Glossary

Model Routing

The practice of dynamically directing AI requests to different language models based on task complexity, cost requirements, and quality needs to optimize spending.

Model routing is the practice of dynamically selecting which language model handles each request based on task characteristics, cost constraints, and quality requirements. Instead of using one model for everything, model routing matches each request to the cheapest model that can handle it effectively — often reducing costs by 40-70%.

Why Model Routing Matters

Most AI applications default to a single model — usually a frontier model like GPT-4o or Claude Sonnet — for all requests. This is like using a Ferrari for both race day and grocery shopping. Many requests are simple enough for a much cheaper model to handle perfectly.

Consider a typical AI application's request distribution:

  • 50-60% simple tasks: Classification, yes/no questions, entity extraction, formatting
  • 25-35% medium tasks: Summarization, Q&A, simple writing, data analysis
  • 10-15% complex tasks: Multi-step reasoning, creative writing, code generation

    • If you route the simple 60% to a model that costs 15x less, you cut your total spend by 50%+ with minimal quality impact.

    How Model Routing Works

    Rule-Based Routing

    The simplest approach: define rules that match request characteristics to models.

    Examples:

  • If task type is "classification" → use GPT-4o-mini
  • If input length > 50,000 tokens → use Claude Sonnet (200K context)
  • If task requires code generation → use Claude Sonnet
  • If response doesn't need to be real-time → use batch API

    • Pros: Simple, predictable, easy to debug

    Cons: Requires manual rule creation, doesn't adapt to new patterns

    Classifier-Based Routing

    Use a fast, cheap classifier model to analyze each request and determine complexity before routing:

  • Send the request to a small classifier (cost: ~$0.001)
  • Classifier returns: "simple," "medium," or "complex"
  • Route to the appropriate model based on classification

    • Pros: Adapts to diverse request types automatically

    Cons: Adds latency (one extra LLM call), classifier can misroute

    Cascading/Fallback Routing

    Start with the cheapest model and escalate only if quality is insufficient:

  • Send request to cheap model (e.g., GPT-4o-mini)
  • Evaluate response quality (heuristic or LLM-based)
  • If quality is below threshold, retry with expensive model (e.g., GPT-4o)

    • Pros: Only pays premium prices when necessary

    Cons: Higher latency for escalated requests, evaluation adds cost

    Embedding-Based Routing

    Use embeddings to match incoming requests against a library of known request types:

  • Embed the incoming request
  • Find the nearest neighbor in a library of labeled examples
  • Route based on the label of the nearest match

    • Pros: Fast, no LLM call needed for routing

    Cons: Requires building and maintaining the example library

    Model Routing in Practice

    Setting Up a Model Router

    A basic model router consists of:

  • Request analyzer: Determines task characteristics (type, complexity, required capabilities)
  • Model registry: Maps available models with their costs, capabilities, and rate limits
  • Routing policy: Rules or algorithms that match requests to models
  • Quality monitor: Tracks output quality by model to validate routing decisions
  • Cost tracker: Measures actual savings from routing decisions

    • Example Routing Configuration

    Default: gpt-4o-mini ($0.15/$0.60 per 1M tokens)

Escalate to gpt-4o ($2.50/$10.00) when:
  - Task is code generation
  - Task requires multi-step reasoning
  - User is on premium tier
  - Quality score of mini response < 0.8

Escalate to claude-sonnet ($3/$15) when:
  - Input exceeds 128K tokens
  - Task requires creative writing
  - Tool use with >5 functions

Use batch API when:
  - Response not needed in real-time
  - Task is content generation or evaluation

    Measuring Routing Effectiveness

    Track these metrics to validate your routing:

  • Cost per request by route: Are cheap-model routes actually cheaper?
  • Quality by route: Are cheap-model responses meeting quality thresholds?
  • Escalation rate: What percentage of requests get escalated?
  • Latency by route: How does routing affect response time?
  • User satisfaction by route: Do users notice any quality difference?

    • Common Model Routing Mistakes

  • No quality monitoring: Routing to cheaper models without measuring quality impact
  • Static routing: Not updating rules as models improve and prices change
  • Over-routing to expensive models: Being too conservative and not trusting cheaper models
  • Ignoring latency: Cheaper models are often faster — this can improve user experience
  • Not A/B testing: Always validate routing changes with controlled experiments

    • 🦞How ClawHQ Helps

    ClawHQ makes model routing easy with built-in analytics that show which requests could be handled by cheaper models. See cost and quality breakdowns by model, identify over-provisioned routes, and measure the ROI of every routing decision. ClawHQ's dashboards help you build and refine routing policies with data, not guesses — and track savings as you optimize.

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    Frequently Asked Questions

    What is model routing in AI?

    Model routing is the practice of dynamically directing AI requests to different language models based on task complexity and cost requirements. Simple tasks go to cheap models (GPT-4o-mini), complex tasks to capable models (GPT-4o). This typically saves 40-70% on AI costs.

    How much can model routing save?

    Model routing typically saves 40-70% on AI costs. The savings come from the fact that 50-60% of requests in most applications are simple enough for models that cost 10-20x less. The key is monitoring quality to ensure cheaper models meet your requirements.

    How do I implement model routing?

    Start with rule-based routing: categorize your request types and assign models accordingly. Monitor quality metrics to validate decisions. As you scale, consider classifier-based or embedding-based routing for more dynamic model selection. ClawHQ provides the analytics to guide these decisions.

    Does model routing affect response quality?

    When done correctly, model routing maintains quality for most requests. Simple tasks like classification or extraction don't benefit from expensive models. The key is monitoring quality alongside cost to ensure routing decisions are sound. A/B testing is essential.

    Related Terms

    AI Cost Optimization

    Strategies and techniques for reducing AI infrastructure and API spending while maintaining or improving output quality, including model routing, caching, and prompt engineering.

    Token Pricing

    The pricing model used by LLM providers where costs are calculated based on the number of tokens (text fragments) processed in API requests and responses.

    Cost Per Token

    The unit price charged by LLM providers for each token processed, typically measured in dollars per million tokens for both input and output.

    AI Spend Management

    The comprehensive discipline of planning, tracking, optimizing, and governing AI-related expenditures across an organization.

    Inference Costs

    The computational expenses incurred when running trained AI models to generate predictions or outputs, including GPU compute, token processing, and API charges.

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