Azure OpenAI Reviews

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.

I use the solution to summarize very long documents, question and answer, build a chatbot, and extract keywords. I build many applications on top of Azure OpenAI.

The models are very intelligent. You just have to write accurate prompts according to your requirements, and the solution gives very good results. You don't need any training data, you don't need to set up your environment completely, or you don't need computational resources. You just pass the prompt with your requirements and get a response.

We had some bad experiences with the solution. We have to send our data to the Azure OpenAI cloud, which they use for training. They say they currently don't use our data for training, but you still have to compromise some secrecy.

The solution's response is a bit slow sometimes. When I use GPT-4, it takes around three to five seconds to generate 100 tokens or a small answer. Many other services perform very well compared to GPT-4. Right now, the issue with GPT-4 is slow response or latency time.

I have been using Azure OpenAI for two years. 

The solution is mostly stable, but there are also some downtimes. It was down a month ago when we were releasing our product. So, we had to wait for the OpenAI servers to work before we could deploy or launch our product. The solution experiences downtime on rare occasions, but it is almost always very stable.

Around 15 to 20 people use OpenAI for production purposes or our applications. Almost everyone uses ChatGPT for daily tasks or generic purposes like coding, text generation, and getting any idea about new products. Around 15 to 20 people use the restricted models, which can be accessed through API.

The solution's initial setup is very easy. You just have to pass the prompt and then hit its API. Integrating the Azure OpenAI models into your application is very easy.

Azure OpenAI is a bit more expensive than other services. Many cloud services and Anthropic AI are cheaper than OpenAI. Many open-source models and API services are also relatively cheap to Azure OpenAI.

We are using both servers. I use Azure OpenAI on our on-premises server. Since OpenAI is a cloud service, we cannot download the Azure OpenAI models on our server. So, we have to use their cloud through the API.

Currently, there is a lot of competition in the LLM area. The person who tries to start with LLMs must try different services, including Azure OpenAI. They should start with the cheapest model, which is GPT-3. They should stick to that model if it works for them and responds well to their requirements. They can also try other cheap API services that respond more accurately to their requirements. I would suggest trying different models or API services to start with.

When you start with the application, it is initially very easy to learn. You just have to write a prompt in simple English language to get the output from the solution. You can write anything; the prompt and your model will yield good results. You can start easily with the solution, but learning more advanced features of prompt engineering will take some time.

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

We used the model to produce the dashboards. They created them, but we weren't satisfied because they weren't interactive for decision-making and governance. We integrated Azure with SharePoint and AI to create an interactive model. That's what we did with Azure for our specific project.

We use Azure, but what we put in place is not just Azure. We created interactive dashboards. These allow people to instantly understand the situation when they see a red code, for instance. This enables governance to make a strong diagnosis of the situation and resolve it. 

It also helps integrate all the digital elements that affect decision-making in project resolution. This allows for evaluation and restructuring of project scope with an agile approach, and to put in place solutions to integrate stabilizing elements. 

The project I completed for this specific issue last year was a big success and is now being used by the entire department.

I'm an IT integrator. When I use Azure, if the model meets the need, I use what the system offers.

Our approach is stronger due to the algorithm we use. The system manages the equilibrium between different project environments. Many projects are executed in a stable environment, but when you create reorganizations, you destabilize the environment. Agile methodologies are necessary in such cases, but managing projects with MS Project in the field involves a stable environment. 

When these two environments interact, it creates resistance and digitalizing elements that hinder project realization. My mandate was to eliminate these obstacles and integrate the stabilizing and unstable environments. This ensures the establishment of a stable environment for the projects being realized. We created a significant part of the project to achieve this and replanned to ensure we could restabilize the project environment. We developed new management techniques and integrated them with various AI models.

For my needs, when working with interactive dashboards, it's expensive. I would prefer a system that provides alternative dashboard options or allows me to go directly into the program and pinpoint problems for decision-makers.

I'm an IT employee implementing solutions for clients who have specific requirements. As a Guidewire PolicyCenter status at Guidewire, I can manage teams with up to 200 professionals. 

This allows me to integrate many specialists and multi-disciplinary specialists into my teams and create strong solutions for clients.

It's good for a regular user, but for someone like me who creates and implements solutions, it is okay. 

The technical team is very helpful and easy to work with.

Positive

Throughout my career, I've been an expert in IT integration with a Guidewire approach. I work with strategies and implement them using Guidewire techniques and IT solutions. I've integrated solutions both vertically and horizontally in projects for clients. I've also created interactive dashboards with AI, using it as an expert system. This allows for a fully integrated solution within multi-project environments with complex issues. I began this in 1988.

The first project I worked on after my master's was presented by the federal government, Environment Canada, for the St. Lawrence River and the Great Lakes. They had a big budget and asked me to conceptualize all the programs that integrated many departments.

They asked me to reorganize and restructure the project to manage quality and ensure continuity after projects were completed. They wanted to ensure technology and budget were used effectively.

We have recently put in place SharePoint. We restructured the system with Microsoft 365 and integrated it with budget and document management. We organized all the applications. 

