Azure OpenAI Service provides access to OpenAI models (GPT-4o, o1, embeddings, DALL-E) within a private Azure environment. Unlike the public OpenAI API, data stays within your Azure tenant, meeting GDPR and enterprise compliance requirements. Deployments map model versions to named endpoints (my-gpt4o) with configurable capacity in TPM (tokens per minute). Content filters and Responsible AI policies apply per-deployment. Private endpoints eliminate public internet exposure.
Azure OpenAI deployment lifecycle in Bicep.
Azure OpenAI API keys are long-lived credentials that can be leaked in code, logs, or CI. Managed identity grants token-based auth that rotates automatically. Use DefaultAzureCredential — one line of code, works locally (dev credentials) and in production (managed identity).
By default, AOAI endpoints are public. Private endpoints route AOAI calls through your VNet on a private IP. For regulated industries (healthcare, finance), this is mandatory for compliance.
Set up Azure Monitor alerts when token consumption exceeds 80% of TPM quota. A spike in usage can exhaust quota mid-day and cause 429 errors. Plan ahead with multiple deployments for load balancing.
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