Azure OpenAI Reviews

We've created a platform to build business use cases that we sell to our customers. We have partnerships with Microsoft, SAP, Databricks, and Azure OpenAI. 

We have two main focuses with Azure OpenAI:

• Enterprise Solutions: We've built solutions that act as an "enterprise copilot," digitizing and transforming processes within departments like HR, legal, finance, recruitment, IT support, and contact centers.
• Industry-Specific Solutions: We've developed solutions tailored to specific industry problems. Examples include automating airline ticket refunds, providing industry-specific customer support, helping customers plan their travel journeys, and assisting with hotel bookings.

Additionally, we integrate Azure OpenAI with data, analytics, and other areas to provide customers with 360 insights and transform customer journeys across various enterprise processes. We leverage the captured data to drive these initiatives.

OpenAI integrates seamlessly with the broader Microsoft Azure ecosystem, and that provides synergies with the other solutions. This integration makes it much easier to build solutions. 

Additionally, we can create custom solutions using Microsoft Azure development tools as needed. Since we're a software engineering organization, we can leverage the OpenAI APIs to go beyond the standard capabilities.

The modular design is very clean. The modules are very well-trained. It significantly reduces computational time compared to previous technologies. Plus, OpenAI provides a lot of training materials that allow us to quickly deploy solutions. 

There are development studios and configurations where people ensure that security is taken care of. So, Azure configurations ensure security and compliance – those guardrails are in place. All these features work seamlessly when you use Azure and OpenAI together. The security features are provided by Azure.

It also integrates with our existing enterprise tools like the broader Microsoft software suite including Office 365.

The product features themselves are fine. However, with Microsoft scaling the service so much, the support structure needs to keep pace. 

When solving complex issues, the process of interacting with Microsoft can be quite time-consuming, especially if you don't have a preferred agreement. For enterprise-level solutions that are mission-critical, real-time support is vital.

So, real-time support from Microsoft is an area for improvement, especially for complex business use cases. Right now, the support is suitable for use cases that won't directly impact the enterprise in a major way if there's a temporary issue.

Similar to what Google is doing with its marketing module, having seamless interaction with multimodal data – like videos, text, and other sources – at an optimal cost would be great. So, seamless interaction across multiple data types at optimal cost.

Multimodal interactions with optimal pricing. As use cases become more complex, and with the rise of edge devices, this becomes increasingly important.

I have been using it for 14 months now. We started using it three to four months after its initial release. 

I would rate the stability a nine out of ten. 

For smaller organizations with individual or limited use cases, the transaction costs can be a bit higher and costly. However, when we have larger-scale deployments across multiple use cases, the ability to scale is quite good. That's when the ROI (return on investment) really comes into play for an organization.

I would rate the scalability a nine out of ten. We successfully deploy it for multi-billion dollar enterprises. However, with the right configuration, it can be commercially viable for smaller businesses as well. We need to tune it properly for that use case.

Everyone has rushed into this technology. Therefore, the rapid adoption of this new technology means there are more people working with it than there are experts to provide support.

Neutral

It's almost too easy to set up. Because it's so easy, many people are jumping in and creating half-baked solutions without fully understanding the risks, security concerns, and potential problems. What's happening is that people without deep experience in operating large technology solutions are offering these solutions to businesses.

This can cause issues. For example, a recent case involved an airline in North America facing legal issues due to incorrect information provided by a chatbot. Now, there are questions of liability – is the chatbot responsible, or the business system itself? The business ended up paying a penalty.

So, responsible AI is key. The ease of setup is good from a technical standpoint, but we need governance, compliance, and checks and balances. Platform providers aren't emphasizing these aspects, and currently, there aren't regulations mandating it. So, while easy configuration is beneficial, we also need easy ways to ensure compliance, governance, security, and safeguards. All of these need to be considered – not just the ability to build something.

Since Azure itself is a cloud platform, we haven't come across many use cases where someone creates a purely private instance.

We can see results within weeks after implementation. The implementation itself might take six to ten weeks. Once live, we can see measurable differences within three to four weeks. Of course, with any new technology solution, there's a stabilization period, and the full impact is best understood over time. 

However, we can see immediate productivity benefits. Then, within six months or so, we might start seeing cost benefits. Within a year, we can potentially see improvements in feedback and metrics like NPS (Net Promoter Score).

Since everyone is on the Azure platform, Azure OpenAI's interactions with the technology generate quite a lot of data. This data can be used to quickly establish relevant digital metrics.

