We performed a comparison between Azure OpenAI and Google Cloud AI Platform based on real PeerSpot user reviews.
Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."OpenAI's models are more mature than Watson's. They offer a wider range of features and provide richer outputs."
"The product saves a lot of time."
"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."
"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 very easy to use instead of AWS services."
"The high precision of information extraction is the most valuable feature."
"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 product's initial setup phase was pretty easy."
"A range of a a wide range of algorithms, EIM voice mails, which can be plugged in right away into your solution into into into our solution, and then have platform that provides know, to to come up with an operational solution really quick."
"Some of the valuable features are the vast amount of services that are available, such as load balancer, and the AI architecture."
"I think the user interface is quite handy, and it is easy to use as compared to the other cloud platforms."
"The initial setup is very straightforward."
"Since the model could be trained in just a couple of hours and deploying it took only a few minutes, the entire process took less than an hour."
"On GCP, we are exposing our API services to our clients so that they send us their information. It can be single individual records or it can be a batch of their clients."
"The solution is able to read 90% of the documents correctly with a 10% error rate."
"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."
"Azure OpenAI is not an optimized tool yet, making it one of its shortcomings where improvements are required."
"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."
"Our customers are worried about data management, ethical, and security issues."
"There is room for improvement in their support services."
"We are awaiting the new updates like multi-model capabilities."
"We encountered challenges related to question understanding."
"There are no available updates of information that are currently provided."
"I think it's the it it also has has evolved quite a bit over the last few years, and Google Cloud folks have been getting, more and more services. But I think from a improvement standpoint, so maybe they can look at adding more algorithms, so adding more AI algorithms to their suite."
"The solution can be improved by simplifying the process to make your own models."
"One thing that I found is that Azure ML does not directly provide you with features on Google Cloud AI Platform, whereas Vertex provides some features of the platform."
"At first, there were only the user-managed rules to identify the best attributes of the individual. Then, we came up with a truth set and developed different machine learning models with the help of that truth set, so now it's completely machine learning."
"The initial setup was straightforward for me but could be difficult for others."
"It could be more clear, and sometimes there are errors that I don't quite understand."
"Customizations are very difficult, and they take time."
Azure OpenAI is ranked 2nd in AI Development Platforms with 17 reviews while Google Cloud AI Platform is ranked 6th in AI Development Platforms with 7 reviews. Azure OpenAI is rated 8.0, while Google Cloud AI Platform is rated 7.8. 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 Cloud AI Platform writes "An AI platform AI Platform to train your machine learning models at scale, to host your trained model in the cloud, and to use your model to make predictions about new data". Azure OpenAI is most compared with Google Vertex AI, Amazon SageMaker, Microsoft Azure Machine Learning Studio, Hugging Face and IBM Watson Studio, whereas Google Cloud AI Platform is most compared with Microsoft Azure Machine Learning Studio, IBM Watson Machine Learning, Google Vertex AI, Hugging Face and Amazon SageMaker. See our Azure OpenAI vs. Google Cloud AI Platform 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.
