Azure OpenAI Reviews

Our team has developed virtual assistants for healthcare organizations, also published in Azure Marketplace. This can be used for a personal assistant perspective. We have also developed an application for one of the fertilizer companies. Here, a farmer can go to their application, click a photo of any disease or progress in the plant, and it will identify what type of fungus or disease that plant has. Accordingly, it will recommend what kind of fertilizers and how to use them. These are a couple of use cases we have worked on.

It is easy to integrate and develop a solution. Most customers are concerned about the security of their data and how cost-effective it is. We have developed some methodologies so that our customers will not be charged too much for these OpenAI services but will still get the same kind of performance and results. It's all developed on Azure, so customers also see its benefit.

I faced one issue with Azure OpenAI: My customer wanted more clarity on the pricing. They were not able to get proper answers from the documentation or the pricing calculator. I suggest that Microsoft maintain standardization in the pricing details published in the documentation and the pricing calculator. 

Sometimes, customers check the prices independently, but the details of the pricing parameter and the documentation sheet are inconsistent. This confuses customers, making them unsure if the service is live or how to test it.

I have been working with the product for a couple of days. 

I haven't faced any issues with the tool's stability. 

The tool is highly scalable. I don't think any of my customers have faced any issues from a performance or scalability perspective when the load on their website has increased. We designed the solution to be highly scalable and reliable so they don't face challenges at the application or performance level. I have not seen any customers complaining about issues or performance problems. 

My company has seven to eight clients using Azure OpenAI. Because of data concerns, all features should be available in at least one data center in each region. For example, there are three data centers in India, and until recently, Azure OpenAI was available in only four regions. Now, it is also available in one of the data centers in India. If Microsoft introduces a new product, it should ensure it is available in at least one data center per region so that customers from that region can validate it.

Azure OpenAI's deployment is easy. It depends on a couple of things, including the solution we are developing. If it is a normal chatbot, it shouldn't take more than two to three days to complete the project. However, it might take longer if the customer has a very complex environment.

You can enjoy cost savings because you need only less expertise. Traditionally, IT services or SLM models require a lot of computing power to train models, but Microsoft has already invested in them. Customers can directly use this high-efficiency model available on the Microsoft platform. Microsoft has already done that, they don't have to build anything from scratch or use high computing resources. So, there is a good ROI.

I rate the overall product an eight out of ten. You should start by researching and experimenting with Azure OpenAI. You can create a simple chatbot or a computer vision model that identifies objects in images. This will help you understand how it works and develop use cases based on your requirements. I would also suggest testing those use cases.

In India, I'm seeing that customers across various industries, whether manufacturing, BFSI, or healthcare, are all starting to use AI in some form. Each customer develops solutions for cross-selling and processing products based on specific use cases. They design user stories for their customers, create offers to grab attention, integrate interactive chatbots into their applications or websites, and develop AI-based visitor and document management systems. Everyone wants to use AI differently, but no two use cases are the same.

We are currently exploring the solution's LLM chat and question-and-answer-based endpoints.

The most valuable feature of the solution is the accuracy of ChatGPT. It is something we cannot reproduce with any open-source LLM. Azure OpenAI is easy to use because the endpoints are created, and we just need to pass our parameters and info.

Azure OpenAI will be expensive if you want to implement it as a permanent solution for a customer. I have explored Azure OpenAI from a purely LLM perspective. Its endpoints are currently enough for us to communicate with their ChatGPT instance and get results. I don't know if Azure has anything implemented for images, videos, and other endpoints.

We have been exploring ChatGPT 3.5 and 4 versions for one and a half years.

Around 20 to 50 users were using Azure OpenAI for one of our projects.

Whenever we encounter issues, we try to raise them on the portal, and we get a resolution from there.

Microsoft takes care of the deployment. We just get our instance, and we have to communicate with it.

If you consider the long-term aspect of any project, Azure OpenAI is a costly solution. However, the solution is cheap if you just want to see results or try some POC in the initial stages. This is because you don't need to spin up your instance; you can just consume things and see the results.

Azure OpenAI is a straightforward solution. After configuring it, you will get your endpoints. You then need to call the endpoints and pass the details. Azure OpenAI is a straightforward tool that is implemented in such a way that even a fresher or junior developer can learn to use it easily.

Overall, I rate the solution an eight out of ten.

I use Azure OpenAI to create message dashboards for my company.

The most valuable feature of Azure OpenAI stems from the GPT-3.5 models it provides to its users. The information that I provide is based on the information I found through research. I have not started to use the solution for the development part.