We've been a long-term Microsoft shop with an enterprise agreement, so that gives us some advantages. As an Azure-certified partner, we receive preferred pricing. However, AWS also has a very competitive solution. 

Ultimately, the best choice depends on your relationship with Microsoft.

Azure OpenAI doesn't use a traditional licensing model. Instead, it's interaction-based, meaning transactional. The cost depends on the complexity of the business use case and the amount of computing used within OpenAI.

It's important to engineer your solution carefully and implement controls. With any AI solution, there's a risk of operational expenses spiraling if the team doesn't put guardrails in place. 

Tools like Azure Synapse can help ensure usage stays within defined limits. This is true for any cloud technology – you need financial controls to prevent unexpected costs.

I would rate the pricing a five out of ten. It's reasonably priced for now. It will likely become more affordable over time. As more providers offer support services within the Microsoft ecosystem and as user feedback shapes the technology, we'll see improvements. Because the technology is only about 20 months old, there's a lot of potential for growth.

Definitely experiment with it. They're leaders in this space. For simple initial use cases, anyone with a basic understanding of IT operations and service management can get started. 

However, when scaling to an enterprise level with multiple use cases, it's essential to bring in experts. You need to consider security, financial risks, and potential reputational risks.

Remember, it's a powerful tool, and like any tool, how you use it matters. Ensure those using the tool fully understand how to do so responsibly.

Considering the current market and its competition, I'd rate it a nine out of ten. Considering them purely as a service provider in this line of business. It's still an evolving technology, so there's room to reach the full ten.  

We use it to streamline and reduce the time spent on fundamental tasks within our organization.

There are ongoing issues with Azure in Japan. Enhancing customer understanding of AI and how it operates, and laying the foundation for effective collaboration would be beneficial. Also, optimizing data utilization, as the current practice of relying extensively on external data can pose challenges for efficient business management. I would like to emphasize the importance of each significant customer having a tailored AI model to ensure a more nuanced and effective approach to addressing their unique needs. There is room for improvement in their support services.

I have been working with it for approximately seven months.

The system's instability is attributed to the absence of certain optimizations and despite attempts to address core issues, the system remains unsteady. I would rate it six out of ten.

Only a small percentage of employees currently use AI, making it challenging to discuss scalability at this point.

Tech support tends to be slow, varying based on the specific team. While some individuals excel in providing assistance, others may lag. I would rate it three out of ten.

Negative

We previously used AWS, which is widely adopted in Japan.

A team of twenty engineers is responsible for deployment, and maintenance, along with partners.

The cost is quite high and fixed. I would rate it two out of ten.

Overall, I do not recommend it. I would rate it five out of ten.

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.

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 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.

We use the solution for Document Intelligence, ChatGPT, and NLP.

Azure OpenAI is very easy to use instead of AWS services. And people are more aware of Azure OpenAI. ChatGPT was acquired by Microsoft, and people want to use the ChatGPT model with Azure OpenAI.

Azure OpenAI is not available in all regions, and its technical support should be improved.

I have been using Azure OpenAI for one year.

Azure OpenAI is a stable solution.

I don't like Microsoft's technical support. Microsoft's support puts our Severity A ticket in the queue, and we have to wait eight hours for its resolution, which is quite annoying.

Negative

You need to send a request to Microsoft. Not all companies have a Microsoft enterprise subscription. You need to enable the Azure OpenAI services specifically for your subscription. You can deploy Azure OpenAI services only after it is approved. Azure OpenAI has limitations as it is not available in all regions. There are specific regions where you can deploy Azure OpenAI, which is a big limitation. It's difficult for us to deploy Azure OpenAI in regions without firewalls.

The solution's pricing depends on the services you will deploy. The solution's ChatGPT service would have a different price depending on the number of tokens or requests. If you go for machine learning, it comes at a different price. Azure OpenAI doesn't have a fixed price.

The pricing depends on the services you deploy, the amount of data you push, and the endpoint output. For example, if you increase the memory of a virtual machine, its cost will increase. Azure OpenAI will show you the cost based on the services you use.

You can definitely use Azure OpenAI, but you need to study the solution and the use cases you need the solution to meet. In Azure OpenAI, you have many services like ChatGPT and Document Intelligence. If you know what kind of services you need from Azure OpenAI, things will be easier. Otherwise, you need to study a little bit.

The solution's language model customization improved the applications of the end users. Azure OpenAI integrated with our existing cloud infrastructure.

Overall, I rate the solution 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.

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.