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 12.9% mindshare in AIDP, compared to Google’s 14.0% 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

8,663 Views
7,905 Comparison Views

93% willing to recommend

Google Vertex AI

Read 10 Google Vertex AI reviews

8,938 Views
8,024 Comparison Views

100% willing to recommend

Azure OpenAI

Google Vertex AI

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Jan 12, 2025

Google Vertex AI and Azure OpenAI are two prominent solutions vying in the AI platform domain. Based on available data, Google Vertex AI appears to have an edge in terms of pricing and customer support, while Azure OpenAI is noted for its comprehensive feature set, especially for enterprises integrated with Microsoft's ecosystem.

Features: Google Vertex AI offers integrated machine learning tools, versatile model training, and deployment capabilities, and a feature store for centralized management. Azure OpenAI showcases robust natural language processing, integration with Microsoft services, and a wide range of GPT models for efficient data processing and retrieval.

Room for Improvement: Google Vertex AI could enhance its NLP capabilities, improve integration with non-Google services, and lower costs for smaller businesses. Azure OpenAI needs to address geographic availability, subscription constraints, and refine its response accuracy. Additionally, reducing initial setup complexity would benefit new users.

Ease of Deployment and Customer Service: Google Vertex AI provides a straightforward and scalable cloud deployment process with responsive customer service. Azure OpenAI offers smooth deployment within Microsoft’s infrastructure and extensive support resources for users familiar with Azure, benefiting from its integration-centric ecosystem.

Pricing and ROI: Google Vertex AI’s flexible pricing models are generally seen as more cost-effective, catering to businesses of varying sizes and resulting in favorable ROI. While Azure OpenAI can involve higher initial costs due to its feature-rich offering, its integration with other Microsoft products can justify the investment for enterprises seeking comprehensive solutions.

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

Buyer's Guide

Azure OpenAI vs. Google Vertex AI

March 2025

Download the complete report

Helped 844,944 peers since 2012

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 April 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 12.9%, down from 21.1% compared to the previous year. The mindshare of Google Vertex AI is 14.0%, down from 20.3% 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."

"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."

"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."

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

"We can use the solution to implement our tasks and models quickly."

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

"The product is easy to integrate with our IT workflow."

"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."

More Azure OpenAI pros

"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 monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."

"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."

"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."

"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."

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

"It provides the most valuable external analytics."

"The integration of AutoML features streamlines our machine-learning workflows."

More Google Vertex AI pros

Cons

"The UI could be a little easier."

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

"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."

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

"Maybe with the next release, the response will be more precise and more human-like."

"Sometimes, the responses are repetitive."

"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."

"There are certain shortcomings with the product's scalability and support team where improvements are required."

More Azure OpenAI cons

"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."

"Google Vertex AI is good in machine learning and AI, but it lacks optimization."

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

"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."

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

"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

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

"According to the negotiations taking place and the contract, there is a need to make either monthly or yearly payments to use the solution."

"I rate the product pricing six out of ten."

"The cost is quite high and fixed."

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

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

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

"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."

More Azure OpenAI pricing and cost advice

"The price structure is very clear"

"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 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."

See which vendors are best for you

Use our free recommendation engine to learn which AI Development Platforms solutions are best for your needs.

See recommendations

844,944 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

15%

Computer Software Company

13%

Manufacturing Company

11%

Government

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?

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.

See all answers

What needs improvement with Azure OpenAI?

Maybe with the next release, the response will be more precise and more human-like.

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

Microsoft Azure Machine Learning Studio vs Azure OpenAI

Compared 8% of the time

Google Cloud AI Platform vs Azure OpenAI

Compared 8% 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 20% of the time

Hugging Face vs Google Vertex AI

Compared 10% of the time

Amazon SageMaker vs Google Vertex AI

Compared 7% of the time

TensorFlow vs Google Vertex AI

Compared 5% 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

March 2025

Download Azure OpenAI product report

Buyer's Guide

Google Vertex AI

March 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

March 2025

Free Report: Azure OpenAI vs. Google Vertex AI

Find out what your peers are saying about Azure OpenAI vs. Google Vertex AI and other solutions. Updated: March 2025.

DOWNLOAD NOW

844,944 professionals have used our research since 2012.

See our Azure OpenAI vs. Google Vertex AI report.

See our list of best AI Development Platforms vendors.

We monitor all AI Development Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.