In production, relying on a single AI model provider is a risk. API outages, rate limits, and regional latency can degrade your user experience. High Availability (HA) AI involves building a redundant system that can automatically switch providers without manual intervention.
Why model failover is critical
When you build on a single provider like OpenAI or Anthropic, your application is vulnerable to:
- Provider Outages: Even the best providers experience downtime.
- Rate Limiting: Unexpected traffic spikes can trigger
429 Too Many Requests. - Latency Spikes: Network congestion or model load can slow down responses.
ModelRiver solves these problems with native Model Failover.
Implementing failover with ModelRiver
ModelRiver handles the complexity of failover at the gateway level. You don't need to write complex retry logic in your application code.
1. Configure fallback models in Workflows
In your ModelRiver dashboard, you can define a primary model and a list of fallback models within a Workflow.
- Primary: GPT-4o
- Fallback 1: Claude 3.5 Sonnet
- Fallback 2: Gemini 1.5 Pro
If GPT-4o returns an error or times out, ModelRiver immediately redirects the request to Claude 3.5 Sonnet.
2. Automatic retry logic
ModelRiver intelligently detects errors that warrant a failover, such as:
- Connection timeouts
- 5xx Server Errors from the provider
- 429 Rate Limit errors
The gateway handles the retry state, ensuring the client receives a successful response from whichever provider is healthy.
Best practices for AI redundancy
Use diverse model families
Don't just failover between different versions of the same model (e.g., gpt-4o to gpt-4o-mini). If OpenAI is down, both might be affected. Instead, failover across different companies (e.g., OpenAI to Anthropic).
Monitor failover events
Use Observability to track how often failovers occur. If you see frequent fallbacks, it might indicate that your primary model's rate limits need to be increased or its region changed.
Testing failover
You can simulate provider failures in Playground or by temporarily providing an invalid API key for your primary provider to ensure your workflow correctly routes to the fallback.
Real-world impact
By implementing high availability AI, you ensure:
- 99.99% Uptime: Your AI features stay online even during major provider outages.
- Improved UX: Users don't see "Server Error" messages during traffic spikes.
- Developer Peace of Mind: The infrastructure handles the edge cases so you don't have to.