Google Vertex AI vs NVIDIA DGX Platform 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 31.0% mindshare in AII, compared to NVIDIA’s 18.5% 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 9 Google Vertex AI reviews

767 Views
712 Comparison Views

100% willing to recommend

NVIDIA DGX Platform

Read 1 NVIDIA DGX Platform review

530 Views
433 Comparison Views

100% willing to recommend

Google Vertex AI

NVIDIA DGX Platform

Categories and Ranking

Google Vertex AI

Ranking in AI Infrastructure

1st

Average Rating

8.2

Number of Reviews

Ranking in other categories

AI Development Platforms (3rd)

NVIDIA DGX Platform

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 September 2024, in the AI Infrastructure category, the mindshare of Google Vertex AI is 31.0%, up from 27.5% compared to the previous year. The mindshare of NVIDIA DGX Platform is 18.5%, up from 16.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Infrastructure

Featured Reviews

Serge Dahdouh

CEO at Les Roches International School of Hotel Management

Aug 16, 2023

A user-friendly platform that automatizes machine learning techniques with minimal effort

I mostly use LLM models on Vertex AI. When there is a large document or multiple documents, I put them in the index database of Vertex AI's platform and it extracts the right information We work with clients who request the implementation of a certain document into a chatbot. Because of the…

Read full review

reviewer2309676

Team Lead, High-Performance Computing (HPC) at a manufacturing company with 1,001-5,000 employees

Nov 14, 2023

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.

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

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

"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."

"It provides the most valuable external analytics."

"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."

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

Cons

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

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

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

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

"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."

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

"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."

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

Pricing and Cost Advice

"The solution's pricing is moderate."

"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."

"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"

Information not available

See which vendors are best for you

Use our free recommendation engine to learn which AI Infrastructure solutions are best for your needs.

See recommendations

805,335 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

13%

Computer Software Company

13%

Manufacturing Company

Retailer

Manufacturing Company

17%

University

13%

Computer Software Company

11%

Educational Organization

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?

It costs 1000/year, so it's very inexpensive. I rate the product’s pricing a five out of ten, where one is cheap, and ten is expensive.

See all answers

What needs improvement with Google Vertex AI?

It should improve the model efficiency, which is just training the model over and over again. Building an application on Agent Builder and grounding it with our internal documents and internal data...

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 41% of the time

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 18% of the time

Hugging Face vs Google Vertex AI

Compared 11% of the time

Amazon SageMaker vs Google Vertex AI

Compared 9% of the time

Replicate vs Google Vertex AI

Compared 1% of the time

More Google Vertex AI Competitors

Hailo-8 vs NVIDIA DGX Platform

Compared 50% of the time

AWS Machine Learning vs NVIDIA DGX Platform

Compared 15% of the time

Radeon Instinct vs NVIDIA DGX Platform

Compared 6% of the time

Dell Validated Design vs NVIDIA DGX Platform

Compared 5% of the time

Lambda Stack vs NVIDIA DGX Platform

Compared 4% of the time

More NVIDIA DGX Platform Competitors

Product Reports

Buyer's Guide

Google Vertex AI

September 2024

Download Google Vertex AI product report

No data available

Also Known As

No data available

NVIDIA DGX-1

Learn More

Google

NVIDIA

Overview

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

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.

Sample Customers

Information Not Available

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

See our list of best AI Infrastructure vendors.

We monitor all AI Infrastructure reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.