Before I arrived, many directors had uncontrolled access to the budget. We implemented a governance system similar to the Business Development Bank to ensure budget control. We integrated everything into Microsoft 365, including MS Project and SharePoint. We used insights to facilitate Kubernetes assessment and the assessment of projects in the field.

If I compare it with other IT I've worked with, I like to work with Appian. I think it's very strong, and for me, it's a benchmark to compare others. I also like to work with MuleSoft. It's another approach, but very interesting for me. When I compare with Microsoft 365, it's good but doesn't necessarily allow me to resolve all the issues I have. With Appian, we can find the solution we need; any kind of requirement we have, we're able to find an approach or solution within the system.

I have used Azure. I have forty years of experience in reorganization and business transformation. When IT can't directly meet my needs, I ask my technicians and analysts to examine the specific case for the project. In this instance, we used Azure to create interactive dashboards. They reprogrammed and worked with SharePoint to integrate Azure into the AI, the internal artificial intelligence.

The integration and the solution modeling can be complex. 

The pricing really depends on the specific requirements and underlying needs. For example, if the goal is to implement innovative solutions for the future or to improve productivity in decision-making and governance, then the cost might be justified. 

In a recent project, I achieved strong results using only 60% of the allocated budget. The client was impressed. They were curious about my approach, but I assured them it was simply my way of working.

I'm an IT integrator. When I use Azure, if the model meets the need, I use what the system offers. I use any kind of IT that I can, depending on the needs and the strategy we want to implement. IT can offer some services, but they have a suite of services that they don't offer, and we have to create and integrate with the IT. 

I work with my team to upgrade the IT we use. We integrate it with, for example, artificial intelligence like OpenAI to resolve or address the specific issue I want to solve. That's my way of working. IT can't stop me from putting a solution in place. I prefer to add to it or create a completely operational solution that can satisfy the client's exact needs.

For example, the problems in a specific project were major. When I finished, I had implemented a solution that answered the project's/client's exact needs. We reorganized the entire project structure, which allowed the company to use the IT we adapted. We put in place specific applications for governance and project management in the field. 

As a program manager, I communicate the needs and the desired results and evaluate what technology can offer based on the requirements. People offer me solutions. If it's on Azure, that's okay. If it's on Microsoft 365, that's fine too. 

I have techs who work for me and present solutions that I assess with them, considering the complexity of integrating all the necessary applications. If the solution satisfies my requirements, I authorize it, and we structure the project. We integrate all the issues and stabilizing elements into the project scope and manage it like any other project.

Azure OpenAI, for me, it's a component I use in my solution to ensure the application I want is realized. That's my approach. I'm a program manager, a person who manages IT architecture, project management, and change management. The requirements of the clients are my guide. Based on that, I will organize the solution.

I've always modified Azure to create interactive solutions. But it depends on the kind of application you want. I can recommend it for standard documentation, but not for developing innovative solutions. My requirements are more advanced.

Overall, I would rate it a seven out of ten. 

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. 

One of the tasks for which I found the use of Azure OpenAI to be useful for my business is related to the area of annotations in images.

Azure OpenAI is not an optimized tool yet, making it one of its shortcomings where improvements are required.

I would like Azure Open AI to provide more integrations with other platforms.

The cost of the product should be lowered.

I have been using Azure OpenAI for six to seven months.

It is a stable solution.

The scalability part of the product depends on whether you have declared the product on an on-premises model and what kind of configurations you are keeping with your back-end servers. I cannot talk about the product's scalability since the tool has more areas like outcomes, precision, and accuracy.

Conversational AI is used across hospitals. The hospital runs Azure OpenAI for EMRs. Businesses have started using AI components for various applications.

The technical support part is documented, and my business works together with Azure OpenAI.

The technical support required by our business depends on the algorithms and the models being developed, which is not what Azure OpenAI provides. It basically lies with the user to solve a problem.

My company works not only with Azure OpenAI but with foundation models, too.

The product's initial setup phase was pretty easy. Installation is not an issue in the tool, but achieving the outcomes matters to our company, which is dependent on algorithms, models, and how much data you use to train your models.

The solution is deployed majorly on the cloud and then on an on-premises model.

The steps that can be deployed in Azure OpenAI include areas like integration with your applications.

Accessibility from your applications and browser is required to deploy the product.

My company has a team of several solution providers who work together. My company has partnered with some of the startups in our ecosystems, so they work with us.

There are around 30 to 40 percent cost-saving outcomes in our company from the use of the solution.

According to the negotiations taking place and the contract, there is a need to make either monthly or yearly payments to use the solution.

With Azure OpenAI, there are a number of alignments that my business is into.

My company works with Azure OpenAI and our own private LLMs.

Though Azure OpenAI is not optimized, it is one of the best when it comes to text generation.

Azure OpenAI is regarded as a foundation model on which our company plans to use our private LLMs.

The natural language understanding capability of Azure OpenAI has improved our company's data analysis since we use the product's integration capabilities for areas like translations and conversational AI.

I recommend the solution to those who plan to use it, but there are also other products that are available on the market.

I rate the overall tool a nine 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 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.