LLM Routing & Failover: A Practical Guide

LLM routing — sending each request to the right model instead of one model for everything — is the highest-leverage cost and reliability decision most teams aren't making. Done well, cost routing cuts spend by more than 60% with no quality loss, and failover keeps your app up when a provider goes down. This guide covers the three routing patterns that matter (cost, quality, failover), tools like claude-code-router, and how to implement routing without building it yourself.

What LLM routing is

LLM routing means sending each request to the right model instead of defaulting everything to one. There are three patterns worth knowing, and they stack:

• Cost routing — cheap model first, escalate hard cases to a flagship.
• Quality routing — match model to task type (a coding model for code, a cheap model for classification).
• Failover — reroute to another provider/model on error or rate-limit.

All three require reaching multiple models through one interface — which is why routing lives at a gateway or router layer.

Why route

Three reasons, in order of impact: cost (most teams overpay by sending easy traffic to a flagship), reliability (a single provider's outage takes your app down without failover), and quality (the best model for code isn't the best for cheap classification). The first is the biggest and most measurable.

Cost routing (the big win)

The single highest-leverage change: route the easy majority of traffic to a cheap model and reserve a flagship for the hard minority. The savings are large and concrete. For a 100M-input / 20M-output monthly workload:

Two patterns implement this:

• Classify-then-escalate — a cheap model (or heuristic) decides "is this hard?" and sends only the hard cases to a flagship.
• Try-cheap-then-verify — run a cheap model, apply a check the task already needs (tests pass, valid JSON, tool call succeeds), and retry on a flagship only on failure. The simplest to start with.

Quality routing

Beyond cost, route by task type: a dedicated coding model (Qwen3 Coder, Grok Code Fast) for code, a cheap general model for classification and extraction, a multimodal model for documents, a flagship for hard reasoning. Quality routing ensures each request hits a model that's actually good at it — often cheaper and better than one general default.

Failover

Providers have outages and rate limits. Failover detects an error or timeout and automatically retries on a different provider or model, so a single provider's bad day doesn't become your downtime. It only works when the fallback targets are reachable through one interface — so failover is implemented at the gateway/router layer that pools providers behind an OpenAI-compatible API. A good failover policy also handles rate-limit (429) responses by rerouting rather than backing off.

Tools: claude-code-router & more

• claude-code-router — an open-source tool that routes Claude Code (and similar agents) across models/providers, letting you use cheaper or alternative models for parts of an agent's work.
• LiteLLM — a popular self-host router: run it yourself, pay no platform fee, keep traffic in your infrastructure.
• Hosted gateways (OpenRouter, DataLLM Lab, Portkey) — routing + failover + one bill across providers, no infrastructure to run. Compared in our OpenRouter alternatives guide.

Build vs buy

Either way the foundation is OpenAI-compatibility: because switching models is a config change, routing logic stays simple. Here's the minimal try-cheap-then-escalate pattern through a gateway:

from openai import OpenAI
client = OpenAI(base_url="https://www.datallmlab.com/v1", api_key="$DATALLMLAB_API_KEY")

def solve(prompt, check):
    cheap = client.chat.completions.create(model="deepseek/deepseek-v3.2", messages=[{"role":"user","content":prompt}])
    out = cheap.choices[0].message.content
    if check(out): return out                # cheap model passed — done
    strong = client.chat.completions.create(model="anthropic/claude-opus-4.7", messages=[{"role":"user","content":prompt}])
    return strong.choices[0].message.content     # escalate only on failure

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