We have been running Semantic Kernel in a production .NET SaaS product in the healthcare data space for six months. The integration replaced a custom prompt orchestration layer that the team had built directly against the Azure OpenAI SDK. It was not a greenfield project. It was a system with real users, real data, and real production constraints from day one.
At Blackthorn Vision, a Microsoft-partnered .NET and AI development company helping enterprise teams build and modernize complex software products, we have now run several Azure OpenAI and Semantic Kernel integrations using our AI and machine learning development practice and .NET development services past the demo stage and into sustained production. This post covers what we found after six months that we did not fully anticipate going in: the things that staging missed, the things we had to rebuild, and the things that worked better than we expected.
Why We Chose Semantic Kernel Over the Raw SDK
The honest answer is that we did not start with Semantic Kernel. The initial integration used direct Azure OpenAI SDK calls wired into ASP.NET Core controllers. It worked for the first three months of limited rollout and covered the basic prompt-response pattern the feature needed.
