Azure OpenAI vs Google Vertex AI comparison

Microsoft and Google are both solutions in the AI Development Platforms category. Microsoft is ranked #1 with an average rating of 7.7, while Google is ranked #3 with an average rating of 8.1. Microsoft holds a 6.5% mindshare in AIDP, compared to Google’s 8.1% mindshare. Additionally, 93% of Microsoft users are willing to recommend the solution, compared to 100% of Google users who would recommend it.

Azure OpenAI

Read 35 Azure OpenAI reviews

5,447 Views
5,447 Comparison Views

93% willing to recommend

Google Vertex AI

Read 14 Google Vertex AI reviews

6,731 Views
5,991 Comparison Views

100% willing to recommend

Azure OpenAI

Google Vertex AI

Comparison Buyer's Guide

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Executive SummaryUpdated on Nov 2, 2025

Google Vertex AI and Azure OpenAI compete in the AI platform space, offering tools for machine learning and AI model development. Google Vertex AI may have an edge in ease of integration and feature richness, while Azure OpenAI offers strong support, making it valuable for specific enterprise needs.

Features: Google Vertex AI emphasizes integration with Google Cloud services, offering AutoML, customizable training capabilities, and advanced MLOps for model management. When it comes to model training and AI, tasks that previously required large teams are streamlined with features like the Feature Store, allowing centralized feature creation and version control. For monitoring, Vertex AI provides an intuitive system that delivers notifications of issues, aiding data scientists in maintaining model performance. In comparison, Azure OpenAI offers seamless integration with Microsoft's ecosystem, providing capabilities for generating, understanding, and analyzing content with advanced language models. Its conversational AI capability allows users to query documents naturally, and its data generation capabilities enable efficient processing and retrieval of information from large volumes of data.

Room for Improvement: Google Vertex AI could benefit from enhanced user guidance for beginners to reduce the reliance on extensive coding. Improved real-time data analytics and customer-specific customizations could enhance its service. Meanwhile, Azure OpenAI could improve by offering more flexible geographical availability, better subscription models to handle rate limiting, and adaptive learning features for better response accuracy. Additionally, integrating further with non-Microsoft platforms could widen its business applicability.

Ease of Deployment and Customer Service: Google Vertex AI supports multiple deployment options, including hybrid and on-premise, and provides comprehensive documentation and tools. Azure OpenAI stands out with strong Microsoft-backed support, facilitating enterprise-specific needs with personalized service. While Google Vertex AI's extensive self-service resources serve as a valuable asset, Azure's dedicated support might appeal more to enterprises needing tailored service.

Pricing and ROI: Google Vertex AI offers competitive pricing with flexible pay-as-you-go models, which are cost-effective for organizations with varying workloads. Azure OpenAI may have a higher initial cost but offers strong ROI through its integration within Microsoft's product suite. While Google Vertex AI presents lower initial costs, Azure OpenAI's broader enterprise features and support can lead to significant overall ROI.

To learn more, read our detailed Azure OpenAI vs. Google Vertex AI Report (Updated: December 2025).

Buyer's Guide

Azure OpenAI vs. Google Vertex AI

December 2025

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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

Azure OpenAI

Ranking in AI Development Platforms

1st

Average Rating

7.8

Reviews Sentiment

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Google Vertex AI

Ranking in AI Development Platforms

3rd

Average Rating

8.2

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

AI-Agent Builders (6th)

Mindshare comparison

As of January 2026, in the AI Development Platforms category, the mindshare of Azure OpenAI is 6.5%, down from 15.7% compared to the previous year. The mindshare of Google Vertex AI is 8.1%, down from 17.0% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms Market Share Distribution
Product	Market Share (%)
Azure OpenAI	6.5%
Google Vertex AI	8.1%
Other	85.4%

AI Development Platforms

Featured Reviews

Rudra Chand

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.

Read full review

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

Quotes from Members

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

Pros

"The most valuable features include analyzing comments and preparing requests for customers, making emails easier and faster."

"GPT was useful for our projects."

"Our clients are interested in building knowledge bases, particularly in child welfare. In this domain, we focus on supporting caseworkers by compiling and organizing relevant information. This information is then stored in a database using a query. The database generates summaries and reminders for specific actions and even facilitates sending emails to parents or other relevant parties. The system's complexity is tailored to the specific needs of child welfare cases. Additionally, we're exploring opportunities to assist a healthcare organization. Specifically, we're working on streamlining the process of filling out forms required for insurance claims. This effort aims to ensure that hospitals can receive funding or payment for the care they provide."

"The AI search functionality is particularly effective, as it creates summaries from data."

"Generative AI or GenAI seems to be the best part of the solution."

"Azure OpenAI has significantly reduced costs and increased efficiency in tasks such as aggressive testing of systems to avoid anomalies and trust issues."

"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."

"Its ability to understand and respond well to queries, including language translation for clients, is beneficial."

