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Azure OpenAI vs Google Vertex AI comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jul 27, 2025

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
1st
Average Rating
7.8
Reviews Sentiment
6.6
Number of Reviews
34
Ranking in other categories
No ranking in other categories
Google Vertex AI
Ranking in AI Development Platforms
2nd
Average Rating
8.2
Reviews Sentiment
6.4
Number of Reviews
14
Ranking in other categories
AI-Agent Builders (4th)
 

Mindshare comparison

As of October 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 9.4%, down from 18.1% compared to the previous year. The mindshare of Google Vertex AI is 10.3%, down from 19.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms Market Share Distribution
ProductMarket Share (%)
Azure OpenAI9.4%
Google Vertex AI10.3%
Other80.3%
AI Development Platforms
 

Featured Reviews

Rafael Keller - PeerSpot reviewer
Creates effective scheduling agents with responsive AI capabilities
I am using it to create agents to schedule appointments for clinics and professionals in general. It serves both small and major companies. The primary use case involves creating agents Its ability to understand and respond well to queries, including language translation for clients, is…
Hamada Farag - PeerSpot reviewer
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

"The document intelligence feature has significantly aided in our operations, facilitating the creation of descriptive content."
"Its ability to understand and respond well to queries, including language translation for clients, is beneficial."
"OpenAI integrates seamlessly with the broader Microsoft Azure ecosystem, and that provides synergies with the other solutions. This integration makes it much easier to build solutions."
"We have many use cases for the solution, such as digitalizing records, a chatbot looking at records, and being able to use generative AI on them."
"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."
"The high precision of information extraction is the most valuable feature."
"Azure's integration with OpenAI's GPT is beneficial as well, especially for text generation tasks."
"Azure OpenAI is very easy to use instead of AWS services."
"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."
"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."
"Google Vertex AI is better for deployment, configuration, delivery, licensing, and integration compared to other AI platforms."
"The integration of AutoML features streamlines our machine-learning workflows."
"Vertex comes with inbuilt integration with GCP for data storage."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"It provides the most valuable external analytics."
 

Cons

"I noticed there are no instructional videos or guides on the network portal for initial configurations. There is limited information available, and this is a concern for me. I would like to see more resources and guides to address these issues."
"The fine-tuning of models with the use of Azure OpenAI is an area with certain shortcomings currently, and it can be considered for improvement in the future."
"Latency performance is a major part. And I'm seeking support for multiple models that handle text, images, videos, and voice. I'm from India, and I'm looking for more support in Indian languages. There are 18 official languages and many more unofficial. We need support for these languages, especially in voice moderation, which is not yet available."
"The solution's response is a bit slow sometimes."
"There are certain shortcomings with the product's scalability and support team where improvements are required."
"The product must improve its dashboards."
"I faced one issue with Azure OpenAI: My customer wanted more clarity on the pricing. They were not able to get proper answers from the documentation or the pricing calculator. I suggest that Microsoft maintain standardization in the pricing details published in the documentation and the pricing calculator."
"Azure needs to work on its own model development and improve the integration of voice-to-text services, particularly for right-to-left languages such as Arabic and Urdu."
"The tool's documentation is not good. It is hard."
"I think the technical documentation is not readily available in the tool."
"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."
"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'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 is not completely mature and needs some features and functions. The interface needs to be more user-friendly."
 

Pricing and Cost Advice

"I rate the product pricing six out of ten."
"The licensing is interaction-based, meaning transactional. It's reasonably priced for now."
"The solution's pricing depends on the services you will deploy."
"We started with monthly payments, but we plan to switch to yearly billing once we've stabilized our solution."
"The cost structure depends on the volume of data processed and the computational resources required."
"Azure OpenAI is a bit more expensive than other services."
"The pricing is acceptable, and it's delivering good value for the results and outcomes we need."
"I'm uncertain about the licensing, specifically the pricing. This falls under the purview of other teams, particularly the sales teams. I am not informed about the pricing details."
"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 price structure is very clear"
"The solution's pricing is moderate."
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Top Industries

By visitors reading reviews
Computer Software Company
13%
Financial Services Firm
12%
Manufacturing Company
10%
Educational Organization
5%
Computer Software Company
14%
Financial Services Firm
10%
Manufacturing Company
9%
Educational Organization
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business15
Midsize Enterprise1
Large Enterprise18
By reviewers
Company SizeCount
Small Business5
Midsize Enterprise3
Large Enterprise7
 

Questions from the Community

What do you like most about Azure OpenAI?
The product is easy to integrate with our IT workflow.
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?
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 s...
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?
Google Vertex AI is one of the best in the market, followed by Azure AI. It can be rated at eight or nine out of ten. It is not completely mature and needs some features and functions. The interfac...
 

Overview

Find out what your peers are saying about Azure OpenAI vs. Google Vertex AI and other solutions. Updated: September 2025.
871,829 professionals have used our research since 2012.