Google Vertex AI vs NVIDIA DGX Cloud comparison

Google and NVIDIA are both solutions in the AI Infrastructure category. Google is ranked #1 with an average rating of 8.3, while NVIDIA is ranked #3 with an average rating of 9.0. Google holds a 18.3% mindshare in AII, compared to NVIDIA’s 17.2% mindshare. Additionally, 100% of Google users are willing to recommend the solution, compared to 100% of NVIDIA users who would recommend it.

Google Vertex AI

Read 11 Google Vertex AI reviews

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708 Comparison Views

100% willing to recommend

NVIDIA DGX Cloud

Read 1 NVIDIA DGX Cloud review

680 Views
402 Comparison Views

100% willing to recommend

Google Vertex AI

NVIDIA DGX Cloud

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 Infrastructure

1st

Average Rating

8.4

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

AI Development Platforms (2nd)

NVIDIA DGX Cloud

Ranking in AI Infrastructure

3rd

Average Rating

9.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of June 2025, in the AI Infrastructure category, the mindshare of Google Vertex AI is 18.3%, down from 32.0% compared to the previous year. The mindshare of NVIDIA DGX Cloud is 17.2%, up from 16.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Infrastructure

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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reviewer2309676

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.

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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

"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."

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

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

"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."

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

"Vertex comes with inbuilt integration with GCP for data storage."

"It provides the most valuable external analytics."

More Google Vertex AI pros

"The most valuable thing about DGX Systems is their super-fast connection."

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."

"The tool's documentation is not good. It is hard."

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

"I'm not sure if I have suggestions for improvement."

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

"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."

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

More Google Vertex AI cons

"One thing that could be better in DGX Systems is their power consumption."

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."

"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."

Information not available

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Top Industries

By visitors reading reviews

Computer Software Company

13%

Financial Services Firm

12%

Manufacturing Company

Retailer

Computer Software Company

13%

Manufacturing Company

13%

University

11%

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

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...

See all answers

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.

See all answers

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.

See all answers

What do you like most about NVIDIA DGX Systems?

The most valuable thing about DGX Systems is their super-fast connection.

See all answers

What is your experience regarding pricing and costs for NVIDIA DGX Systems?

The prices for DGX are pretty high, and not everyone can afford them. We only have a few out of our total servers because of the cost. It would be great if the prices could come down in the future ...

See all answers

What needs improvement with NVIDIA DGX Systems?

One thing that could be better in DGX systems is their power consumption. They have been making improvements, but finding the right balance between performance and using less power is a challenge. ...

See all answers

Comparisons

Azure OpenAI vs Google Vertex AI

Compared 35% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 17% of the time

Hugging Face vs Google Vertex AI

Compared 11% of the time

Amazon SageMaker vs Google Vertex AI

Compared 7% of the time

GroqCloud Platform vs Google Vertex AI

Compared 2% of the time

More Google Vertex AI Competitors

Hailo-8 vs NVIDIA DGX Cloud

Compared 34% of the time

AMD Instinct vs NVIDIA DGX Cloud

Compared 11% of the time

Dell Validated Design vs NVIDIA DGX Cloud

Compared 11% of the time

Amazon Bedrock vs NVIDIA DGX Cloud

Compared 8% of the time

AWS Machine Learning vs NVIDIA DGX Cloud

Compared 7% of the time

More NVIDIA DGX Cloud Competitors

Product Reports

Buyer's Guide

Google Vertex AI

June 2025

Download Google Vertex AI product report

No data available

Also Known As

No data available

NVIDIA DGX-1, DGX Cloud, NVIDIA DGX Platform

Overview

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

Google

NVIDIA DGX Systems are designed for deep learning and artificial intelligence tasks, offering robust hardware performance, seamless AI workflow integration, and the ability to handle large datasets efficiently. They significantly improve research and development efficiency, allowing multiple experiments to run simultaneously.

NVIDIA DGX Systems are renowned for their high performance in AI workloads and accelerated computing capabilities. The integration with existing environments is seamless, and the infrastructure is reliable, featuring robust security and excellent customer support. Users experience reduced time to insight and efficient heavy computational task support. The systems' scalability ensures they grow with technological demands, providing advanced GPU capabilities and efficient data processing.

What are the key features of NVIDIA DGX Systems?

High performance in AI workloads: Optimized for deep learning and AI research.
Accelerated computing: Reduces model training times and handles large datasets efficiently.
Reliable infrastructure: Seamlessly integrates with existing workflows.
Robust security: Protects data and ensures compliance with standards.
Scalability: Grows to meet technological demands and supports heavy computational tasks.

What should users look for in reviews of NVIDIA DGX Systems?

Improved productivity: Handling multiple simultaneous experiments to boost project output.
Reduced computation time: Accelerates research and development timelines.
Efficient data processing: Manages large datasets effectively.
Customer support: Addresses user needs promptly and provides reliable assistance.

NVIDIA DGX Systems find applications in numerous industries, enhancing AI research and development processes. Companies in healthcare use them for medical imaging and drug discovery, while financial institutions leverage their capabilities for algorithmic trading and risk management. Automotive and manufacturing sectors implement these systems for advancements in autonomous driving and predictive maintenance.

NVIDIA

Sample Customers

Information Not Available

Open AI, UC Berkley, New York University, Massachusetts General Hospital

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