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Azure OpenAI vs Google Vertex AI comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jan 12, 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
8.0
Reviews Sentiment
6.7
Number of Reviews
32
Ranking in other categories
No ranking in other categories
Google Vertex AI
Ranking in AI Development Platforms
2nd
Average Rating
8.4
Reviews Sentiment
7.4
Number of Reviews
10
Ranking in other categories
AI Infrastructure (1st)
 

Mindshare comparison

As of March 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 13.9%, down from 20.9% compared to the previous year. The mindshare of Google Vertex AI is 14.9%, down from 19.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

Anup Karade - PeerSpot reviewer
Created a chatbot powered by OpenAI to answer HR, travel, and expense-related questions
We have created our own platform called CanvasAI. It provides machine-learning plumbing and integration with your services. So, we've integrated Azure OpenAI through Canvas. We're also looking at some hard interface modeling from AWS as well. We access Azure OpenAI solutions for our business workflow via the platform, not directly from the service provider. There are security considerations we're working through with the security team, etc. Canvas provides the control plane, which handles RBAC for user registration and model management. For internal processes, we're using OpenAI to create partner call reports, like summarizing quarterly zip code data and other financial models. We also use it for legal and contract stuff, like comparing SOWs and contracts. For user experience, we've created a chatbot powered by OpenAI to answer HR, travel, and expense-related questions.
Serge Dahdouh - PeerSpot reviewer
A user-friendly platform that automatizes machine learning techniques with minimal effort
We work with clients who request the implementation of a certain document into a chatbot. Because of the limited knowledge of AI, our task is to link that file to the ML and provide a platform that can work as a customer service. We previously used LangChain Phython, but now it is done through Vertex AI.

Quotes from Members

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

Pros

"I would rate it a nine out of ten."
"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 is easy to integrate and develop a solution. Most customers are concerned about the security of their data and how cost-effective it is. We have developed some methodologies so that our customers will not be charged too much for these OpenAI services but will still get the same kind of performance and results. It's all developed on Azure, so customers also see its benefit."
"Azure OpenAI is very easy to use instead of AWS services."
"The document intelligence feature has significantly aided in our operations, facilitating the creation of descriptive content."
"My goal was to create an experience where project managers don't have to read through entire documents. Instead, they can ask a question and receive relevant point analysis. This analysis identifies the document and specific section where the information resides. Previously, users had to rely on these document references. Now, Azure OpenAI enhances the experience by providing the answer directly in the user's own language, relevant to their context."
"Azure OpenAI has significantly reduced costs and increased efficiency in tasks such as aggressive testing of systems to avoid anomalies and trust issues."
"The high precision of information extraction is the most valuable feature."
"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 monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"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."
"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."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
"It provides the most valuable external analytics."
"Vertex comes with inbuilt integration with GCP for data storage."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
 

Cons

"The solution needs to accommodate smaller companies."
"Our customers are worried about data management, ethical, and security issues."
"Sometimes, it gives answers in English, even when the request is in Polish."
"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."
"We encountered challenges related to question understanding."
"I have found the tool unreliable in certain use cases. I aim to enhance the system's latency, particularly in responding to calls. Occasionally, calls don't respond, so I want to improve reliability."
"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."
"Deployment was slightly complex for me to understand."
"Both major systems, Azure and Google, are not yet stabilized, especially their customer support."
"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"I think the technical documentation is not readily available in the tool."
"The tool's documentation is not good. It is hard."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"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 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."
 

Pricing and Cost Advice

"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."
"The cost is pretty high. Even by US standards, you would find it high."
"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."
"While the product meets our business requirements well, I consider it relatively expensive, especially for individual users like myself."
"If you consider the long-term aspect of any project, Azure OpenAI is a costly solution."
"Cost-wise, the product's price is a bit on the higher side."
"The solution's pricing depends on the services you will deploy."
"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
Financial Services Firm
14%
Computer Software Company
13%
Manufacturing Company
11%
Government
6%
Computer Software Company
13%
Financial Services Firm
13%
Manufacturing Company
9%
Retailer
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

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?
The pricing is very good for handling various kinds of jobs. While small jobs are manageable, more complex jobs require a higher model, which is a bit challenging.
What needs improvement with Azure OpenAI?
Maybe with the next release, the response will be more precise and more human-like.
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?
I'm not sure if I have suggestions for improvement. Based on my comparison between the two, Vertex has various additional functionalities that Azure doesn't provide.
 

Overview

Find out what your peers are saying about Azure OpenAI vs. Google Vertex AI and other solutions. Updated: January 2025.
839,319 professionals have used our research since 2012.