Hybrid Model Routing

Simple Explanation

Use multiple models together: fast, cheap models for easy tasks and bigger models for hard cases. Route requests based on confidence, cost, or explicit rules, and add fallbacks when the first choice fails.

Technical Explanation

Implement a router that scores tasks by complexity/uncertainty (e.g., heuristics or LLM self-estimates) and selects a model backend accordingly. Combine thresholds, guardrails, and retries with fallback escalation. Log decisions and outcomes to tune thresholds over time.

When to use - Large volume with mixed difficulty and tight latency/cost budgets - Quality-sensitive paths needing escalation to stronger models

Key Primitives - Scoring/thresholds: difficulty, confidence, domain tags - Backends: small vs large model clients - Fallbacks & retries: escalate on low confidence or errors - Observability: decision logs and success metrics

Try it - Run the sample: samples/08-hybrid-model-routing/hybrid_routing_demo.py - See the README: samples/08-hybrid-model-routing/README.md

Gotchas - Avoid flapping by adding hysteresis and minimum confidence deltas - Capture routing features so you can reproduce and debug decisions - Guard against runaway retries; cap attempts and escalate deterministically