What should I use for global inference endpoints?

For global inference endpoints, the best choice is usually a globally distributed, latency-aware endpoint type that can route requests to the nearest healthy region and scale across regions automatically.

In practice, use:

• Azure AI Foundry / Azure OpenAI global deployments if you want built-in global routing for model inference.
• A global load balancer / traffic manager in front of regional inference endpoints if you’re running the service yourself.
• Any managed “global endpoint” offering from your cloud provider if the goal is low latency and high availability across regions.

If you mean Azure specifically, the recommended option is typically:

• Global endpoint / global standard deployment for inference
  • good for multi-region failover
  • lower latency for worldwide users
  • less operational work than managing region-by-region routing manually

If you mean which architecture to use, a common pattern is:

1. Deploy inference in multiple regions
2. Put a global router in front
3. Route based on:
   • latency
   • health
   • capacity
   • geographic proximity
4. Add fallback/failover to another region

If you want, I can give you a recommendation specifically for:

• Azure AI / Azure OpenAI
• AWS
• GCP
• or self-hosted model serving.