We performed a comparison between Azure OpenAI and TensorFlow 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."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."
"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."
"The most valuable feature of Azure OpenAI stems from the GPT-3.5 models it provides to its users."
"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."
"Our clients are interested in building knowledge bases, particularly in child welfare. In this domain, we focus on supporting caseworkers by compiling and organizing relevant information. This information is then stored in a database using a query. The database generates summaries and reminders for specific actions and even facilitates sending emails to parents or other relevant parties. The system's complexity is tailored to the specific needs of child welfare cases. Additionally, we're exploring opportunities to assist a healthcare organization. Specifically, we're working on streamlining the process of filling out forms required for insurance claims. This effort aims to ensure that hospitals can receive funding or payment for the care they provide."
"The product is easy to integrate with our IT workflow."
"Generative AI or GenAI seems to be the best part of the solution."
"It's got quite a big community, which is useful."
"It is open-source, and it is being worked on all the time. You don't have to pay all the big bucks like Azure and Databricks. You can just use your local machine with the open-source TensorFlow and create pretty good models."
"TensorFlow improves my organization because our clients get a lot of investment from their investors and we are progressively improving the products. Every six months we release new features."
"What made TensorFlow so appealing to us is that you could run it on a cluster computer and on a mobile device."
"Edge computing has some limited resources but TensorFlow has been improving in its features. It is a great tool for developers."
"It provides us with 35 features like patch normalization layers, and it is easy to implement using the Kras library when the Kaspersky flow is running behind it."
"Optimization is very good in TensorFlow. There are many opportunities to do hyper-parameter training."
"Our clients were not aware they were using TensorFlow, so that aspect was transparent. I think we personally chose TensorFlow because it provided us with more of the end-to-end package that you can use for all the steps regarding billing and our models. So basically data processing, training the model, evaluating the model, updating the model, deploying the model and all of these steps without having to change to a new environment."
"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."
"We encountered challenges related to question understanding."
"There are no available updates of information that are currently provided."
"Deployment was slightly complex for me to understand."
"Our customers are worried about data management, ethical, and security issues."
"The solution's response is a bit slow sometimes."
"One area for improvement is providing more flexibility in configuration and connectivity with external tools."
"However, if I want to change just one thing in the implementation of TensorFlow functions I have to copy everything that they wrote and I change it manually if indeed it can be amended. This is really hard as it's written in C++ and has a lot of complications."
"TensorFlow deep learning takes a lot of computation power. The more systems you can use, the easier it is. That's a good ability, if you can make a system run immediately at the same time on the same task, it's much faster rather than you having one system running which is slower. Running systems in parallel is a complex situation, but it can improve. There is a lot of work involved."
"I would love to have a user interface like a programming interface. You need to have a set of menus where you can put things together in a graphical interface. The complete automation of the integration of the modules would also be interesting. It’s more like plumbing as opposed to a fully automated environment."
"It doesn't allow for fast the proto-typing. So usually when we do proto-typing we will start with PyTorch and then once we have a good model that we trust, we convert it into TensorFlow. So definitely, TensorFlow is not very flexible."
"It would be cool if TensorFlow could make it easier for companies like us to program for running it across different hyperscalers."
"The solution is hard to integrate with the GPUs."
"It would be nice to have more pre-trained models that we can utilize within layers. I utilize a Mac, and I am unable to utilize AMD GPUs. That's something that I would definitely be like to be able to access within TensorFlow since most of it is with CUDA ML. This only matters for local machines because, in Azure, you can just access any GPU you want from the cloud. It doesn't really matter, but the clients that I work with don't have cloud accounts, or they don't want to utilize that or spend the money. They all see it as too expensive and want to know what they can do on their local machines."
"It would be nice if the solution was in Hungarian. I would like more Hungarian NAT models."
Azure OpenAI is ranked 2nd in AI Development Platforms with 24 reviews while TensorFlow is ranked 4th in AI Development Platforms with 16 reviews. Azure OpenAI is rated 8.0, while TensorFlow is rated 9.0. 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 TensorFlow writes "Effective deep learning, free to use, and highly stable". Azure OpenAI is most compared with Google Vertex AI, Amazon SageMaker, Microsoft Azure Machine Learning Studio, Hugging Face and OpenVINO, whereas TensorFlow is most compared with Microsoft Azure Machine Learning Studio, Google Vertex AI, OpenVINO, IBM Watson Machine Learning and Infosys Nia. See our Azure OpenAI vs. TensorFlow 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.
