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.

Positive

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.

I use it in my company for generative AI or GenAI, transcription services, chat services, and text summarization API services.

The chatbot available with the help of the tool seems to be the best feature for our company as we are into healthcare, and whatever work we want the tool to help us with when it comes to the healthcare section, we get prompt responses. Generative AI or GenAI seems to be the best part of the solution.

The developer access provided by the tool is a bit less, while the costing part doesn't seem to be clear, making both areas where improvements are needed. A user is not able to get a clear-cut idea of the cost side of the product, making it an area where some improvements are required.

There are certain shortcomings with the product's scalability and support team where improvements are required.

I have been using Azure OpenAI for about six months. My company is a customer of the product.

The product's stability is good. Stability-wise, I rate the solution a seven out of ten.

The product's scalability is low. Scalability-wise, I rate the solution a five out of ten.

In my company, we are mostly into research and development, and we use the tool for certain analyses that we have made public, so we have not tried to figure out the number of users who use the solution.

Earlier, my company had tried to contact the product's technical support team, but we did not get a proper response back then. The response from the technical support team was not quick.

I rate the technical support a three out of ten.

Negative

I don't have any experience with any solutions before Azure OpenAI.

The product's initial setup phase was not that difficult to handle as it was a manageable process. One does not need to have any experience to take care of the initial setup phase.

The solution's deployment didn't take much time for our company.

Regarding ROI, I would say that my company is still working on it.

Cost-wise, the product's price is a bit on the higher side.

I would tell those who plan to use the solution that Azure OpenAI's developer forum and support need improvement.

I rate the overall product a seven 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 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.

The typical use cases include building chatbots for financial document analysis, agents for transaction categorization, and call centre voice identification or conversation analytics.

Two aspects I appreciate are the turnaround time and ease of use. As it's a managed service, the quick turnaround is beneficial, and the simple interface makes it easy to work with. Performance and scalability are also strong points since you can scale as needed. 

We used Azure OpenAI to analyze call center voice data. This helped us better understand customer sentiments and make recommendations.

I have found the tool unreliable in certain use cases. I aim to enhance the system's latency, particularly in responding to calls. Occasionally, calls don't respond, so I want to improve reliability.

I have been working with the product for six months. 

I have issues with Azure OpenAI's stability and reliability. 

The tool's scalability is good. 

I have worked with Amazon AWS but found Azure OpenAPI to be simpler. 

Azure OpenAI's deployment is straightforward. The deployment process takes around half a day to a full day, considering the use case and the end-to-end deployment. It works for around four to eight hours. To deploy the product, typical steps include data analysis, setting up keys for OpenAI, making API calls with the relevant dataset, implementing basic guardrails, and analyzing the final output. These are the basic steps involved in the deployment process.

A project that would have taken three to six months to build was completed in just six weeks with the help of Azure OpenAPI. So, that's our ROI. The biggest value of the service is how quickly you can prototype your use cases. It offers unlimited scalability, and it is easy to find something closer to your country. Plus, it's highly scalable and comparatively cheaper than other solutions.

I rate the overall product an eight out of ten. If you're comfortable with your data being in the cloud and want quick results, Azure OpenAI is a great option. However, I haven't used it in a production environment yet, so I can't comment.

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.

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.