Routing
Sending each request to the right model — semantic routing, mixture-of-models, and cost-aware cascades.
When to Use Reasoning Models vs Standard LLMs
Reasoning models don't just generate text - they think before they answer. Here's what that actually means, how they're built, and when to use one over a standard LLM.
Signal-Driven Routing for Mixture-of-Models in Production
Signal-driven routing replaces static LLM classification with composable keyword, embedding, and domain signals - cutting costs 3.66x while preserving 95% of GPT-4 quality in production mixture-of-models deployments.
RouteLLM vs vLLM Semantic Router: Which One Actually Cuts Costs?
RouteLLM and vLLM Semantic Router both reduce LLM costs - but they solve fundamentally different problems. Here's the benchmark data, the architecture breakdown, and the exact decision framework to pick the right one.
Prefill Activation Routing: Predicting Model Failure Early
Prefill activation routing reads a model's internal hidden states before a single token is generated - predicting failure in advance, slashing inference costs by up to 74%, and routing queries to the right model every time.
LLM Routing: What It Is and How to Cut Costs With It
LLM routing directs each query to the right model instead of defaulting to the most expensive one. Done right, it cuts inference costs by 40–85% while retaining 95%+ of output quality.
LiteLLM Router Setup: Fallback, Cost Routing & Model Pools
A practical, code-first guide to setting up the LiteLLM Router in production - covering model pools, all six routing strategies, three fallback types, cost-based routing, and Redis-backed reliability.