Azure OpenAI Reviews

We work with a lot of enterprise-level customers in healthcare, retail, and manufacturing. The solution can be used to predict diseases based on X-rays in healthcare and for predictive analytics in retail. In manufacturing, we need more than 100 images to train the models. DALL·E can generate more than 1000 images based on the specific descriptions we give.

The tool is the best choice for summarizing the key elements of a document that contains more than 1000 pages. It is the best at summarizing documents.

Sometimes, the responses are repetitive. It is easy to identify whether a text is created by OpenAI. The responses must be made more natural. The keywords must not be repeated every time.

I have been working with the product for more than two years.

The tool is easily scalable.

I have been working with the support team for the past ten years. My experience has always been wonderful.

The initial setup is very easy.

The product saves a lot of time. We use it for our customers and our development processes. Our developers have stopped using Stack Overflow. Most of them use ChatGPT for development. We have integrated the solution with GitHub Copilot, which has expedited our code-writing practices.

Our testers describe the test cases and search for use cases on OpenAI. It gives the right results almost 70 to 80% of the time. Our productivity has increased by 40% to 45%. It is a great return on investment for me. The projects that we delivered with the help of three people, we now deliver with one or two people. The customers also see an increase in productivity.

We are a Microsoft Gold partner. We get access to all the latest technologies. Overall, I rate the product a nine out of ten.

We are assisting our customers in deploying a commercial universal AI solution aimed at aiding them in researching and managing their internal company policies and regulations. To do this, I've extracted all the relevant documents from the HR department and created conversational interfaces for our clients. These interfaces are integrated into various platforms like Microsoft Teams, allowing everyone within the company to interact with the AI.

Its main use for indexing documents and assembling information is highly effective. Previously, we had to meticulously map out each process and step, essentially creating a chatbot for the task.

The most crucial aspect is the conversational capability, where you can simply ask questions, and it provides answers based on your content and documents, particularly tailored to your specific environment.

We encountered challenges related to question understanding. These instances occur when questions are not phrased precisely, resulting in problematic answers. Microsoft is actively addressing this issue and working diligently on improving it.

I have been working with it for six months now.

We have nearly thirty customers using our system, and I can't recall any instances where they've encountered stability issues.

I would rate its scalability capabilities seven out of ten.

We have a direct connection with all the technical support staff in the support area. I would rate it nine out of ten.

Positive

We tried integrating Google in the past, but it didn't proceed as planned so we just stopped it.

The initial setup was straightforward.

The pricing is acceptable, and it's delivering good value for the results and outcomes we need.

My advice is to pay close attention to the content's quality before indexing it within OpenAI. If the documents provided lack good quality, they'll end up with incorrect answers. This is particularly important because the initial setup is not inexpensive and it involves significant investments. Overall, I would rate it nine out of ten.

We can use the solution to implement our tasks and models quickly.

The product must improve its dashboards. I would like to know how many tokens my requests use and how accurate the search is. We are using Python language to find out about it.

I have been using the solution for six months. I am using the latest version of the solution.

I rate the tool’s stability a ten out of ten. I do not have any problems with it.

About 5000 people use the tool in our organization. I rate the tool’s scalability a six out of ten because it doesn’t have automatic installation options.

We have done some POCs with AWS Bedrock and some AI products from Google. We chose Azure OpenAI because we have a preference for the vendor. All our infrastructure is deployed on Azure.

We are maintaining the solution, but the maintenance is not on OpenAI services. We maintain the Python code that we embedded. Our technical team has five people, including two engineers and two data scientists.

The response time of our call centers has improved. The team is using the language models to ask simple questions.

I would recommend the tool to others. Overall, I rate the solution an eight out of ten.

I use the platform for troubleshooting issues. When I encounter a problem, I turn to OpenAI to understand the reasons behind the issue and how to resolve it.

I find the platform's accessibility across all devices to be highly valuable. Additionally, the paid version offers a wide range of GPTs for various tasks, including technical problem-solving, scientific research, communication improvement, writing, art, and design. These GPTs use ChatGPT to build specific tools that leverage AI to expedite work processes. 

The product could be more user-friendly in terms of features. There could be an ability to generate visual data, such as architecture diagrams. This could enhance its utility for various use cases.

I have been working with OpenAI for for approximately two to six months.

The product has been highly stable. I have not encountered any outages so far.

Since OpenAI is cloud-based, it can scale to accommodate any number of users. Each user needs a unique email ID, but otherwise, it is quite flexible in terms of scalability. Approximately 200 or more users in our organization are utilizing it regularly. 

The free version does not offer support. To receive support, one typically needs to purchase a monthly subscription, which costs $19.

OpenAI is a cloud-based solution, so there is no need for local setup. It operates on the cloud and is accessible via cloud-based services.

The platform generates a return on investment in terms of efficiency in problem-solving and task automation has contributed to productivity gains.

