Model Routing
Automatically selecting the best AI model for each query based on task type, complexity, cost, and latency requirements. Smart routers analyze your prompt and route it to the optimal model — saving money on simple tasks and ensuring quality on complex ones.
Why it matters
No single model is best at everything. Some are cheap and fast, others are slow but brilliant at hard reasoning. Model routing automatically sends each request to the right one, so you don't overpay for simple tasks or get weak answers on complex ones. For anyone running AI at scale, this quietly cuts costs and raises quality without users noticing anything except better, faster results.
A concrete example
A support app gets two questions: "What are your hours?" and "Explain why my invoice math is wrong." A router sends the first to a tiny, cheap model that answers instantly, and the second to a powerful reasoning model that can work through the numbers. Users get quick replies to easy questions and thorough ones to hard questions, while the company avoids paying premium rates on every single message.
How to use it
Routing pays for itself because most requests do not need your most capable model. A workable default: fast cheap models for extraction, classification, reformatting and short summaries; expensive reasoning models for genuinely hard problems and final drafts. Whether a router picks automatically or you choose per task, the saving comes from the same insight — matching the model to the difficulty rather than to the hardest thing you ever do.
The common mistake
Routing on a keyword rather than on difficulty. A short request can be hard and a long one trivial, so rules based on length or surface features send exactly the wrong things to the expensive model.
Related terms
Inference Cost
The computational expense of running a trained AI model to generate outputs. Measured in cost per token or per request. Varies dramatically between models — GPT-4 class models cost 10-50x more per token than smaller models.
Benchmark
A standardized test or dataset used to evaluate and compare AI model performance. Common benchmarks include MMLU (knowledge), HumanEval (coding), and MT-Bench (conversation). Helps users choose the right model for their needs.
Mixture of Experts (MoE)
An AI architecture where multiple specialized sub-networks (experts) handle different parts of a task. A gating network routes each input to the most relevant experts, achieving high performance with fewer active parameters per query.
AI Dubbing
Automated translation and re-voicing of audio/video content into other languages while preserving the original speaker's voice characteristics, timing, and emotional delivery.
AI Orchestration
Coordinating multiple AI models, tools, and data sources in a unified pipeline. An orchestration layer manages prompt routing, context passing, error handling, and output aggregation across different AI services.
API (Application Programming Interface)
A way for software applications to communicate with each other. AI APIs let developers integrate AI capabilities into their own applications programmatically.