We performed a comparison between Azure OpenAI and PyTorch 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."Azure OpenAI is very easy to use instead of AWS services."
"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."
"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."
"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."
"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 useful for benchmarking products."
"The product saves a lot of time."
"It's been pretty scalable in terms of using multiple GPUs."
"The tool is very user-friendly."
"I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code."
"yTorch is gaining credibility in the research space, it's becoming easier to find examples of papers that use PyTorch. This is an advantage for someone who uses PyTorch primarily."
"The framework of the solution is valuable."
"Its interface is the most valuable. The ability to have an interface to train machine learning models and construct them with the high-level interface, without excess busting and reconstructing the same technical elements, is very useful."
"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."
"There are certain shortcomings with the product's scalability and support team where improvements are required."
"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."
"Deployment was slightly complex for me to understand."
"Azure OpenAI should use more specific sources like academic articles because sometimes the source can't be found."
"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."
"The solution needs to accommodate smaller companies."
"The product must improve its dashboards."
"The training of the models could be faster."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
"On the production side of things, having more frameworks would be helpful."
"I would like a model to be available. I think Google recently released a new version of EfficientNet. It's a really good classifier, and a PyTorch implementation would be nice."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"PyTorch could make certain things more obvious. Even though it does make things like defining loss functions and calculating gradients in backward propagation clear, these concepts may confuse beginners. We find that it's kind of problematic. Despite having methods called on loss functions during backward passes, the oral documentation for beginners is quite complex."
Azure OpenAI is ranked 2nd in AI Development Platforms with 19 reviews while PyTorch is ranked 10th in AI Development Platforms with 6 reviews. Azure OpenAI is rated 8.0, while PyTorch is rated 8.6. 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 PyTorch writes "User-friendly, easy to learn, performs well, and is more advanced than other tools". Azure OpenAI is most compared with Google Vertex AI, Amazon SageMaker, Microsoft Azure Machine Learning Studio, Hugging Face and Google Cloud AI Platform, whereas PyTorch is most compared with OpenVINO, MXNet, Microsoft Azure Machine Learning Studio, Caffe and Google Vertex AI. See our Azure OpenAI vs. PyTorch 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.
