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 17.5% mindshare in AIDP, compared to Google’s 18.8% 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 32 Azure OpenAI reviews

8,535 Views
7,918 Comparison Views

93% willing to recommend

Google Vertex AI

Read 10 Google Vertex AI reviews

8,597 Views
7,806 Comparison Views

100% willing to recommend

Azure OpenAI

Google Vertex AI

Comparison Buyer's Guide

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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: January 2025).

Buyer's Guide

Azure OpenAI vs. Google Vertex AI

January 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

8.0

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 January 2025, in the AI Development Platforms category, the mindshare of Azure OpenAI is 17.5%, down from 19.2% compared to the previous year. The mindshare of Google Vertex AI is 18.8%, up from 17.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

AI Development Platforms

Featured Reviews

Anup Karade

Associate Vice President at LTIMindtree

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.

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

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

"Its versatility makes it incredibly useful for technical problem-solving, content creation, data analytics, and more."

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

"Azure OpenAI is useful for benchmarking products."

"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."

"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 very easy to use instead of AWS services."

More Azure OpenAI pros

"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."

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

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

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

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

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

More Google Vertex AI pros

Cons

"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."

"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."

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

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

"The dialogue manager needs to be improved."

"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."

"The solution needs to accommodate smaller companies."

"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 accuracy in these languages requires improvement."

More Azure OpenAI cons

"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."

"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."

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

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

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

"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."

More Google Vertex AI cons

Pricing and Cost Advice

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

"Regarding pricing and licensing, it's a bit complex due to the minimum purchase requirement for PTO units. We're evaluating the best approach between PTE and pay-as-you-go models. Our organization is cautious about committing to PTE due to the fixed bandwidth reservation, while pay-as-you-go doesn't offer enough flexibility. We're discussing these matters with legal teams to ensure compliance and data security."

"The licensing is interaction-based, meaning transactional. It's reasonably priced for now."

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

"The cost is pretty high. Even by US standards, you would find it high."

"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 platform offers a flexible pricing model which depends on the features and capabilities we utilize."

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

More Azure OpenAI pricing and cost advice

"The solution's pricing is moderate."

"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 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%

Educational Organization

Financial Services Firm

13%

Computer Software Company

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

Amazon SageMaker vs Azure OpenAI

Compared 11% of the time

Hugging Face vs Azure OpenAI

Compared 11% of the time

Microsoft Azure Machine Learning Studio vs Azure OpenAI

Compared 9% 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 9% of the time

Amazon SageMaker vs Google Vertex AI

Compared 8% of the time

TensorFlow vs Google Vertex AI

Compared 5% of the time

AWS Machine Learning vs Google Vertex AI

Compared 3% of the time

More Google Vertex AI Competitors

Product Reports

Buyer's Guide

Azure OpenAI

January 2025

Download Azure OpenAI product report

Buyer's Guide

Google Vertex AI

January 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

January 2025

Free Report: Azure OpenAI vs. Google Vertex AI

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