Best Practices
Production readiness guidelines for monitoring, content safety, disaster recovery, cost management, and governance of Azure OpenAI deployments.
Monitoring and Observability
| Metric | Source | Alert Threshold |
|---|---|---|
| Token usage | Azure Monitor / APIM | Budget threshold |
| 429 rate limit errors | Azure Diagnostics | > 5% of requests |
| Latency (P95) | Application Insights | > SLA target |
| Content filter triggers | Azure OpenAI diagnostics | Any occurrence |
| PTU utilization | Azure Monitor | > 90% sustained |
Content Safety
- Content filtering: Configure severity thresholds for hate, violence, sexual, and self-harm categories
- Prompt shields: Enable jailbreak detection and indirect attack protection
- Groundedness detection: Validate that model responses are grounded in provided context for RAG applications
- Custom blocklists: Define organization-specific blocked terms and patterns
- Logging: Log filtered content for review and continuous improvement of safety policies
Disaster Recovery
Multi-Region
Deploy in at least two regions. Use APIM or Front Door to route traffic with automatic failover on errors.
Configuration Backup
Store all deployment configurations as IaC (Bicep/Terraform). Version control all APIM policies.
Graceful Degradation
Implement fallback to smaller models (GPT-4o mini) when primary deployments are unavailable.
Testing
Regularly test failover procedures. Simulate region outages to validate recovery time objectives.
Governance Checklist
- Access control: Use RBAC to restrict who can create deployments and modify configurations
- Cost allocation: Tag resources and use APIM subscriptions for per-team/per-app cost tracking
- Data classification: Define which data sensitivity levels are permitted for OpenAI processing
- Model lifecycle: Establish processes for evaluating, testing, and deploying new model versions
- Responsible AI: Document use cases, conduct impact assessments, and maintain human oversight
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