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. It would be great if Azure OpenAI could increase the limit of the knowledge of its chatbot. If I have datasets for fine-tuning, the chatbot can only answer the user's queries related to training data, meaning the chatbot would not provide any knowledge to its users from the outside world.

I have been using Azure OpenAI for maybe a month.

I work in a company involved in some business related to messaging, where I did not have a reason to think about scalability options provided by the solution.

I have not contacted the solution's technical support yet since I am still involved in some research related to the product.

I have experience with LangChain. LangChain uses OpenAI.

The documentation provided by Azure OpenAI is clear enough. Based on my research, I think the product's initial setup phase would be easy.

The solution is deployed on the cloud services from Azure.

Though in my company, we are in the process related to the deployment of Azure OpenAI, it has not been deployed yet.

I want Azure OpenAI since it is the best solution related to language processing.

The only suggestion I can provide to those who plan to use Azure OpenAI is to consider providing the tool with clear prompts. Azure OpenAI is one of the best tools and easy to use.

Sometimes, when I try some prompts, Azure OpenAI fails to understand them. Overall, Azure OpenAI is a really helpful tool.

I rate the overall solution a nine out of ten.

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 solution allows you to work through the extraction and summarization of unstructured documents.

The solution needs to accommodate smaller companies. 

I have been using the product for four months. 

I rate Azure OpenAI a nine out of ten. 

I rate the product's scalability a three out of ten. 

Azure OpenAI's deployment is straightforward. It is quick to deploy and can be completed in weeks. We had three resources deploying it. 

The tool costs around 20 dollars a month. 

I rate Azure OpenAI a nine out of ten. 

When I write documentation, I use the solution for translating anything and researching.

Azure OpenAI is useful for benchmarking products. We can search for all metrics with AI tools and compare our products with other products.

The solution’s stability could be improved. Azure OpenAI should use more specific sources like academic articles because I can't find the source.

I have been using Azure OpenAI for more than a year.

I rate the solution a nine out of ten for stability.

I rate the solution’s scalability a seven out of ten.

The solution’s initial setup is very easy.

I can deploy the solution by myself in a few seconds.

We pay a licensing fee for Azure OpenAI. The solution's pricing is normal worldwide but expensive in Turkey because Turkey's currency is different.

Azure OpenAI provides me with a lot of benefits. It's quick and saves me time.

Overall, I rate the solution an eight out of ten.

The most valuable feature is the ALM. It can execute clarifications against security. 

The dialogue manager needs to be improved. 

I have been using Azure OpenAI for six months. 

It is a scalable solution. 

I would rate the overall solution a seven out of ten. 

We utilize the product for various applications that handle unstructured or semi-structured data, such as question-and-answer systems, chatbots, and text-generation tasks.

The platform's most valuable features are its data generation capabilities and ability to retrieve information from large volumes of data. These features enable efficient processing and retrieval of relevant information.

While the product is closely linked with several other products offered by Microsoft Azure, especially when building generic AI solutions, some aspects could still be enhanced. One area for improvement is providing more flexibility in configuration and connectivity with external tools.

We have been using Azure OpenAI for 5 months.

I rate the platform's stability an eight.

The product is highly scalable. It is expensive and suitable for medium enterprises. Once all the components are configured, we can easily accommodate different requirements. 

I have had limited interaction with the technical support team but heard of delays in response, likely due to various factors.

Neutral

I have previously considered using a different solution before exploring Azure OpenAI. While I haven't utilized ChatGPT extensively, I find OpenAI, particularly in ChatGPT, quite effective. Additionally, many customers prefer the Microsoft platform due to its inherent trust and seamless integration with other Azure services. This trust factor and integration capability make it easier for customers to build solutions that connect various components within the Azure ecosystem, which is perceived as a superior feature.

The initial setup for Azure OpenAI can be complex, as various roles and access-related settings must be configured. This process can be time-consuming, especially for users unfamiliar with the Azure platform.

The platform offers a flexible pricing model which depends on the features and capabilities we utilize.

The product is easy to integrate with our IT workflow. I recommend it to others and rate it a seven out of ten.

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 customers are worried about data management, ethical, and security issues. 

I have been working with the solution for the last six months. 

The tool is stable. 

Azure OpenAI is scalable. 

The tool's deployment can take weeks to complete. 

I rate Azure OpenAI an eight out of ten. Don't focus on the technology, but on the problem you try to solve.