Putting Azure OpenAI in production is not just calling the API. It is designing a system that is secure, observable, cost-effective and maintainable. After 3 enterprise integrations, here is what we learned.
Real cost, not pricing cost
The price per token lies. What blows up the invoice is the system prompt. If you put 8K tokens of system prompt and the user makes 100,000 queries per month, you pay for tokens that add no value. Optimize prompts like you optimize SQL queries: caching, few-shot only when it helps, retrieval only when needed.
Latency that yes, latency that no
- GPT-4o on text: 300-800 ms p50, 2-4s p95 with streaming.
- Embeddings with text-embedding-3-large: 100-300 ms.
- Vision with GPT-4o: 1-3s. No useful streaming here.
- End-to-end RAG with 5 context chunks: +200-400 ms over the base model.
Security and Purview
Your prompts are data. If you paste contracts, NDAs or PII in the prompt, they get logged in Azure OpenAI. Configure customer-managed keys, disable logging for sensitive data if your compliance requires it, and do content filter review quarterly.
14+ years leading enterprise digital transformation projects in LATAM and Europe. Founder of TIKAL SOLUTIONS.
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