What I like most about Azure OpenAI Service is how easy it is to apply advanced AI capabilities to real business scenarios. The platform feels reliable and secure, and it integrates smoothly with other Azure services. It also saves time by automating tasks such as content generation, summarization, and data analysis. Another big plus for me is that it’s well suited for enterprise use, thanks to its strong security and compliance features. Review collected by and hosted on G2.com.

One thing I dislike about Azure OpenAI Service is that the initial setup and configuration can feel confusing for new users, particularly when it comes to managing Azure resources and permissions. The pricing model can also be hard to interpret for large-scale usage, and some advanced features require solid technical knowledge to implement correctly. Better documentation and a more streamlined onboarding experience would make the service much easier to adopt and even better overall. Review collected by and hosted on G2.com.

Through the Azure OpenAI service, Microsoft enterprise customers now access the OpenAI LLM models with stronger Data Privacy and appropriate security. It is fully compliant with major information security compliance frameworks like SOX,GDPR,HIPAA. Azure Open AI also can be integrated easily with major Microsoft products such as Microsoft Dynamics, O365, and Power BI. Review collected by and hosted on G2.com.

Microsoft Azure Billing for Open AI is bit complex and costly for medium size customers. And the Deployment team needs a formal Technical learning to fully utilize the feature. Review collected by and hosted on G2.com.

I'm a Data Analyst at a medium-sized online store, and I mostly handle customer feedback from support tickets, surveys, and what people say about our stuff. Before Azure OpenAI, doing this was a real pain. I had to open tons of tickets in Zendesk, copy and paste everything into Excel, and try to sort things out myself. It would take forever, and I'd often miss stuff.

What I really like about Azure OpenAI is that it sorts all this messy text into something I can use automatically. Every Monday, I grab the last week's support tickets (usually like 500–600 chats) and run them through a gpt-4o-mini thing that our IT folks set up for me. I send each ticket to the system with a simple instruction: Put this ticket into one of these groups: Billing, Shipping, Product Quality, Returns, or Other. Also, figure out if the customer is happy, okay, or mad, and write a quick summary of the problem.

The result is a nice, clean file that I can just drop into Power BI. What used to take me 6–8 hours every Monday now takes about 20 minutes. I didn't even need to learn fancy coding — our team made this easy thing that connects Zendesk to Azure OpenAI and puts the results in a SharePoint list. I just open the list, hit refresh on my Power BI thing, and my report is ready for the meeting at 10 AM. It actually works with other Azure stuff; I don't have to be a tech expert to get something out of it. Review collected by and hosted on G2.com.

I'm a business user, not a techie, so my issues are all about using this stuff every day.

First off, the rate limiting is a pain in the neck. We have 120,000 TPM (tokens per minute) for our gpt-4o-mini, which sounds like plenty. But when you're dealing with 500 tickets, and each has 800–1,500 words, you hit that limit fast. Then you get those 429 errors (Rate limit exceeded). When that happens, my Power Automate thing just stops working. I've got to wait a minute and start it up again myself. IT said I could do some kind of batching thing, but that's way too complicated for me. I just want it to work without getting into all that technical stuff.

Second, the outputs are all over the place. Even with the temperature set to zero, the same ticket might be called Shipping one week and Delivery the next, even though I said Shipping is the right one. It makes it hard to track trends over time. Someone on Gartner Peer Insights nailed it, saying it has inconsistent behavior even with specific instructions and low temperature. Couldn't agree more.

Third, it's hard to know how much this all costs if you're not a tech person. I don't see the Azure cost stuff; IT just tells me we're spending too much. I have no idea how many tokens I'm using for my weekly summaries, or if I could save money by using shorter tickets. There's no easy way for regular users to see how much they're using. I feel like I'm using this really powerful thing without knowing how much it's costing me. Review collected by and hosted on G2.com.

It is easy to access different AI models in one place and track the metrices. and cost with simple interface and pay-as-you-go pricing which i love most and it is flexible, scalable, and convenient to use. Review collected by and hosted on G2.com.

The main thing I dislike is the cost. For a small startup like us, it’s hard to justify, and running certain tests ends up being really expensive. Review collected by and hosted on G2.com.

What I like most is how it brings together the best of both worlds: you get OpenAI’s cutting-edge AI models, along with Azure’s reliability, security, and scalability. It feels like having powerful AI right at your fingertips, backed by enterprise-grade peace of mind. Review collected by and hosted on G2.com.

If I had to pick something, I'd say that depending on Azure’s ecosystem might mean some complexity for folks who aren't already familiar with it. Also, for smaller, simpler projects, it might be more than you need—kind of like using a rocket when you just need a bicycle! Review collected by and hosted on G2.com.

Azure open AI service offers Powerful AI Capabilities with enterprise grade security and service.

It integrates seamlessly with the Microsoft azure ecosystem, making deployment and management efficient.

This platform is highly effective for automation, content generation, analytics and improving business productivity Review collected by and hosted on G2.com.

Pricing and usage costs may become expensive for large scale or high-volume workloads. some advanced features and model access may require additional approvals or have regional availability limitations. Review collected by and hosted on G2.com.

Single place with most of the AI models for openai and other providers. Integration is also quick with api or sdk Review collected by and hosted on G2.com.

The models are slow to respond when compared with independent providers like openrouter Review collected by and hosted on G2.com.

I like Azure OpenAI Service for its enterprise-grade security and the ability to keep data in my private Azure account. The seamless integration with Azure services, like AI search, is fantastic as it makes implementing RAG very straightforward. It allows us to ground AI responses in our own enterprise data using a low-code RAG pattern, ensuring that models provide accurate, context-aware answers using vector search combined with semantic ranking and built-in citation tracking. Review collected by and hosted on G2.com.

I don't like the regional availability of newer models and the rollout of features not being the same time globally. Also, the quota management system and its approval to increase quota is manual and can take several days. I wish Microsoft could add more granular cost control tools at model and project levels to prevent overcharges. Also, better debugging tools could be added. Review collected by and hosted on G2.com.

This has been most helpful for automating API documentation for our software modules and cutting down on manual overhead. I also appreciate that the AI tools analyze the code with security in mind. Review collected by and hosted on G2.com.

Sometimes during peak traffic, even when I haven’t reached the limit, it still shows “limit exceeded.” Review collected by and hosted on G2.com.

What I like most about Azure OpenAI Service is the ease of integration with other Azure services that we already use. The security and regulatory compliance is excellent for business data. Also, access to the latest OpenAI models without having to wait on waitlists. And the ability to adjust parameters like temperature and maximum tokens gives us a lot of control over the responses. The documentation and code examples are very comprehensive, which makes implementation easier. I also like that we can use our own training data securely within the Azure environment. Microsoft's technical support has been quite helpful when we've had technical questions. Review collected by and hosted on G2.com.

The cost can be high when processing large volumes of tokens, especially with the more advanced models. Sometimes there is latency in responses during peak hours. The initial setup and permissions in Azure can be a bit complex for new users. It would also be useful to have more options for native Spanish models. Review collected by and hosted on G2.com.