Routing
Sending each request to the right model — semantic routing, mixture-of-models, and cost-aware cascades.
LiteLLM Router Setup: Fallback, Cost Routing & Model Pools
A step-by-step walkthrough of LiteLLM's Router class — defining model pools, configuring multi-provider fallbacks, enabling cost-based routing, and adding task-specific pools for math, code, and creative tasks.
LLM Routing: What It Is and How to Cut Costs With It
Does this request actually need your most expensive model? Semantic routing answers that question automatically — before the expensive model ever sees it.
Prefill Activation Routing: Predicting Model Failure Early
Most routing systems decide before the model does any work. Activation routing flips that — it reads what happens inside the model during prefill and uses those signals to decide whether to escalate.
RouteLLM vs vLLM Semantic Router: Which Should You Use?
RouteLLM, semantic-router, and vLLM each solve a different layer of the routing problem. Here's what each tool actually does, where they overlap, and how to choose.
Signal-Driven Routing for Mixture-of-Models in Production
Most LLM routers make one decision and commit. Signal-driven MoE routing makes continuous routing decisions across a request's full lifecycle — before generation, during generation, after generation — driven by signals from the query, the output, the system, and history.
When to Use Reasoning Models vs Standard LLMs
What the research on automatic routing between standard and reasoning models found — which task types justify the cost premium, what the accuracy tradeoff looks like, and how to automate the decision.