Azure OpenAI Reviews

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.

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.

Positive

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.

We use OpenAI for the insurance process to analyze documents and insurer-to-client requests in the public parts of our process. 

We plan to use it for confidential parts as well. This is the main solution we're aiming for.

We have a case from a company where we need to generate a complex report for a customer, comparing multiple documents. We plan to use OpenAI for this.

The most valuable features include analyzing comments and preparing requests for customers, making emails easier and faster.

Sometimes, it gives answers in English, even when the request is in Polish. That's the main reason it's not a perfect ten.

So, the language support could be better.

We started a few months ago. It was a good first choice but not the best.

I would rate the stability a nine out of ten. It is quite good. 

We haven't had any problems with scalability. We have around 40 end users.

We will increase the number of users.

We're in touch with customer service and support because we plan to implement Azure and Azure OpenAI. We also have a dedicated contact person at Microsoft, so we haven't had any issues getting support.

We currently use OpenAI, but we've decided to use Azure in the future.

My colleagues from the programming team handled the setup. I don't know the specifics, but they didn't have any issues using it.

We started with monthly payments, but we plan to switch to yearly billing once we've stabilized our solution.

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

I chose Azure OpenAI and would recommend it to others because it's easy to set up, and I plan to use the cloud, which eliminates concerns about equipment and other infrastructure.

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'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.