Table of Contents
Multi-region AI is rarely needed. When it is, the architecture is non-trivial.
TL;DR
Go multi-region when: regulatory data residency requires it, users in >3 distant regions, or 99.95%+ SLA. Pattern: dedicated GPU per region + geo-load-balancer + per-region vector store.
When to go multi-region
- Regulatory: UK customers want UK-resident data, EU customers want EU-resident
- Latency: users in US + EU + Asia, need <200 ms RTT for each
- SLA: single-server can't reach 99.95%
Architecture pattern
- Dedicated GPU server per region (e.g., UK + EU + US)
- Per-region vector store (data stays local)
- Geo-DNS or anycast load balancer
- Cross-region failover via LiteLLM router
- Async replication of LoRA adapters across regions
Verdict
Multi-region is operational complexity for a real benefit. Most teams don't need it. When you do, plan carefully.
Bottom line
Single-region with fallback covers 95% of teams. See GDPR-compliant AI.
