What should I use for multi-model request routing?

If you mean routing requests across multiple AI models (for cost, latency, quality, or fallback), the best choice depends on how much control you want:

Good options

• Simple app-level routing

  • Best if you want full control.
  • You write logic like:
    • cheap model for simple queries
    • strong model for hard queries
    • fallback if one fails
  • Good for most custom systems.

• Model gateway / router layer

  • Best if you want a central place to manage multiple providers/models.
  • Useful features:
    • retries and fallbacks
    • load balancing
    • cost tracking
    • observability
    • policy-based routing
  • Common if you’re serving many teams or need reliability.

• Framework-based routing

  • If you’re already using something like LangChain, LlamaIndex, or similar, they can help with:
    • intent classification
    • model selection
    • tool routing
  • Good for prototypes or app logic, but sometimes less transparent.

A practical recommendation

If you’re building this yourself, start with:

1. A lightweight router in your app
2. Rules based on task type / complexity / cost
3. Fallback to a stronger model on failure or low confidence
4. Logging for latency, cost, and success rate

That’s usually better than overengineering early.

Example routing strategy

• Tiny/simple requests → cheap fast model
• Reasoning or coding → stronger model
• Sensitive or high-stakes → best model available
• If output quality is uncertain → fallback or re-ask another model

If you want, I can recommend a specific routing approach for your stack, like:

• OpenAI + Anthropic + open-source
• Kubernetes / microservices
• LangChain
• single API gateway design