Azure OpenAI vs Google Vertex AI comparison

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6,658 views|6,241 comparisons
90% willing to recommend
Google Logo
6,695 views|6,051 comparisons
100% willing to recommend
Comparison Buyer's Guide
Executive Summary
Updated on Mar 6, 2024

We compared Google Vertex AI and Azure OpenAI based on our user's reviews in several parameters.

Google Vertex AI is praised for its advanced machine learning capabilities, efficient model training, and seamless integration. Users appreciate the scalability, reliability, and positive ROI it offers. On the other hand, Azure OpenAI stands out for its seamless integration with Azure services, flexibility, affordability, and robust machine learning capabilities. Users value the extensive documentation and superior customer service provided by Microsoft. Overall, Google Vertex AI prioritizes user-friendly interfaces and streamlined processes, while Azure OpenAI emphasizes integration, scalability, and robust support.

Features: Google Vertex AI offers advanced machine learning capabilities, seamless integration with existing infrastructure, efficient and automated model training, a user-friendly interface, handling large datasets, accurate predictions, and real-time insights. In contrast, Azure OpenAI prioritizes seamless integration with other Azure services, flexible resource scaling, robust machine learning capabilities, and extensive documentation and support.

Pricing and ROI: The setup cost of Google Vertex AI is considered reasonable and competitive, with a straightforward and hassle-free process that requires minimal effort. The licensing is flexible and accommodating to different business needs. On the other hand, Azure OpenAI is praised for its affordable pricing, minimal setup cost, and flexible licensing options. The setup process is smooth and hassle-free, requiring minimal efforts, and the licensing options are adaptable to meet varying user requirements., Users have reported positive ROI from Google Vertex AI, praising its efficiency, effectiveness, cost-effectiveness, and innovative features. Azure OpenAI users have also experienced positive ROI, with increased efficiency, cost reduction, and valuable insights. Both platforms offer valuable investment opportunities.

Room for Improvement: Google Vertex AI users have mentioned the need for better customization options, comprehensive documentation, and improved customer support. Azure OpenAI users have expressed concerns about certain functions and have suggested improvements.

Deployment and customer support: The reviews for Google Vertex AI indicate that deployment and setup may have different time periods, ranging from a week to three months. In contrast, the reviews for Azure OpenAI suggest that deployment and setup may refer to the same period, typically taking a week or two., The customer service of Google Vertex AI is commendable and satisfying, offering reliable and professional assistance. On the other hand, Azure OpenAI's customer service is highly appreciated, regarded positively, and goes above and beyond to ensure a smooth user experience.

The summary above is based on 17 interviews we conducted recently with Google Vertex AI and Azure OpenAI users. To access the review's full transcripts, download our report.

To learn more, read our detailed Azure OpenAI vs. Google Vertex AI Report (Updated: May 2024).
772,422 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
"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.""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.""The most crucial aspect is the conversational capability, where you can simply ask questions, and it provides answers based on your content and documents, particularly tailored to your specific environment.""The high precision of information extraction is the most valuable feature.""Azure OpenAI is easy to use because the endpoints are created, and we just need to pass our parameters and info.""The product is easy to integrate with our IT workflow.""We can use the solution to implement our tasks and models quickly.""The solution has a very drag-and-drop environment. Instead of coding something from scratch or understanding any concept in extensive depth before deployment, this is good. Plus, they have an auto dataset, which means you can choose any dataset they have instead of providing your own. So that's also pretty nice."

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

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"One area for improvement is providing more flexibility in configuration and connectivity with external tools.""There are no available updates of information that are currently provided.""We are awaiting the new updates like multi-model capabilities.""Azure OpenAI is not available in all regions, and its technical support should be improved.""The product must improve its dashboards.""Since we don't train the model on our data, it's a struggle to ensure OpenAI answers questions exclusively from our data. During user testing, we found ways to make the system provide answers from outside sources.""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.""The product features themselves are fine. However, with Microsoft scaling the service so much, the support structure needs to keep pace. When solving complex issues, the process of interacting with Microsoft can be quite time-consuming."

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"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions.""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.""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.""Google Vertex AI is good in machine learning and AI, but it lacks optimization."

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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 pretty high. Even by US standards, you would find it high."
  • "The cost is quite high and fixed."
  • "The tool costs around 20 dollars a month."
  • "Cost-wise, the product's price is a bit on the higher side."
  • "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."
  • "According to the negotiations taking place and the contract, there is a need to make either monthly or yearly payments to use the solution."
  • More Azure OpenAI Pricing and Cost Advice →

  • "The price structure is very clear"
  • "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."
  • More Google Vertex AI Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:The product is easy to integrate with our IT workflow.
    Top Answer:We've been a long-term Microsoft shop with an enterprise agreement, so that gives us some advantages. As an Azure-certified partner, we receive preferred pricing. However, AWS also has a very… more »
    Top Answer:While the product is closely linked with several other products offered by Microsoft Azure, especially when building generic AI solutions, some aspects could still be enhanced. One area for… more »
    Top Answer: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… more »
    Top Answer:Vertex 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.
    Top Answer: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… more »
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    The Azure OpenAI service provides REST API access to OpenAI's powerful language models including the GPT-3, Codex and Embeddings model series. These models can be easily adapted to your specific task including but not limited to content generation, summarization, semantic search, and natural language to code translation. Users can access the service through REST APIs, Python SDK, or our web-based interface in the Azure OpenAI Studio.

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

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    Buyer's Guide
    Azure OpenAI vs. Google Vertex AI
    May 2024
    Find out what your peers are saying about Azure OpenAI vs. Google Vertex AI and other solutions. Updated: May 2024.
    772,422 professionals have used our research since 2012.

    Azure OpenAI is ranked 2nd in AI Development Platforms with 24 reviews while Google Vertex AI is ranked 3rd in AI Development Platforms with 5 reviews. Azure OpenAI is rated 8.0, while Google Vertex AI is rated 8.4. The top reviewer of Azure OpenAI writes "Created a chatbot powered by OpenAI to answer HR, travel, and expense-related questions". On the other hand, the top reviewer of Google Vertex AI writes "A user-friendly platform that automatizes machine learning techniques with minimal effort". Azure OpenAI is most compared with Amazon SageMaker, Microsoft Azure Machine Learning Studio, Hugging Face, Google Cloud AI Platform and IBM Watson Studio, whereas Google Vertex AI is most compared with Microsoft Azure Machine Learning Studio, Hugging Face, Amazon SageMaker, TensorFlow and AWS Machine Learning. See our Azure OpenAI vs. Google Vertex AI report.

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    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.