Azure OpenAI + Semantic Kernel in a .NET SaaS: What Breaks in Production and How to Fix It
Integrating Azure OpenAI and Semantic Kernel into a .NET SaaS product can lead to unexpected challenges in production. Common issues include latency spikes, higher-than-expected token costs, and rate limit errors. Solutions involve optimizing response handling and reviewing timeout settings across the application stack.
- ▪Latency spikes occur when a .NET SaaS application built on synchronous request handling is used for LLM calls.
- ▪Production deployments often incur higher costs than estimated due to the pricing structure of input and output tokens.
- ▪Common failures in production include 429 rate limit errors and observability gaps that complicate troubleshooting.
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