Azure OpenAI Reviews

I'll provide feedback on additional features after the project is completed. I think it would be better to comment on that after the implementation is finished.

I have been testing this solution for the past month. 

The deployment is not finished yet. To assess performance and stability, the project needs completion. Currently, our professional service is actively involved, managing tasks related to services and users in the ongoing termination process.

Currently, I'm in constant communication with them. They are professional, helpful, and highly experienced.

They need to simplify the implementation process. I've observed that sometimes the professional service is focused on the database, especially around log shipping, and it can be challenging. I'm actively involved in the deployment process, but it's carried out by our professional service. Our plans are implemented through this service, acting as intermediaries between our clients and the professionals. The implementation typically takes around a month, but various issues, such as management, resource, and other challenges, may arise during the deployment.

One notable difficulty we face is the lack of exceptional resources for deploying the solution in our plans. Despite encountering challenges, our satisfaction with the professional service remains high. They are dedicated to implementing the solution effectively.

I would also rate it a ten overall. It's scalable and easy to deploy. However, I have some concerns. 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.

Regrettably, this product remains incomplete, and the interim phase is still pending. It is challenging to determine its effectiveness due to some significant license initial issues.

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.

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. 

We're implementing an assistant using Azure OpenAI. The challenge is grounding OpenAI responses to our specific data. 

We can only offer users basic querying, like for documents they're stuck on. It handles the request. It's primarily the question-answering feature.

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. 

It's focused on information retrieval and question-answering, which suits our needs perfectly. It is more like a natural language query tool we leverage.

We use Azure OpenAI alongside Azure Cognitive Search. These are both new services we've deployed. There's a process where we need to ask Microsoft to create private endpoints to link OpenAI to Azure as a connectivity service.

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. 

As a governance department, accuracy and control are crucial. We're trying to tune the system to stick with our content, but it's an ongoing challenge. 

We've been working on fine-tuning prompts and parameters for about four weeks now.

I've been using Azure OpenAI as a creative source for the past six months.

We've noticed some issues with scaling. It takes time for the service to adapt when we increase the load. We're still in the pre-production phase, and we're seeing this even during testing. 

Also, there's limited capacity in our region (Canada East), which makes it difficult to accommodate the expected load. We've submitted capacity increase requests, but we're not sure if they'll be approved.

The main challenge we've faced is around capacity. Even after running extensive load tests, we don't have sufficient capacity to handle our projected volume.

We have a consultant from Microsoft working with us. They've been very helpful.

However, they're very busy. We could use more of their time if they were available. But they're very competent and helpful. We just wish we could have more access to their expertise.

Positive

We have an alternative search engine that indexes our document base. We use Azure OpenAI's question-answering feature to query that index, generating answers from relevant documents.

We don't use GPT-4 specifically, nor are we training any models. Our IT group leverages Azure OpenAI for its existing capabilities.

It is our first implementation of this kind.

There are some limitations right now. For our specific use case, where we need a traditional information retrieval system, it's not an ideal fit. 

Azure OpenAI is a question-answering system built on top of information retrieval, and that distinction is important for us. Given our use case, I don't think it's well-suited.

Our management team requires accurate and complete results, with precision that matches our existing keyword search tools. It's difficult to evaluate and prove that Azure OpenAI consistently meets that standard. 

We're still early in our adoption, so the rating could change as we deploy it to a larger audience.

For now, I would rate the solution a five 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.

We use the solution for training. We did a POC. We use it for some hackathon projects we have been working on.

The solution has improved our development process. When we integrate with OpenAI, we get immediate responses for what kind of code or logic we must use.

GPT was useful for our projects.

The UI could be a little easier. The prompts must be updated.

The product is not stable. We get different results for the same prompts. The stability must be improved. However, it is common across any AI tool we use.

The product is pretty much scalable. It helped us scale some of our projects. The internal teams use the tool for projects.

The setup is easier compared to AWS. The tool can be deployed on the cloud.

We use the API calls to integrate OpenAI into our existing workflows. I use API calls. We used the tool for testing purposes for a small project. It was not given to the end users. People who want to use the solution must go through its capabilities. Though AI is available in the market, not everyone is exploring it. The tool is an advanced alternative to Google Search. It helps with development and coding by providing prompt responses. Overall, I rate the product a nine 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.

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.