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.
"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."
"It's very powerful. It allows users to query our documents using natural language and receive answers in the same way. This makes our product information much more accessible than traditional keyword-based search."
"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 feature of Azure OpenAI stems from the GPT-3.5 models it provides to its users."
"OpenAI's models are more mature than Watson's. They offer a wider range of features and provide richer outputs."
"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."
"Azure OpenAI is very easy to use instead of AWS services."
"The product saves a lot of time."
"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."
"The solution needs to accommodate smaller companies."
"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."
"Deployment was slightly complex for me to understand."
"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 are certain shortcomings with the product's scalability and support team where improvements are required."
"Sometimes, the responses are repetitive."
"There is room for improvement in their support services."
"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."
"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
Azure OpenAI is ranked 2nd in AI Development Platforms with 17 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.
