When your AI gateway handles all AI traffic, it becomes critical infrastructure. Here's how to design for enterprise-grade availability.
Availability Targets
| Target | Annual Downtime | Design Complexity |
|--------|----------------|-------------------|
| 99.9% | 8.76 hours | Basic |
| 99.95% | 4.38 hours | Moderate |
| 99.99% | 52.6 minutes | High |
| 99.999% | 5.26 minutes | Very High |
Most enterprises should target 99.95-99.99% for AI gateways.
Architecture Patterns
Active-Active Multi-Region
Deploy gateway instances across multiple regions with global load balancing. Highest availability but most complex.
Active-Passive with Automatic Failover
Primary region handles traffic; secondary region takes over on failure. Simpler but with brief failover period.
Kubernetes-Native HA
Deploy on Kubernetes with multiple replicas, pod disruption budgets, and horizontal pod autoscaling.
Key Components
- Health checks (every 10 seconds minimum)
- Automatic failover (< 30 seconds detection and switch)
- Connection draining (graceful shutdown)
- Circuit breakers (prevent cascade failures)
- Rate limiting (protect against overload)
Testing HA
- Regular failover exercises
- Chaos engineering (random failure injection)
- Load testing to failure point
- DR drills with documented recovery procedures
High availability isn't a configuration—it's an ongoing practice.
David designs enterprise security architectures at ZeroShare, with particular focus on zero trust implementations. His background includes 15 years building security infrastructure at hyperscale technology companies.
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This article reflects research and analysis by the ZeroShare editorial team. Statistics and regulatory information are sourced from publicly available reports and should be verified for your specific use case. For details about our content and editorial practices, see our Terms of Service.