LLM API Gateway
Scaling Production

Scaling Overview

Production LLM API gateways require sophisticated scaling strategies to handle variable loads while maintaining performance and cost efficiency. This guide covers horizontal scaling, auto-scaling, and capacity planning.

Horizontal Scaling Architecture

Kubernetes Horizontal Pod Autoscaler

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: api-gateway-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api-gateway
  minReplicas: 3
  maxReplicas: 50
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80
  - type: Pods
    pods:
      metric:
        name: requests_per_second
      target:
        type: AverageValue
        averageValue: 1000
  behavior:
    scaleDown:
      stabilizationWindowSeconds: 300
      policies:
      - type: Percent
        value: 10
        periodSeconds: 60
    scaleUp:
      stabilizationWindowSeconds: 0
      policies:
      - type: Percent
        value: 100
        periodSeconds: 15
      - type: Pods
        value: 4
        periodSeconds: 15
      selectPolicy: Max

Auto-Scaling Strategies

Predictive Scaling

Use historical data to predict traffic patterns and pre-scale before demand spikes:

predictive_scaling:
  enabled: true
  lookback_period: 7d
  prediction_window: 1h
  
  patterns:
    - name: daily_peak
      schedule: "0 9 * * 1-5"
      min_replicas: 10
      
    - name: weekly_low
      schedule: "0 0 * * 0,6"
      min_replicas: 2

Event-Driven Scaling

Scale based on business events and external triggers:

• Marketing campaigns: Pre-scale before product launches or promotions
• External integrations: Scale when partner traffic increases
• Cost thresholds: Scale down during low-revenue periods

Capacity Planning

Resource Requirements Calculator

Cost Optimization

# Right-sizing recommendations
instance_sizing:
  small:
    cpu: 1
    memory: 2Gi
    max_rps: 500
    cost_per_hour: "$0.05"
    
  medium:
    cpu: 4
    memory: 8Gi
    max_rps: 3000
    cost_per_hour: "$0.20"
    
  large:
    cpu: 16
    memory: 32Gi
    max_rps: 15000
    cost_per_hour: "$0.80"

Performance Optimization

Connection Pooling

connection_pool:
  max_connections: 1000
  max_per_host: 100
  idle_timeout: 60s
  keep_alive: true
  
  # Optimize for LLM API characteristics
  timeouts:
    connect: 5s
    request: 120s  # LLM responses can be slow
    response_header: 30s

Load Balancing Strategies

• Round Robin: Simple, even distribution across instances
• Least Connections: Route to instances with fewest active requests
• Weighted: Send more traffic to larger instances
• Geographic: Route to nearest region for latency optimization