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 7.9, while Google is ranked #2 with an average rating of 8.3. Microsoft holds a 12.0% mindshare in AIDP, compared to Google’s 13.1% 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 33 Azure OpenAI reviews

8,315 Views
7,525 Comparison Views

93% willing to recommend

Google Vertex AI

Read 10 Google Vertex AI reviews

8,778 Views
7,813 Comparison Views

100% willing to recommend

Azure OpenAI

Google Vertex AI

Comparison Buyer's Guide

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Executive SummaryUpdated on Apr 20, 2025

Google Vertex AI and Azure OpenAI compete in the AI platform category, offering business intelligence and data analysis tools. Based on features, Azure OpenAI appears to lead in language model support, while Google Vertex AI provides better integration within the Google ecosystem.

Features: Google Vertex AI offers seamless integration with Google Cloud services, a user-friendly interface, and the capability for custom model training. The Feature Store in Vertex AI allows for centralized feature management with version control. Azure OpenAI highlights its comprehensive language model offerings, ease of use with a drag-and-drop environment, and security features integrated with Microsoft's ecosystem.

Room for Improvement: Google Vertex AI could enhance its language model capabilities and expand the flexibility of deployment options beyond the Google ecosystem. Improved security features are also a potential area of development. Azure OpenAI might benefit from more geographic availability of certain models and addressing subscription constraints and rate limiting. Enhancing model performance consistency and refining query adaptability could also improve user experience.

Ease of Deployment and Customer Service: Google Vertex AI streamlines deployment with comprehensive documentation and support via its cloud network, enabling quick integration. It emphasizes rapid deployment within existing Google infrastructure. Azure OpenAI offers flexible deployment options and robust support through Microsoft's services, allowing for a more customizable deployment model.

Pricing and ROI: Google Vertex AI provides competitive pricing with scalable plans aimed at cost-effectiveness and high ROI, leveraging its analytics capabilities. Although Azure OpenAI may have a higher price point, it offers value through advanced features and complex models, appealing to businesses requiring large-scale AI solutions.

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

Buyer's Guide

Azure OpenAI vs. Google Vertex AI

April 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

6.7

Number of Reviews

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

Ranking in other categories

AI Infrastructure (1st)

Mindshare comparison

As of May 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 12.0%, down from 20.8% compared to the previous year. The mindshare of Google Vertex AI is 13.1%, down from 20.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Viswanath Barenkala

Associate Vice President at State Street

Offers tools to moderate generated content and guidance to safely design applications, but it is not consistently accessible

Instead of a feature, the GPT-4 model has been most beneficial for automating tasks. We transitioned from GPT-3.5 to GPT-4 and actively use it. However, we face limitations due to geographic availability, subscription constraints, and rate limiting, which we are currently negotiating and working towards optimizing. While we haven't formally benchmarked Azure OpenAI's language understanding against industry standards, we find it performs well about 70-80% of the time. Occasionally, we need to refine our queries and adapt our systems accordingly to improve accuracy and effectiveness.

Read full review

Serge Dahdouh

CEO at Les Roches International School of Hotel Management

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.

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

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

"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."

"Azure OpenAI is useful for benchmarking products."

"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."

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

"You just have to write accurate prompts according to your requirements, and the solution gives very good results."

"The most valuable feature is the ALM."

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

More Azure OpenAI pros

"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."

"It provides the most valuable external analytics."

"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 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."

"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."

"Google Vertex AI is an out-of-the-box and very easy-to-use solution."

More Google Vertex AI pros

Cons

"Sometimes, it gives answers in English, even when the request is in Polish."

"The solution needs to accommodate smaller companies."

"Sometimes, the responses are repetitive."

"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 are awaiting the new updates like multi-model capabilities."

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

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

"One area for improvement is providing more flexibility in configuration and connectivity with external tools."

More Azure OpenAI cons

"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."

"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."

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

"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."

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

"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."

"The tool's documentation is not good. It is hard."

More Google Vertex AI cons

Pricing and Cost Advice

"The cost is quite high and fixed."

"Azure OpenAI is a bit more expensive than other services."

"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."

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

"It's a token-based system, so you pay per token used by the model."

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

"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."

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 price structure is very clear"

"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."

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Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

13%

Manufacturing Company

11%

Educational Organization

Computer Software Company

13%

Financial Services Firm

13%

Manufacturing Company

Retailer

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.

See all answers

What is your experience regarding pricing and costs for Azure OpenAI?

In the past, the primary expense involved token limitations which constrained scaling. Recent iterations have increased token allowances, mitigating some challenges associated with concurrent user ...

See all answers

What needs improvement with Azure OpenAI?

Azure ( /products/microsoft-azure-reviews ) could significantly benefit from including more LLM models apart from OpenAI, as I often need to switch clouds when a model doesn't meet my requirements....

See all answers

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...

See all answers

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.

See all answers

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.

See all answers

Comparisons

Hugging Face vs Azure OpenAI

Compared 13% of the time

Amazon SageMaker vs Azure OpenAI

Compared 12% of the time

Google Cloud AI Platform vs Azure OpenAI

Compared 7% of the time

Microsoft Azure Machine Learning Studio vs Azure OpenAI

Compared 7% of the time

IBM Watson Studio vs Azure OpenAI

Compared 3% of the time

More Azure OpenAI Competitors

Microsoft Azure Machine Learning Studio vs Google Vertex AI

Compared 18% of the time

Hugging Face vs Google Vertex AI

Compared 11% of the time

Amazon SageMaker vs Google Vertex AI

Compared 7% of the time

TensorFlow vs Google Vertex AI

Compared 4% of the time

IBM Watson Studio vs Google Vertex AI

Compared 3% of the time

More Google Vertex AI Competitors

Product Reports

Buyer's Guide

Azure OpenAI

May 2025

Download Azure OpenAI product report

Buyer's Guide

Google Vertex AI

April 2025

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

April 2025

Free Report: Azure OpenAI vs. Google Vertex AI

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