Increased fallbacks

We’ve enhanced our fallback and retry system to provide even greater reliability and flexibility in production use cases. Previously, Orq.ai allowed users to define a single fallback model if the primary model failed to generate a satisfactory output. Now, we’ve increased the number of fallback models to five, giving users even more control over model orchestration without additional coding.

What’s New?

• Up to Five Fallback Models – Previously limited to one fallback model, users can now configure up to five fallback models for increased reliability.
• Flexible Fallback Configuration – The ability to define multiple fallback models allows for more advanced use cases, such as distributing API requests across different endpoints. For example:
  • Azure provides different regional deployments for models like GPT-4o. If a user is making a large number of API calls through the Sweden Central region, they can configure their first fallback to West Europe, and subsequent fallbacks to other locations if rate limits are hit.
  • If a primary model encounters rate limits, Orq can seamlessly transition requests to alternative endpoints, ensuring uninterrupted performance.

This update doesn’t change the core retry and fallback functionality that users already love—it simply expands its capabilities, making Orq.ai’s automated failover system even more powerful.

Try out the new expanded fallbacks and let us know how they enhance your workflow!