Fallbacks

Overview

Who is this for? Developers building production AI applications who need maximum reliability and availability, with automatic recovery from provider failures, rate limits, and service interruptions.

What you'll achieve: Implement intelligent fallback strategies that automatically switch between providers when errors occur, ensuring your application remains operational even when individual providers experience issues.

The AI Proxy provides sophisticated fallback mechanisms that automatically route requests to alternative providers when primary providers fail, ensuring high availability and resilience for your AI applications.

How Fallbacks Work

Automatic Provider Switching

When a request fails on the primary provider, the AI Proxy automatically:

Detects Provider Failure: Identifies retriable errors from the current provider
Selects Fallback Provider: Chooses the next available provider in the fallback chain
Preserves Request Context: Maintains original request parameters and context
Executes Fallback Request: Sends the same request to the fallback provider
Returns Unified Response: Delivers response in consistent format regardless of provider

Fallback Triggers

Fallbacks are automatically triggered by:

Rate Limiting: Provider rate limits exceeded
Service Unavailable: Provider downtime or maintenance
Timeout Errors: Request timeouts or network issues
Model Unavailable: Specific model temporarily unavailable
Quota Exceeded: Provider usage limits reached
Authentication Issues: API key problems or authorization failures

Basic Fallback Configuration

Sequential Provider Fallback

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Multi-Model Fallback Chain

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Provider-Specific Fallback

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Advanced Fallback Strategies

Conditional Fallback Rules

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Performance-Based Fallback

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Cost-Optimized Fallback

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Implementation Examples

Node.js Fallback Handler

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Python Resilient Client

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React Fallback Hook

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Fallback Scenarios

Rate Limit Recovery

Scenario: Primary provider hits rate limits
Action: Automatically switches to provider with available quota
Benefit: Maintains service availability without user interruption

Model Outage Handling

Scenario: Specific model becomes unavailable
Action: Falls back to equivalent model on different provider
Benefit: Continues operation with similar model capabilities

Regional Failover

Scenario: Provider experiences regional outage
Action: Switches to provider in different geographic region
Benefit: Maintains low latency and service availability

Quality Degradation Prevention

Scenario: Provider returns poor quality responses
Action: Falls back to known reliable provider
Benefit: Maintains response quality standards

Provider Compatibility

Fallback Sequences by Use Case

General Chat Completions

Primary: OpenAI GPT-4
Fallback 1: Anthropic Claude 3.5 Sonnet
Fallback 2: Google AI Gemini Pro
Fallback 3: Groq Llama 3.1

Vision Analysis

Primary: OpenAI GPT-4V
Fallback 1: Anthropic Claude 3.5 Sonnet
Fallback 2: Google AI Gemini Pro Vision

Code Generation

Primary: Anthropic Claude 3.5 Sonnet
Fallback 1: OpenAI GPT-4
Fallback 2: Groq CodeLlama

Cost-Optimized

Primary: Groq Llama 3.1 (fastest, cheapest)
Fallback 1: OpenAI GPT-3.5 Turbo
Fallback 2: Google AI Gemini Flash

Monitoring and Analytics

Fallback Metrics

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Performance Tracking

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Cost Analysis

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Best Practices

Fallback Chain Design

Compatible Models: Use models with similar capabilities in fallback chains
Performance Tiering: Order providers by latency and availability
Cost Consideration: Balance cost with reliability requirements
Capability Matching: Ensure fallback providers support required features

Error Handling Strategy

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Testing Fallbacks

Chaos Testing: Simulate provider failures to test fallback paths
Load Testing: Verify fallback performance under high load
End-to-End Testing: Test complete fallback scenarios
Monitoring Alerts: Set up alerts for fallback activations

Configuration Options

Fallback Policies

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Retry vs. Fallback

Retries: Same provider, multiple attempts (for transient errors)
Fallbacks: Different providers (for provider-specific issues)
Combined Strategy: Retry first, then fallback for maximum resilience

Circuit Breaker Integration

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Enterprise Features

Custom Fallback Logic

Business Rules: Implement domain-specific fallback rules
SLA Enforcement: Fallback based on service level agreements
Compliance Requirements: Ensure fallbacks meet regulatory requirements
Custom Metrics: Track business-specific fallback metrics

Multi-Region Fallbacks

Geographic Distribution: Fallback to providers in different regions
Data Residency: Respect data location requirements
Latency Optimization: Choose providers based on user location
Regulatory Compliance: Ensure fallbacks meet local regulations

Troubleshooting

Common Issues

Infinite Fallback Loops
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Context Loss in Fallbacks
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Performance Degradation
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Debugging Fallbacks

Request Tracing: Track request flow through fallback chain
Provider Logs: Monitor individual provider responses
Timing Analysis: Measure fallback overhead and latency
Error Classification: Categorize errors to optimize fallback triggers

Cost Considerations

Fallback Costs

Additional Requests: Fallbacks may increase total request volume
Provider Pricing: Different providers have different pricing models
Optimization: Use cost-effective providers as fallbacks when possible
Monitoring: Track fallback-related costs and optimize accordingly

Cost Optimization Strategies

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Next Steps

Retries & Error Handling: Combine with retry strategies
Load Balancing: Balance load across providers
Monitoring: Monitor fallback performance and costs