Definition: Model routing sends each AI request to the most suitable model, based on task, difficulty, cost or latency, instead of sending every request to one model.
A router inspects each request using rules, a classifier or a lightweight model, then picks a destination: a fast, low-cost model for routine tasks, a stronger model for hard reasoning, or a specialized model for code or images. Fallback routing retries with another model or provider when one fails or responds too slowly.
In many applications most traffic is routine, so sending it to cheaper models can cut cost and latency while keeping quality on the hard cases. Routing across providers also reduces dependence on any single vendor.
Routing usually lives in an LLM gateway or orchestration layer. Teams validate each route with evals and watch quality per model in production, since a route that saves money can quietly lower answer quality.
Ryz AI engineers build routing and fallback logic across Anthropic, OpenAI and other models, working on your team.
Thanks — your message has been sent. We’ll get back to you soon.
Something went wrong while sending your message. Please try again or email info@ryzlabs.com.