Gemini Enterprise Agent Platform vs Google Cloud AI Platform comparison

Gemini Enterprise Agent Platform vs. Google Cloud AI Platform

Download the complete report

Helped 889,955 peers since 2012

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...

Ranking in AI Development Platforms

1st

Average Rating

8.2

Reviews Sentiment

6.3

Number of Reviews

Ranking in other categories

AI-Agent Builders (5th)

Google Cloud AI Platform

Ranking in AI Development Platforms

14th

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2026, in the AI Development Platforms category, the mindshare of Gemini Enterprise Agent Platform is 8.2%, down from 14.0% compared to the previous year. The mindshare of Google Cloud AI Platform is 3.2%, down from 4.3% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms Mindshare Distribution
Product	Mindshare (%)
Google Vertex AI	8.2%
Google Cloud AI Platform	3.2%
Other	88.6%

AI Development Platforms

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.

Read full review

Ton Jacobs

Owner at Go knowledge

Streamlines app development with dynamic databases and an easy setup

I used Oracle APEX before Google Cloud AI Platform. Oracle APEX is a free tool, except for the Oracle database, which I can only use with it. To have more freedom, I chose Firebase and Google's solutions as it allows me to run it on a hosted server if I want to.

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 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."

"The most useful function of Google Vertex AI for me is the ease of integration, as we can easily create a prompt and integrate it into our current system."

"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 support is perfect and fantastic."

"The features I have found most valuable in Google Vertex AI are Gemini's large language models, which are currently among the best, and the vision tool of Gemini, which I consider quite good."

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

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

More Gemini Enterprise Agent Platform pros

"The initial setup is very straightforward."

"On GCP, we are exposing our API services to our clients so that they send us their information. It can be single individual records or it can be a batch of their clients."

"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."

"The feedback left about these tools was really helpful and informative for us"

"We are trying our best to improve our existing models and privacy and to keep on updating it, and also we are trying to use reinforcement learning and separate APIs so that if a user wants to update their data, they can do so."

"The platform's Google Vision API is particularly valuable."

"The solution is able to read 90% of the documents correctly with a 10% error rate."

"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."

More Google Cloud AI Platform pros

Cons

"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."

"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."

"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."

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

"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."

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

More Gemini Enterprise Agent Platform cons

"At first, there were only the user-managed rules to identify the best attributes of the individual. Then, we came up with a truth set and developed different machine learning models with the help of that truth set, so now it's completely machine learning."

"The initial setup was straightforward for me but could be difficult for others."

"I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite."

"The technical support from Google is not very fast. I think it is about a five out of ten even though they have courses online where I can learn a lot, if I really need support, I have to wait a very long time."

"The solution can be improved by simplifying the process to make your own models."

"It could be more clear, and sometimes there are errors that I don't quite understand."

"The model management on Google Cloud AI Platform could be better."

"The solution can be improved by simplifying the process to make your own models."

More Google Cloud AI Platform cons

Pricing and Cost Advice

"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 solution's pricing is moderate."

"The price structure is very clear"

"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 of the solution is competitive."

"For every thousand uses, it is about four and a half euros."

"The solution has an attractive starting program, which costs only 300 USD for a duration of three months. During this period, one can accomplish a lot of work on the solution."

"The pricing is on the expensive side."

"The licenses are cheap."

See which vendors are best for you

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

See recommendations

889,955 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

10%

Computer Software Company

10%

Manufacturing Company

10%

Comms Service Provider

Computer Software Company

12%

Manufacturing Company

11%

Financial Services Firm

10%

University

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	2
Large Enterprise	2

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.

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 ...

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.

What is your experience regarding pricing and costs for Google Cloud AI Platform?

For the most part, the pricing is perfect sinceit grows with the use of my app. In some cases, they could be more specific about the pricing, especially for some AI features.

What is your primary use case for Google Cloud AI Platform?

I use Google Cloud AI Platform due to Firebase and the many APIs that are available with it.

What advice do you have for others considering Google Cloud AI Platform?

I have knowledge of it, and I do recommend Google Cloud AI Platform to other people. I would definitely rate the overall solution as an eight out of ten.

Azure OpenAI vs Gemini Enterprise Agent Platform

Comparisons

Compared 22% of the time

Microsoft Azure Machine Learning Studio vs Gemini Enterprise Agent Platform

Compared 10% of the time

Azure AI Foundry vs Gemini Enterprise Agent Platform

Compared 8% of the time

Hugging Face vs Gemini Enterprise Agent Platform

Compared 6% of the time

Replicate vs Gemini Enterprise Agent Platform

Compared 2% of the time

More Gemini Enterprise Agent Platform Competitors

Azure OpenAI vs Google Cloud AI Platform

Compared 12% of the time

IBM Watson Machine Learning vs Google Cloud AI Platform

Compared 10% of the time

Hugging Face vs Google Cloud AI Platform

Compared 9% of the time

TensorFlow vs Google Cloud AI Platform

Compared 7% of the time

Distyl AI vs Google Cloud AI Platform

Compared 7% of the time

More Google Cloud AI Platform Competitors

Product Reports

Gemini Enterprise Agent Platform

Download Gemini Enterprise Agent Platform product report

AI Development Platforms

Download Google Cloud AI Platform product report

Also Known As

Vertex, Google Vertex AI

Google Cloud for AI

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

Google AI Platform is a managed service that enables you to easily build machine learning models, that work on any type of data, of any size. Create your model with the powerful TensorFlow framework that powers many Google products, from Google Photos to Google Cloud Speech.

Google

Sample Customers

Information Not Available

Carousell

Gemini Enterprise Agent Platform vs. Google Cloud AI Platform