors reading reviews
Computer Software Company
11%
Financial Services Firm
9%
Manufacturing Company
9%
Educational Organization
7%
Computer Software Company
13%
University
12%
Manufacturing Company
11%
Comms Service Provider
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise4
Large Enterprise7
No data available
 

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...
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Also Known As

No data available
NVIDIA DGX-1, DGX Cloud, NVIDIA DGX Platform
 

Overview

 

Sample Customers

Information Not Available
Open AI, UC Berkley, New York University, Massachusetts General Hospital
Find out what your peers are saying about Microsoft, Hugging Face, Google and others in AI Development Platforms. Updated: February 2026.
882,744 professionals have used our research since 2012.