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Google Vertex AI vs NVIDIA DGX Platform comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Google Vertex AI
Ranking in AI Infrastructure
1st
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
10
Ranking in other categories
AI Development Platforms (2nd)
NVIDIA DGX Platform
Ranking in AI Infrastructure
3rd
Average Rating
9.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of December 2024, in the AI Infrastructure category, the mindshare of Google Vertex AI is 27.2%, down from 29.5% compared to the previous year. The mindshare of NVIDIA DGX Platform is 22.2%, up from 16.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Infrastructure
 

Featured Reviews

Serge Dahdouh - PeerSpot reviewer
A user-friendly platform that automatizes machine learning techniques with minimal effort
We work with clients who request the implementation of a certain document into a chatbot. Because of the limited knowledge of AI, our task is to link that file to the ML and provide a platform that can work as a customer service. We previously used LangChain Phython, but now it is done through Vertex AI.
reviewer2309676 - PeerSpot reviewer
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

"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"The integration of AutoML features streamlines our machine-learning workflows."
"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."
"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."
"Vertex comes with inbuilt integration with GCP for data storage."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
"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."
"The most valuable thing about DGX Systems is their super-fast connection."
 

Cons

"I'm not sure if I have suggestions for improvement."
"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."
"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."
"I think the technical documentation is not readily available in the tool."
"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."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"One thing that could be better in DGX Systems is their power consumption."
 

Pricing and Cost Advice

"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 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."
Information not available
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Top Industries

By visitors reading reviews
Financial Services Firm
13%
Computer Software Company
13%
Manufacturing Company
9%
Retailer
7%
Manufacturing Company
16%
Computer Software Company
12%
University
11%
Educational Organization
6%
 

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...
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?
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.
What do you like most about NVIDIA DGX Systems?
The most valuable thing about DGX Systems is their super-fast connection.
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 ...
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. ...
 

Also Known As

No data available
NVIDIA DGX-1
 

Learn More

 

Overview

 

Sample Customers

Information Not Available
Open AI, UC Berkley, New York University, Massachusetts General Hospital
Find out what your peers are saying about Amazon Bedrock vs. Google Vertex AI and other solutions. Updated: November 2024.
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