LLM Proxy Model Fallback

Ensure continuous AI service availability with intelligent model fallback. Automatically switch between LLM providers when errors occur, rate limits are hit, or performance degrades.

99.99%

Uptime SLA

<100ms

Failover Time

Dropped Requests

Automatic Failover Flow

GPT-4 (Primary)

Rate limit exceeded - 429 error

Failed

↓ Automatic Fallback ↓

⚡

Claude 3 Opus

Successfully handling request

Active

↓ Standby ↓

✓

Gemini Pro

Ready for next fallback

Standby

Fallback Triggers

Multiple conditions that can initiate automatic model fallback

🚫

Rate Limits

Automatic switch when rate limit thresholds are reached

⏱️

High Latency

Fallback when response times exceed configured limits

❌

API Errors

Handle 5xx errors, timeouts, and service unavailability

💰

Cost Thresholds

Switch to cheaper models when budget limits approached

📊

Quality Scores

Fallback when response quality drops below thresholds

🔒

Auth Failures

Handle expired keys or authentication errors

🌐

Regional Issues

Geographic failover for regional outages

📈

Load Balancing

Distribute load across multiple models intelligently

Model Fallback Chains

Configure intelligent fallback chains based on model capabilities and costs

Primary Model	Fallback Chain	Trigger	Use Case
G GPT-4 Turbo	Claude 3 → Gemini Pro	Rate limit, Error	Complex reasoning
C Claude 3 Opus	GPT-4 → GPT-3.5	Latency > 5s	Long-form content
G Gemini Ultra	GPT-4 → Claude 3	Availability	Multimodal tasks
G GPT-3.5 Turbo	Claude Instant → Gemini Flash	Cost threshold	High-volume chat
L Llama 3 70B	Mistral Large → GPT-3.5	All triggers	Self-hosted priority

Configuration Example

                fallback_config.yaml
                YAML Configuration
            

                # Model fallback configuration
fallback:
  chains:
    - name: "premium-reasoning"
      models:
        - provider: "openai"
          model: "gpt-4-turbo"
          priority: 1
        - provider: "anthropic"
          model: "claude-3-opus"
          priority: 2
        - provider: "google"
          model: "gemini-pro"
          priority: 3
      
      triggers:
        - type: "rate_limit"
          enabled: true
        - type: "latency"
          threshold_ms: 5000
        - type: "error_rate"
          threshold: 0.05
      
      retry:
        max_attempts: 3
        backoff: "exponential"
        
    - name: "fast-chat"
      models:
        - provider: "openai"
          model: "gpt-3.5-turbo"
        - provider: "anthropic"
          model: "claude-instant"
      triggers:
        - type: "all"
            

Fallback Features

⚡ Instant Failover

Sub-100ms automatic switching between models with zero user-visible interruption.

🔄 Smart Retry Logic

Exponential backoff with jitter to prevent thundering herd on recovery.

📊 Real-time Monitoring

Live dashboards showing model health, fallback events, and performance metrics.

💰 Cost Optimization

Balance performance with cost by configuring budget-aware fallback chains.

🎯 Capability Matching

Ensure fallback models meet minimum capability requirements for your use case.

📝 Full Audit Trail

Complete logging of all fallback events for debugging and compliance.