More Azure OpenAI pros

"The support is perfect and fantastic."

"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."

"It provides the most valuable external analytics."

"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 comes with inbuilt integration with GCP for data storage."

"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."

"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."

More Google Vertex AI pros

Cons

"The product must improve its dashboards."

"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."

"We encountered challenges related to question understanding."

"There is room for improvement in their support services."

"Azure OpenAI will be expensive if you want to implement it as a permanent solution for a customer."

"There could be an ability to generate visual data, such as architecture diagrams."

"Azure OpenAI should use more specific sources like academic articles because sometimes the source can't be found."

"One major drawback of Azure OpenAI is its availability, as it's not consistently accessible for effective use."

More Azure OpenAI cons

"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."

"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."

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

"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."

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

"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 think the technical documentation is not readily available in the tool."

"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."

More Google Vertex AI cons

Pricing and Cost Advice

"The cost structure depends on the volume of data processed and the computational resources required."

"The pricing is acceptable, and it's delivering good value for the results and outcomes we need."

"The cost is quite high and fixed."

"While the product meets our business requirements well, I consider it relatively expensive, especially for individual users like myself."

"The tool costs around 20 dollars a month."

"We started with monthly payments, but we plan to switch to yearly billing once we've stabilized our solution."

"Cost-wise, the product's price is a bit on the higher side."

"If you consider the long-term aspect of any project, Azure OpenAI is a costly solution."

More Azure OpenAI 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."

"The solution's pricing is moderate."

"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"

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Computer Software Company

12%

Manufacturing Company

10%

Comms Service Provider

Computer Software Company

13%

Financial Services Firm

Manufacturing Company

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	16
Midsize Enterprise	1
Large Enterprise	19

By reviewers
Company Size	Count
Small Business	5
Midsize Enterprise	3
Large Enterprise	7

Questions from the Community

What do you like most about Azure OpenAI?

The product is easy to integrate with our IT workflow.

See all answers

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 ...

See all answers

What needs improvement with Azure OpenAI?

I have not thought about how Azure OpenAI can be improved. I have not explored AI model customization in Azure OpenAI, but it's not a very common use case to do model customization. There are certa...

See all answers

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?

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 othe...

See all answers

What is your primary use case for Google Vertex AI?

We are developing AI models and agents using Google Vertex AI platform, and we are deploying them using Google Vertex AI platform on Google Cloud Platform, GCP. With just one single platform, Googl...

See all answers

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TensorFlow vs Azure OpenAI

Compared 6% of the time

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Compared 5% of the time

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Compared 13% of the time

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Compared 9% of the time

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Compared 6% of the time

Amazon Bedrock vs Google Vertex AI

Compared 6% of the time

TensorFlow vs Google Vertex AI

Compared 4% of the time

More Google Vertex AI Competitors

Product Reports

Buyer's Guide

Azure OpenAI

January 2026

Download Azure OpenAI product report

Buyer's Guide

Google Vertex AI

January 2026

Download Google Vertex AI product report

Overview

Azure OpenAI integrates advanced language models with robust security for precise information extraction and task automation. Its seamless Azure integration and drag-and-drop interface simplify implementation and enhance accessibility.

Azure OpenAI offers a comprehensive suite of features designed for efficient data processing and task automation. It provides high precision in extracting information and strong conversational capabilities, crucial for developing chatbots and customer support systems. Its integration with Azure ensures seamless data handling and security, addressing key enterprise requirements. Users can employ its versatile GPT models for diverse applications such as predictive analytics, summarizing large documents, and competitive benchmarking. Despite its strengths, it faces challenges like latency, inadequate regional support, and limited integration of new technologies. Improvements in model fine-tuning and more flexible configuration are desired by users.

What features make Azure OpenAI a reliable choice?

High Precision: Ensures accurate data extraction suitable for critical applications.
Conversational Capabilities: Facilitates the creation of sophisticated chatbots.
Azure Integration: Seamlessly connects with Azure services for enhanced functionality.
Automation Support: Automates repetitive tasks, improving efficiency.
Security Features: Offers robust protection for sensitive data.
Drag-and-Drop Environment: Simplifies user interaction and deployment.

What benefits and ROI should users consider?

Natural Language Processing: Provides superior language model performance.
Quick Implementation: Reduces time to deploy applications.
Scalability: Supports growing business needs with ease.
Versatile GPT Models: Applicable across different industry workflows.

Azure OpenAI is implemented across industries like healthcare, finance, and education for tasks like invoice processing, digitalizing records, and language translation. It enhances policy management, document assimilation, and customer support with predictive analytics and keyword extraction. Organizations in such sectors benefit from streamlined workflows and task automation.

Microsoft

Build, deploy, and scale ML models faster, with pre-trained and custom tooling within a unified artificial intelligence platform.

Google

Buyer's Guide

Azure OpenAI vs. Google Vertex AI

December 2025

Free Report: Azure OpenAI vs. Google Vertex AI

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