Kubernetes is the natural deployment target for AI security gateways. Here's how to do it right.
Deployment Architecture
Namespace Isolation
Deploy AI gateways in a dedicated namespace with network policies limiting communication to required services.
Resource Management
- CPU: Start with 500m request, 2000m limit per replica
- Memory: Start with 512Mi request, 2Gi limit
- Scale based on actual usage patterns
High Availability
- Minimum 3 replicas across availability zones
- Pod disruption budget: maxUnavailable 1
- Pod anti-affinity for zone distribution
Helm Chart Configuration
Key values to configure:
- Replica count and HPA settings
- Resource requests and limits
- Ingress configuration
- TLS certificate management
- Logging and monitoring integration
Scaling Strategies
Horizontal Pod Autoscaler
Scale based on CPU utilization (target 70%) or custom metrics (requests per second).
Vertical Pod Autoscaler
Automatically adjust resource requests based on actual usage. Use in "recommend" mode initially.
Monitoring
- Prometheus metrics for request latency, throughput, and error rates
- Grafana dashboards for visualization
- Alerting on SLA breaches and error spikes
A well-deployed AI gateway on Kubernetes should be invisible to users while providing complete protection and visibility.
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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