I like how Azure OpenAI Service integrates very smoothly with other Azure services, making it easier for me to build end-to-end AI solutions. It also helps with data security because our data stays within our private Azure environment, which aligns with our strict enterprise rules and requirements. Setting it up was easy with clear instructions available. Review collected by and hosted on G2.com.

The one thing that I want to improve for Azure OpenAI Service is the fine-tuning options are restricted compared to other open source models, and there is limited control over training data. The current limitation is that fine-tuning is only available for select models and it does not accept all the latest GPT models. I would like to see fine-tuning for all major models and have the ability to control it. Review collected by and hosted on G2.com.

The enterprise-grade security is the strongest aspect. Unlike other AI platforms, our data isn’t used to train public models, which is critical for meeting our compliance requirements. On top of that, the seamless integration with the existing Azure ecosystem and Microsoft 365 makes it much easier to scale across the company. Review collected by and hosted on G2.com.

The setup process is significantly more complex than the standard OpenAI API. It requires a solid understanding of Azure networking and security protocols. Additionally, the pricing can be a bit opaque and difficult to forecast for large-scale projects, and getting access to specific models can sometimes involve an approval waitlist. Review collected by and hosted on G2.com.

The seamless integration with existing Azure resources is a huge plus. It feels natural to plug advanced AI models into my workflows without a complex setup. The service is highly scalable and secure, and it’s backed by Microsoft’s enterprise-grade infrastructure, which gives me confidence when deploying solutions in production.

I also appreciate the flexibility: whether it’s text generation, summarization, or embedding models, the breadth of features covers most of my use cases. The documentation and SDKs make implementation straightforward, and being able to manage everything through the Azure Portal helps keep things organized and easy to maintain. Review collected by and hosted on G2.com.

Pricing can be a bit tricky to estimate, especially when usage spikes unexpectedly. The documentation is solid overall, but I’d like to see more in-depth advanced examples that help developers better optimize both performance and costs. Review collected by and hosted on G2.com.

What I appreciate most about Azure OpenAI Service is its enterprise‑grade reliability, security, and seamless integration with the broader Azure ecosystem. It lets me deploy advanced AI capabilities with confidence, because I know data governance, compliance, and scalability are handled to a world‑class standard. The mix of powerful models, flexible deployment options, and strong operational stability makes it a great fit for building real, production‑ready AI solutions. Review collected by and hosted on G2.com.

Overall, I value Azure OpenAI Service, but there are a few areas where it could be improved. The pricing structure can feel complicated, especially when trying to balance model performance with predictable costs at scale. Some of the newest model releases also seem to arrive later than on other platforms, which can slow down experimentation and iteration. In addition, certain configuration and deployment options feel more restrictive than I expected, which can reduce flexibility for more advanced use cases. Review collected by and hosted on G2.com.

I like Azure OpenAI Service for its data privacy and resiliency, which are crucial for my work. Its integration capabilities and performance are great, especially with scalability, which helps manage our enterprise-wide data infrastructure effectively. I also appreciate the improvement in data quality it provides. The dedicated GPU infrastructure adds to the reliability and performance, which I find valuable. Review collected by and hosted on G2.com.

The cost can become expensive, especially with extensive use of newer advanced models. The complexity of setup can be challenging if you're unfamiliar with the broader Azure ecosystem. Model access might not always offer the absolute latest models at the moment compared to direct API access. Review collected by and hosted on G2.com.

Azure OpenAI service is good for me because it gives me plenty of options to integrate with services already running in Azure. I use the Azure service in my current project, which is deployed on an Azure VM, so it’s easy to use and fits perfectly with my setup. Review collected by and hosted on G2.com.

Mostly, they have some limitations in model availability and in certain regions due to zone restrictions. Along with that, job tracking could be better, and the model also blocks some valid prompts because of the safety measures. Review collected by and hosted on G2.com.

It’s an effective, straightforward way to make requests to OpenAI offerings. Developers and employees can easily send requests and use ChatGpt in their projects. Setting up the API is simple as well, which makes it quick to get started. For example one of the projects we used it for was to create a tool that would parse documents and extract key information. The tool would access OpenAI through Azure API. Review collected by and hosted on G2.com.

The only thing I dislike is it is difficult to access and retrieve specific historical requests / chats in an organized manner. Review collected by and hosted on G2.com.

Below are some points I like about Azure .

Built on Azure’s trusted infrastructure, with strict compliance certifications (ISO, SOC, HIPAA, GDPR).

You can scale from small experiments to production workloads without worrying about infrastructure.

Smoothly Integrate with Azure Ecosystem . Review collected by and hosted on G2.com.

Below some points I dont like about it .

Running Large model can get expensive quickly .

Not every Azure region support openAI . Review collected by and hosted on G2.com.

Azure OpenAI Service provides enterprise-grade access to powerful AI models, enabling secure, scalable, and customizable AI solutions for content generation, analysis, coding, and conversational applications. It supports a range of use cases while helping organizations build and deploy AI capabilities in a way that aligns with enterprise needs. Review collected by and hosted on G2.com.

The UI is a little complex for beginners. Apart from that, I haven’t found anything to dislike so far. Review collected by and hosted on G2.com.

I like how easy it is to deploy and manage models with Azure OpenAI. The role-based access controls, resource management, and monitoring tools make it much simpler to operationalize AI, especially compared to using standalone APIs. Review collected by and hosted on G2.com.

Quota limits and pricing can be difficult to predict, especially as usage scales up. It takes careful monitoring to avoid unexpected throttling or sudden cost spikes, and to keep usage within the expected limits. Review collected by and hosted on G2.com.