What should I use if I need multi-region model serving?

If you need multi-region model serving, you generally want a global model serving layer that can route requests to the nearest healthy region and support replication across regions.

Common choices:

• Kubernetes + Ingress / Global Load Balancer

  • Deploy the model server in multiple regions
  • Put a global traffic manager in front
  • Good if you already run on Kubernetes

• Managed cloud AI serving

  • AWS SageMaker multi-region deployment with Route 53 / Global Accelerator
  • Google Vertex AI with global load balancing patterns
  • Azure ML with Front Door / Traffic Manager
  • Easier operationally, less control

• Specialized model serving platforms

  • BentoML, KServe, Ray Serve, Seldon
  • These can be deployed per region and fronted by a global load balancer
  • Good if you want portability and custom autoscaling

• CDN / edge inference for lightweight models

  • If the model is small and latency-critical, consider edge inference on platforms like Cloudflare Workers AI or similar
  • Best for low-latency, smaller models rather than large GPU-heavy ones

A practical architecture is:

1. Replicate the model to each region
2. Run inference in-region
3. Use global routing to send users to the nearest healthy region
4. Keep model versions synchronized with CI/CD or artifact replication
5. Use failover for regional outages

If you tell me:

• your cloud provider,
• model size / GPU needs,
• latency target,
• and whether you need active-active or active-passive,

I can recommend a specific setup.