While the product meets our business requirements well, I consider it relatively expensive, especially for individual users like myself. However, as I become more accustomed to its benefits, it may become more affordable over time.

I would recommend OpenAI to anyone who can afford it. Its versatility makes it incredibly useful for technical problem-solving, content creation, data analytics, and more. It is a powerful tool for enhancing productivity across various domains.

It is at the forefront of the AI trend, providing powerful tools that leverage AI to streamline workflows and improve efficiency.

I rate it a seven out of ten. 

Implementing Azure OpenAI has notably streamlined our document creation process, increasing efficiency and productivity.

It aligns with our organization's compliance policies and data security requirements, assuring regulatory compliance.

It enhances our AI-driven projects by seamlessly integrating with tools like GitHub CoPilot, improving real-time coding capabilities, and facilitating development workflows.

In the next release, they could enhance the product's features for even greater usability and efficiency.

I have been working with Azure OpenAI for approximately one year.

I rate the platform's stability a seven. 

Currently, over 1000 users within our organization utilize Azure OpenAI.
I rate the platform's scalability an eight. 

There can be delays in receiving responses from the technical support team. 

Neutral

The initial setup has been relatively straightforward, although it may present challenges for beginners, particularly when deploying with infrastructure as code.

Depending on the backend infrastructure, the deployment typically takes just a few minutes, ranging from two to five minutes. Two executives are required to handle the operations. 

I rate the process around a seven.

I rate the product pricing six out of ten.

The product is integrated into our business workflows, particularly within our application development platforms.

The writing capabilities have been particularly crucial for generating descriptive content, such as case studies and product descriptions.

The document intelligence feature has significantly aided in our operations, facilitating the creation of descriptive content.

I recommend it to others, particularly those already utilizing Microsoft products or seeking a robust AI solution.

I rate the product a nine.

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.

I was freelancing for a company that wanted me to make tutorials on how the platform can be used. So, here are just a few model-building video tutorials I made from the platform. That's pretty much it.

It's very easy and convenient to use compared to others. It has good documentation, and it's very easy to follow. So somebody using it for the first time finds it very convenient.

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.

Maybe Azure OpenAI could provide a few video tutorials, in addition to the documentation. If they want to make it easier for somebody to do it for the very first time, providing video tutorials might be a good idea.

So, I would like to have a tutorial added for new users. 

I have only worked for around a month or so.

I would rate the stability a nine out of ten. It is very stable. 

I would rate the scalability a seven out of ten. 

I took up a course that gave me access to Amazon. But when I compare OpenAI with Google and Amazon because I work with both Google and Amazon, I would put OpenAI, then Google, then Amazon.

So, Azure OpenAI is on top of my list. They've got a very user-friendly platform, so that works best. Amazon is slightly complex. Google provides video tutorials, but somehow Azure has a better UI.

I would rate my experience with the initial setup a seven out of ten, where one is difficult, and ten is easy. 

Deployment was slightly complex for me to understand. So, my senior was working on it, but I did not directly deploy it. The instructions are very clear on how to deploy it, so it is fine, and it doesn't take a lot of time. It hardly takes a few minutes, I think, d depending on the data. If the dataset is very big and if the model is complex, then maybe deployment will take more time. But if it's something very simple and basic, deployment was fine.

I would suggest you should give it a try. Overall, I would rate the solution an eight out of ten. 

The main use case for Azure OpenAI is invoice processing. The first step is to recognize the text from images through Azure Cognitive Services, and then utilize Azure OpenAI to extract relevant information from the text. It provides more accurate information extraction compared to Azure Recognizer. This automation helps streamline the accounting process.

The high precision of information extraction is the most valuable feature. It enables the accurate extraction of information from various types of documents, including contracts, invoices, CDs, and fiscal documents.

Azure OpenAI needs to be updated quickly to keep up with rapidly changing technologies. There are no available updates of information that are currently provided. It is important to integrate newer technologies and ensure accurate information is available for seamless operation.

It is a recently launched platform, so I have used it for a couple of months now.

I didn't face any issues regarding the stability. I would rate it nine out of ten.

I would rate the scalability ten out of ten.

Microsoft offers very good support services. If there is any issue, regarding the operation or during deployment, you can reach out to their global IT department for assistance. I would rate it nine out of ten.

Positive

The initial setup was easy. I would rate it eight out of ten. It is simple to use. There are certain security concerns that may arise with multinational companies, that require approval from IT department to use it. Overall, it is not a difficult process.

We received notification that our team works on the deployment of the solution on the preferred cloud.

The cost structure depends on the volume of data processed and the computational resources required. There might be additional costs for private cloud usage and security considerations.

It is a useful solution that offers a variety of purposes. Developers can benefit from improvements in the system that would align with current technologies. Different departments, such as marketing, accounting, and finance can already use Azure OpenAI as a helpful assistant. I would rate it nine out of ten.