Model Routing
Automatic routing of AI requests to the optimal model based on task type, cost, latency, and quality requirements.
Model routing automatically selects the best model per request – premium for important tasks, cheap for batch. Saves 70-80% costs.
Explanation
Model routing optimizes the cost-quality ratio: Task classifier analyzes incoming requests, routes to matching model. Typical strategy: Batch tasks → DeepSeek R1 (cheap), Analysis → Claude/DeepSeek, Creative → GPT-5 (premium), Multimodal → Gemini. Implementation via AI gateways (OpenRouter, Portkey) or custom logic.
Marketing Relevance
Typically saves 70-80% AI costs without quality loss. Enables enterprise-scale AI with controlled budgets.
Example
Marketing platform: 1,000 batch emails → DeepSeek ($0.50), 10 premium headlines → GPT-5 ($0.15). Total: $0.65 instead of $15 with GPT-5-only.
Common Pitfalls
Classifier itself causes costs/latency. Wrong routing decisions on edge cases. Quality gates needed for fallback.
Origin & History
Model routing emerged with the proliferation of LLM providers in 2023/2024. OpenRouter and Portkey pioneered unified API abstractions with intelligent model selection.
Comparisons & Differences
Model Routing vs. Single Model Approach
Single-model uses one model for everything; model routing matches tasks to optimal models for cost and quality.
Model Routing vs. Load Balancing
Load balancing distributes load evenly; model routing selects intelligently based on task type and model strengths.