Google Vertex AI vs NVIDIA DGX Cloud comparison

The compared Google and NVIDIA solutions aren't in the same category. Google is ranked #1 in AIDP , with an average rating of 8.1, and holds a 8.4% mindshare in the category. NVIDIA is ranked #2 in AII , and holds a 13.5% 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 15 Google Vertex AI reviews

6,949 Views
6,115 Comparison Views

100% willing to recommend

NVIDIA DGX Cloud

Read 1 NVIDIA DGX Cloud review

1,829 Views
732 Comparison Views

100% willing to recommend

Google Vertex AI

NVIDIA DGX Cloud

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Executive Summary

We performed a comparison between Google Vertex AI and NVIDIA DGX Cloud based on real PeerSpot user reviews.

Find out what your peers are saying about Google, Microsoft, Hugging Face and others in AI Development Platforms.

To learn more, read our detailed AI Development Platforms Report (Updated: March 2026).

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AI Development Platforms

March 2026

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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

Average Rating

8.2

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

AI Development Platforms (1st), AI-Agent Builders (4th)

NVIDIA DGX Cloud

Average Rating

9.0

Number of Reviews

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 8.4%, down 14.9% compared to last year.
NVIDIA DGX Cloud, on the other hand, focuses on AI Infrastructure, holds 13.5% mindshare, down 29.4% since last year.

AI Development Platforms Mindshare Distribution
Product	Mindshare (%)
Google Vertex AI	8.4%
Hugging Face	6.9%
Azure OpenAI	6.5%
Other	78.2%

AI Development Platforms

AI Infrastructure Mindshare Distribution
Product	Mindshare (%)
NVIDIA DGX Cloud	13.5%
Amazon Bedrock	15.1%
GroqCloud Platform	12.4%
Other	59.0%

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 most valuable feature we've found is the model garden, which allows us to deploy and use various models through the provided endpoints easily."

"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."

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

"The support is perfect and fantastic."

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

"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 best feature of Google Vertex AI is the ease of use, along with the integration with the rest of the Google ecosystem and the way models can be made available outside Google through endpoints."

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

More Google Vertex AI pros

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

Cons

"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."

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

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

"Google can improve Google Vertex AI in terms of analysis and accuracy. When passing a very large context, instead of receiving vague responses, it would be better if the system could prompt users not to pass overly large prompts and provide clearer guidance on how to fine-tune Gemini for specific use cases."

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

"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."

"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 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 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"

Information not available

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Top Industries

By visitors reading reviews

Computer Software Company

11%

Financial Services Firm

10%

Manufacturing Company

Educational Organization

Computer Software Company

13%

University

12%

Manufacturing Company

11%

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	4
Large Enterprise	7

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.

See all answers

What needs improvement with Google Vertex AI?

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. To ...

See all answers

What is your primary use case for Google Vertex AI?

Google Vertex AI has been utilized for Vertex Pipelines. I have not utilized the pre-trained APIs in Google Vertex AI, as our deployment is primarily on AWS, and we use API calls.

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Comparisons

Azure OpenAI vs Google Vertex AI

Compared 22% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 12% of the time

Azure AI Foundry vs Google Vertex AI

Compared 8% of the time

Hugging Face vs Google Vertex AI

Compared 7% of the time

Replicate vs Google Vertex AI

Compared 2% of the time

More Google Vertex AI Competitors

NVIDIA RTX Series vs NVIDIA DGX Cloud

Compared 15% of the time

Amazon Bedrock vs NVIDIA DGX Cloud

Compared 15% of the time

AMD Instinct vs NVIDIA DGX Cloud

Compared 13% of the time

Hailo-8 vs NVIDIA DGX Cloud

Compared 11% of the time

Dell Validated Design vs NVIDIA DGX Cloud

Compared 9% of the time

More NVIDIA DGX Cloud Competitors

Product Reports

Buyer's Guide

Google Vertex AI

March 2026

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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 Cloud is a powerful infrastructure solution designed for artificial intelligence and machine learning workloads, offering scalable and high-performance compute capabilities to streamline AI development and deployment across enterprises.

Providing access to NVIDIA's most advanced GPUs, DGX Cloud delivers an efficient and robust cloud-based platform for managing AI applications. Users benefit from its integration with popular AI frameworks and tools, making it a preferred choice for developers aiming to accelerate AI projects. With its focus on flexibility, DGX Cloud supports diverse workloads, catering to the needs of organizations striving for innovation in machine learning and data analytics.

What are the standout features of NVIDIA DGX Cloud?

Enhanced Compute Power: Leverages top-tier NVIDIA GPUs to provide exceptional processing capabilities.
AI Framework Integration: Seamlessly integrates with leading AI frameworks to facilitate development.
Scalability: Offers flexible resources to meet the dynamic demands of AI workloads.
Robust Security: Ensures data protection with advanced security measures.
Collaboration Tools: Supports team collaboration features to improve project efficiency.

What advantages and ROI should users expect?

Increased Efficiency: Accelerates AI project timelines with optimal compute power.
Cost Savings: Reduces infrastructure costs by offering scalable cloud resources.
Flexibility: Adapts easily to growing or changing AI workloads.
Enhanced Innovation: Enables rapid experimentation and iteration for AI models.
Secured Investments: Protects data and intellectual property with strong security protocols.

In industries like healthcare, finance, and automotive, NVIDIA DGX Cloud is transforming workflows by enabling faster model training and deployment. Companies utilize its capabilities to enhance predictive analytics, improve decision-making, and drive advancements in autonomous systems, making it indispensable for firms seeking a competitive edge in AI.

NVIDIA

Sample Customers

Information Not Available

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

Buyer's Guide

AI Development Platforms

March 2026

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