Multi-Region AI Failover

For production AI requiring regional redundancy without active-active complexity, the active-passive failover pattern is right. Primary region serves all traffic; standby region warm but idle; failover via DNS / load balancer when primary degrades. Cost: ~1.5-2× single-region; ops complexity manageable.

Pattern

• Primary region: full stack serving production traffic
• Standby region: full stack running but receiving 0% production traffic
• Data replication: vector store, configs, model weights (model weights are static; replicate once)
• Health checks on primary; fail to standby on degradation
• DNS / load balancer reconfig → standby becomes primary

Data sync

• Vector store: Qdrant snapshot replication every 1 hour to standby region
• Model weights: pull once to standby; configuration in version control
• Configs / secrets: managed via Vault / cloud secret manager with cross-region replication
• Structured logs: streamed to off-site retention (multi-region by default)
• Eval datasets: in DVC / version control; fetched to standby on demand

Failover

Failover sequence:

1. Primary health check fails for 2-3 consecutive minutes
2. Automated alert; on-call confirms failure isn't transient
3. DNS update or LB reconfig points traffic at standby
4. Standby vLLM (already warm) serves new requests
5. RTO target: < 5 minutes
6. Vector store: standby is 0-60 minutes behind primary; document RPO impact
7. Failback to primary after issue resolved; replicate any standby-only data

Verdict

For production AI requiring regional resilience, active-passive failover is the cost-effective middle ground. ~1.5-2× single-region cost; RTO < 5 minutes; RPO < 1 hour. Test quarterly with planned drills — untested failover is theatre. For latency-sensitive global users, step up to active-active.