LLM Proxy Production Deployment - Complete Operations Guide

Prerequisites

Before deploying an LLM proxy to production, ensure you have the necessary infrastructure, security controls, and operational processes in place. Production deployments require careful planning to ensure reliability and security.

📋 Minimum Requirements

Container orchestration platform (Kubernetes, ECS, or Docker Swarm), Redis or similar cache, secrets management system, monitoring infrastructure, and load balancer with TLS termination.

Infrastructure Sizing

Scale	Requests/Day	Proxy Instances	Cache Memory	Network Bandwidth
Small	<100K	2-3 replicas	2 GB	100 Mbps
Medium	100K-1M	5-10 replicas	8 GB	500 Mbps
Large	>1M	10+ replicas	32 GB+	1 Gbps+

Infrastructure Setup

1 Container Registry & Images

Build and store container images in a private registry. Use semantic versioning for image tags and maintain a CI/CD pipeline for automated builds.

✓Private container registry (ECR, GCR, Docker Hub)
✓Automated build pipeline on git push
✓Vulnerability scanning for images
✓Multi-architecture builds (ARM/AMD64)

2 Secrets & Configuration

Configure secrets management before deployment. Never store API keys in container images or environment variables exposed in process listings.

✓HashiCorp Vault, AWS Secrets Manager, or equivalent
✓Secret rotation policies configured
✓Separate secrets per environment (dev/staging/prod)
✓Access logging for secret retrieval

Deployment Steps

                        kubernetes-deployment.yaml
                        YAML
                    

                        apiVersion: apps/v1
kind: Deployment
metadata:
  name: llm-proxy
  labels:
    app: llm-proxy
spec:
  replicas: 3
  selector:
    matchLabels:
      app: llm-proxy
  template:
    metadata:
      labels:
        app: llm-proxy
    spec:
      containers:
      - name: proxy
        image: your-registry/llm-proxy:v1.2.0
        ports:
        - containerPort: 8000
        env:
        - name: REDIS_URL
          valueFrom:
            secretKeyRef:
              name: llm-proxy-secrets
              key: redis-url
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "1Gi"
            cpu: "1000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 10
          periodSeconds: 30
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000
          initialDelaySeconds: 5
          periodSeconds: 10
                    

⚠️ Deployment Best Practices

Use rolling updates with maxSurge and maxUnavailable settings. Implement pod disruption budgets for high availability. Always test deployments in staging before production. Maintain rollback capability.

Monitoring & Observability

Comprehensive monitoring is essential for operating LLM proxies in production. Implement metrics collection, log aggregation, and alerting to detect and respond to issues quickly.

📊 Key Metrics to Track

✓Request count by endpoint, model, and provider
✓Token usage (input, output, total)
✓Request latency (p50, p95, p99)
✓Error rates by type and provider
✓Cache hit rate
✓Active connections and queue depth

🔔 Critical Alerts

✓Error rate exceeds 1%
✓Latency p95 exceeds 5 seconds
✓Cache hit rate drops below 30%
✓Provider API errors increase
✓Cost exceeds daily budget
✓Certificate expiration warning

Scaling Strategies

Plan for both vertical and horizontal scaling to handle traffic growth and seasonal variations. Implement autoscaling based on CPU, memory, and custom metrics like request queue depth.

                        hpa.yaml
                        YAML
                    

                        apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: llm-proxy-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: llm-proxy
  minReplicas: 3
  maxReplicas: 20
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
                    

Production Go-Live Checklist

✅ Pre-Launch Verification

✓ TLS certificates installed and valid

✓ Secrets injected from vault (not env vars)

✓ Health checks responding correctly

✓ Monitoring dashboards configured

✓ Alert channels tested

✓ Load testing completed

✓ Rollback procedure documented

✓ Runbook created for incidents

✓ Rate limits configured per plan

✓ Cache warming completed

🔗 Related Operations Guides

Continue learning: Architecture Explained | Why Use LLM Proxy | Security & Rate Limiting | Load Balancing Strategies