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.
"Generative AI or GenAI seems to be the best part of the solution."
"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."
"We can use the solution to implement our tasks and models quickly."
"The most valuable feature is the ALM."
"Azure OpenAI is easy to use because the endpoints are created, and we just need to pass our parameters and info."
"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."
"Azure OpenAI is useful for benchmarking products."
"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."
"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."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
"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."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"Deployment was slightly complex for me to understand."
"We are awaiting the new updates like multi-model capabilities."
"One area for improvement is providing more flexibility in configuration and connectivity with external tools."
"Azure OpenAI is not an optimized tool yet, making it one of its shortcomings where improvements are required."
"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."
"The solution's response is a bit slow sometimes."
"The solution needs to accommodate smaller companies."
"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."
"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 would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"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."
Azure OpenAI is ranked 2nd in AI Development Platforms with 23 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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