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Azure OpenAI vs Gemini Enterprise Agent Platform comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Apr 23, 2026

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

Azure OpenAI
Ranking in AI Development Platforms
2nd
Average Rating
7.8
Reviews Sentiment
6.4
Number of Reviews
36
Ranking in other categories
No ranking in other categories
Gemini Enterprise Agent Pla...
Ranking in AI Development Platforms
1st
Average Rating
8.2
Reviews Sentiment
6.3
Number of Reviews
15
Ranking in other categories
AI Agent Builders (5th)
 

Mindshare comparison

As of June 2026, in the AI Development Platforms category, the mindshare of Azure OpenAI is 6.8%, down from 11.6% compared to the previous year. The mindshare of Gemini Enterprise Agent Platform is 8.0%, down from 12.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Mindshare Distribution
ProductMindshare (%)
Gemini Enterprise Agent Platform8.0%
Azure OpenAI6.8%
Other85.2%
AI Development Platforms
 

Featured Reviews

RC
AI Engineering Manager at a tech vendor with 10,001+ employees
Empowerment in regulatory content generation marred by inconsistency and hallucination issues
While it is good, we sometimes encounter hallucination issues, which is a significant concern. We are changing the prompt and fine-tuning it, but we still face some inconsistent behavior. We have specific instructions and keep the temperature very low to avoid overly generative responses, ensuring we receive specific answers from the particular source document without deviation; however, the results can sometimes vary. The main issue with Azure OpenAI is the inconsistency in output. We have a set template instruction, and it should generate within those parameters without any creativity because it's meant for regulatory authoring documents. The business provides the template instructions, and it should generate accordingly. While we have different prompts for various needs, sometimes it generates the correct results, and sometimes it does not, leading to inconsistency. For stability, based on the current model I am using, I would rate Azure OpenAI a 7 due to the ongoing hallucination issues.
Hamada Farag - PeerSpot reviewer
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.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Azure OpenAI has significantly reduced costs and increased efficiency in tasks such as aggressive testing of systems to avoid anomalies and trust issues."
"The solution has a very drag-and-drop environment. Instead of coding something from scratch or understanding any concept in extensive depth before deployment, this is good. Plus, they have an auto dataset, which means you can choose any dataset they have instead of providing your own. So that's also pretty nice."
"Azure OpenAI is used as chat services, allowing me to replace human tasks with analytical capabilities."
"OpenAI's models are more mature than Watson's. They offer a wider range of features and provide richer outputs."
"Two aspects I appreciate are the turnaround time and ease of use. As it's a managed service, the quick turnaround is beneficial, and the simple interface makes it easy to work with. Performance and scalability are also strong points since you can scale as needed."
"It's very powerful. It allows users to query our documents using natural language and receive answers in the same way. This makes our product information much more accessible than traditional keyword-based search."
"GPT was useful for our projects."
"The product saves a lot of time."
"Vertex comes with inbuilt integration with GCP for data storage."
"Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"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 integration of AutoML features streamlines our machine-learning workflows."
"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."
"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."
 

Cons

"Sometimes, it gives answers in English, even when the request is in Polish."
"One area for improvement is providing more flexibility in configuration and connectivity with external tools."
"I would like to see in the future that Azure OpenAI brings non-OpenAI models into their service because they currently do not have independent models and follow the OpenAI roadmap."
"Our customers are worried about data management, ethical, and security issues."
"There are no available updates of information that are currently provided."
"Since we don't train the model on our data, it's a struggle to ensure OpenAI answers questions exclusively from our data. During user testing, we found ways to make the system provide answers from outside sources."
"There are certain shortcomings with the product's scalability and support team where improvements are required."
"Deployment was slightly complex for me to understand."
"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"I'm not sure if I have suggestions for improvement."
"I think the technical documentation is not readily available in the tool."
"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."
"The tool's documentation is not good. It is hard."
"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."
"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."
"It is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
 

Pricing and Cost Advice

"The cost is pretty high. Even by US standards, you would find it high."
"The tool costs around 20 dollars a month."
"I rate the product pricing six out of ten."
"Cost-wise, the product's price is a bit on the higher side."
"The solution's pricing is normal worldwide but expensive in Turkey because Turkey's currency is different."
"The platform offers a flexible pricing model which depends on the features and capabilities we utilize."
"The cost is quite high and fixed."
"We started with monthly payments, but we plan to switch to yearly billing once we've stabilized our solution."
"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 solution's pricing is moderate."
"The price structure is very clear"
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Top Industries

By visitors reading reviews
Computer Software Company
11%
Financial Services Firm
11%
Manufacturing Company
10%
Comms Service Provider
7%
Manufacturing Company
10%
Financial Services Firm
10%
Computer Software Company
8%
Comms Service Provider
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business17
Midsize Enterprise1
Large Enterprise19
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise4
Large Enterprise7
 

Questions from the Community

What is your experience regarding pricing and costs for Azure OpenAI?
In terms of pricing for Azure OpenAI, I would rate it as average compared to Gemini. Currently, Gemini is becoming increasingly popular, which prompts leadership to consider a switch primarily due ...
What needs improvement with Azure OpenAI?
The customization option in Azure OpenAI is quite challenging because any customization must be done through the knowledge base since Azure OpenAI models cannot be trained. I must build a knowledge...
What is your primary use case for Azure OpenAI?
Azure OpenAI's main use case for me involves defining solutions for incident remediation where AI provides intelligence to solve problems, perform root cause analysis, or triage incidents or change...
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.
 

Also Known As

No data available
Vertex, Google Vertex AI
 

Overview

Find out what your peers are saying about Azure OpenAI vs. Gemini Enterprise Agent Platform and other solutions. Updated: April 2026.
899,283 professionals have used our research since 2012.