AI Model Failover Strategies

AI Model Failover Strategies

AI models can fail for many reasons: provider outages, API errors, rate limits, or unexpected bugs. To ensure reliability and uptime, robust failover strategies are essential.

Why Failover Matters

• Minimize downtime: Keep your application running even if a model or provider fails.
• Improve user experience: Avoid interruptions and errors for end users.
• Meet SLAs: Maintain service level agreements for critical applications.

Core Failover Strategies

1. Multi-Provider Redundancy

Use multiple AI providers (e.g., OpenAI, Anthropic, Google) so you can switch if one fails.

class MultiProviderFailover:
    def __init__(self, providers):
        self.providers = providers  # List of provider clients
        self.last_used = 0

    def get_next_provider(self):
        self.last_used = (self.last_used + 1) % len(self.providers)
        return self.providers[self.last_used]

    def generate(self, prompt, model, **kwargs):
        for i in range(len(self.providers)):
            provider = self.get_next_provider()
            try:
                return provider.generate(prompt, model, **kwargs)
            except Exception as e:
                print(f"Provider {provider} failed: {e}")
        raise Exception("All providers failed")

2. Health Checks & Monitoring

Regularly check provider health and switch to healthy ones automatically.

import requests

def check_provider_health(url):
    try:
        response = requests.get(url, timeout=2)
        return response.status_code == 200
    except Exception:
        return False

## Example: check OpenAI and Anthropic
providers = {
    'openai': 'https://api.openai.com/v1/models',
    'anthropic': 'https://api.anthropic.com/v1/models',
}
health = {name: check_provider_health(url) for name, url in providers.items()}

3. Automatic Fallback Logic

Implement fallback logic in your API client or backend.

def robust_generate(prompt, model, providers):
    for provider in providers:
        try:
            return provider.generate(prompt, model)
        except Exception:
            continue
    raise Exception("All providers failed")

4. Circuit Breaker Pattern

Temporarily disable failing providers to avoid repeated errors.

import time

class CircuitBreaker:
    def __init__(self, failure_threshold=3, recovery_time=60):
        self.failure_count = 0
        self.failure_threshold = failure_threshold
        self.last_failure_time = None
        self.recovery_time = recovery_time
        self.open = False

    def record_failure(self):
        self.failure_count += 1
        self.last_failure_time = time.time()
        if self.failure_count >= self.failure_threshold:
            self.open = True

    def can_attempt(self):
        if not self.open:
            return True
        if time.time() - self.last_failure_time > self.recovery_time:
            self.open = False
            self.failure_count = 0
            return True
        return False

Best Practices

• Always monitor provider health and log failures.
• Use exponential backoff and retries for transient errors.
• Test failover regularly (simulate provider outages).
• Document your failover logic for maintainability.

Conclusion

Failover is critical for production AI systems. By combining multi-provider redundancy, health checks, fallback logic, and circuit breakers, you can ensure your AI-powered applications remain reliable—even when models or providers “bug out”.