Gemini Enterprise Agent Platform vs NVIDIA DGX Cloud comparison

Why PeerSpot?

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 8.0% mindshare in the category. NVIDIA is ranked #2 in AII , and holds a 9.9% mindshare. Additionally, 100% of Google users are willing to recommend the solution, compared to 100% of NVIDIA users who would recommend it.

Gemini Enterprise Agent Pla...

Read 15 Gemini Enterprise Agent Platform reviews

9,692 Views
8,044 Comparison Views

100% willing to recommend

NVIDIA DGX Cloud

Read 1 NVIDIA DGX Cloud review

2,015 Views
1,008 Comparison Views

100% willing to recommend

Gemini Enterprise Agent Pla...

NVIDIA DGX Cloud

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Gemini Enterprise Agent Platform 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.

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AI Development Platforms

September 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

Gemini Enterprise Agent Pla...

Average Rating

8.2

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

AI Development Platforms (1st), AI Agent Builders (5th)

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. Gemini Enterprise Agent Platform is designed for AI Development Platforms and holds a mindshare of 8.0%, down 10.7% compared to last year.
NVIDIA DGX Cloud, on the other hand, focuses on AI Infrastructure, holds 9.9% mindshare, down 18.9% since last year.

AI Development Platforms Mindshare Distribution
Product	Mindshare (%)
Gemini Enterprise Agent Platform	8.0%
Azure OpenAI	7.2%
Hugging Face	3.9%
Other	80.9%

AI Development Platforms

AI Infrastructure Mindshare Distribution
Product	Mindshare (%)
NVIDIA DGX Cloud	9.9%
Amazon Bedrock	12.3%
GroqCloud Platform	7.9%
Other	69.9%

AI Infrastructure

Featured Reviews

Pethuru Chelliah

Chief Architect at a energy/utilities company with 10,001+ employees

Developed and deployed AI agents through a unified platform that supports integration with enterprise systems

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 other optimizations to enhance the performance of the AI models. We are not fully leveraging the automation being provided by Google Vertex AI for building AI models. Google Vertex AI has to be enhanced to support agentic AI system development. AI agents have to be developed through Google Vertex AI. Additionally, RAG support, vector database support, knowledge graph support, integration with enterprise systems such as ERP, CRM, PLM, MES, and other enterprise systems need improvement. Automated workflow generation and automation could also be enhanced. At this point, we are completely satisfied with the features and functionalities of Google Vertex AI platform, but as we move towards autonomous systems through agentic AI paradigm, Google Vertex AI platform needs to be improved to facilitate agentic AI system design, development, deployment, monitoring, observability, governance, and security.

Read full review

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

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

"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 better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."

"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 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 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."

"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."

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

More Gemini Enterprise Agent Platform pros

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

Cons

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

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

"Some of the tools should have more advanced settings available. Some are very locked into certain features and settings, and there is no customization."

"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."

"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."

"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."

"We used AutoML feature for developing AI models automatically, but we are not comfortable with the performance of those models."

More Gemini Enterprise Agent Platform 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."

"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 solution's pricing is moderate."

Information not available

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Top Industries

By visitors reading reviews

Outsourcing Company

10%

Financial Services Firm

Manufacturing Company

Comms Service Provider

University

11%

Manufacturing Company

10%

Comms Service Provider

Financial Services Firm

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.

See all answers

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Comparisons

Azure OpenAI vs Gemini Enterprise Agent Platform

Compared 28% of the time

Microsoft Foundry vs Gemini Enterprise Agent Platform

Compared 9% of the time

Microsoft Azure Machine Learning Studio vs Gemini Enterprise Agent Platform

Compared 6% of the time

Amazon Bedrock vs Gemini Enterprise Agent Platform

Compared 4% of the time

Hugging Face vs Gemini Enterprise Agent Platform

Compared 4% of the time

More Gemini Enterprise Agent Platform Competitors

Amazon Bedrock vs NVIDIA DGX Cloud

Compared 17% of the time

NVIDIA RTX Series vs NVIDIA DGX Cloud

Compared 9% of the time

AMD Instinct vs NVIDIA DGX Cloud

Compared 8% of the time

Dell Validated Design vs NVIDIA DGX Cloud

Compared 8% of the time

Tenstorrent Cloud vs NVIDIA DGX Cloud

Compared 7% of the time

More NVIDIA DGX Cloud Competitors

Product Reports

Buyer's Guide

Gemini Enterprise Agent Platform

September 2026

Download Gemini Enterprise Agent Platform product report

No data available

Also Known As

Vertex, Google Vertex AI

NVIDIA DGX-1, DGX Cloud, NVIDIA DGX Platform

Overview

Gemini Enterprise Agent Platform offers advanced AI capabilities with large language and vision models, a seamless Google ecosystem integration, and low-code/no-code options, enhancing AI agent development, deployment, and monitoring.

Gemini Enterprise Agent Platform empowers businesses with its comprehensive AI development environment, focusing on generative and agentic AI applications. It streamlines tasks like chatbots, document summarization, and data workflows integration. Despite its complexity and high cost, it offers advantages in feature centralization, end-to-end integration, external analytics, and multi-model capabilities. Challenges include navigation issues, setup complexity, and documentation accessibility. Users wish for improved model efficiency, system integration, customer support, and a more user-friendly experience for non-technical users. The platform's potential in optimizing machine learning modules and managing fintech tasks makes it valuable for organizations seeking robust AI model management.

What are Gemini Enterprise Agent Platform's Key Features?

Large Language and Vision Models: Enhance AI capabilities across diverse applications.
Google Ecosystem Integration: Seamlessly integrates within Google's platforms.
Feature Store: Centralizes features for better team communication and model management.
Low-Code/No-Code Options: Facilitates AI development with minimal coding.
End-to-End Integration: Offers comprehensive tools for agent deployment and monitoring.

What Benefits and ROI Should Users Look For?

Feature Centralization: Streamlines communication and coordination across teams.
Integration Ease: Simple integration with existing systems enhances operational efficiency.
AI Optimization: Improves machine learning modules, reducing costs and boosting efficiency.
Advanced Language Analytics: Enables better data insights via keyword detection and document processing.

Gemini Enterprise Agent Platform finds application in industries focusing on generative AI and agentic tasks. Businesses use it for chatbots, document summarization, and fintech-related tasks, benefiting from its capabilities in handling large documents, keyword detection, and language analytics. Its ease of integration with current systems is a significant advantage for many organizations.

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

September 2026

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