Key Takeaway
By the end of this blueprint you will have a model routing layer that classifies incoming requests by complexity, routes simple tasks to smaller models, uses cascading with confidence checks for ambiguous requests, and provides fallback chains for provider outages — reducing LLM costs while maintaining quality.
Prerequisites
- An LLM gateway or direct access to multiple model providers (see LLM Gateway blueprint)
- Python 3.11+ for the routing logic
- Access to at least two model tiers (e.g., Claude Haiku and Claude Sonnet, or GPT-4o-mini and GPT-4o)
- An evaluation dataset to validate routing quality (see LLM Evaluation blueprint)
- Redis for routing decision caching and metrics
Routing Strategies
There are three primary routing strategies, each suited to different scenarios. Classification-based routing uses a lightweight model to score request complexity and routes based on the score. Cascading starts with the cheapest model and escalates only when confidence is low. Rules-based routing uses deterministic rules (request type, user tier, feature) to select models without an LLM call. In practice, production systems combine all three: rules handle known patterns, classification routes ambiguous requests, and cascading provides a safety net.
| Strategy | Latency Overhead | Cost Savings | Quality Risk | Best For |
|---|---|---|---|---|
| Classification | 50-200ms (classifier call) | 40-60% | Low with good classifier | High-volume mixed workloads |
| Cascading | None (cheap model first) | 30-50% | Very low (always escalates) | Quality-critical applications |
| Rules-based | None | 20-40% | None (deterministic) | Known task types, user tiers |
| Hybrid (all three) | 0-200ms | 50-70% | Lowest | Production systems at scale |
Complexity Classifier
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