What is LLM Routing? A Developer's Guide

Let me paint a picture you probably recognize. You've got a production app making hundreds of LLM calls a day. Maybe it's a chatbot, maybe it's a document processing pipeline, maybe it's a coding assistant. Every single request goes to GPT-4o because, well, it works. Your users are happy. Your finance team is not.

Last month you spent $4,200 on OpenAI API calls. And here's the thing that should keep you up at night: probably 60-70% of those requests didn't need GPT-4o. A "summarize this email" request doesn't require the same firepower as "analyze this legal contract for liability clauses." You're using a sledgehammer for every nail, including the thumbtacks.

That's the problem LLM routing solves.

What LLM Routing Actually Is

LLM routing is exactly what it sounds like: an intelligent layer that sits between your application and multiple large language model providers, deciding which model handles each request. But "routing" undersells it. It's not just a proxy that round-robins between endpoints. A good router evaluates each request and picks the optimal model based on the criteria you set — cost, quality, speed, or some combination of all three.

Think of it like a load balancer, but instead of distributing traffic across identical servers, it's distributing prompts across fundamentally different models with different strengths, speeds, and price points.

A simple classification task? Route it to Claude 3.5 Haiku or GPT-4o-mini. Costs a fraction of a cent. Complex reasoning over a 50-page document? That goes to Claude Opus 4 or GPT-4o. Creative writing? Maybe Gemini 2.5 Pro handles that better for your specific use case.

The key insight: no single model is the best at everything. And you shouldn't pay top-tier prices for tasks that a cheaper model handles just as well.

How Routing Works in Practice

The architecture is simpler than you'd think. Your app makes a standard OpenAI-compatible API call. The router intercepts it, classifies the request, selects the best model, forwards it, and returns the response in the same format you'd expect. Your code barely changes.

Here's a typical setup with Requesty as an example:

import OpenAI from "openai";

// Just change the base URL — that's it
const client = new OpenAI({
  apiKey: "your-router-key",
  baseURL: "https://router.requesty.ai/v1",
});

const response = await client.chat.completions.create({
  model: "router",  // Let the router decide
  messages: [{ role: "user", content: "Summarize this email..." }],
});

That's it. No SDK changes. No new abstractions. You swap a base URL and an API key, and now you've got intelligent routing across dozens of models. The router handles provider failover, rate limits, and model selection behind the scenes.

The Four Routing Strategies

Not every team optimizes for the same thing. Here are the four main strategies routers use:

1. Cost Optimization

The most common use case. The router picks the cheapest model that can handle your request at an acceptable quality level. For simple tasks — classification, summarization, entity extraction — this means using models that cost 10-50x less than GPT-4o. You set a quality floor, and the router finds the cheapest path above it.

2. Quality Maximization

For requests where you absolutely need the best answer regardless of cost. The router selects the highest-performing model for that specific task type. This is less about saving money and more about getting better results than any single model could provide, since different models excel at different things.

3. Latency Optimization

When speed matters more than anything — real-time chat, autocomplete, streaming responses — the router picks the fastest model that meets your quality threshold. This often means using smaller models or providers with lower current load. Some routers track real-time latency across providers and route accordingly.

4. Custom / Weighted

You define the formula. Maybe you want 50% weight on cost, 30% on quality, 20% on latency. Or you want specific models for specific task types: always use Claude for code, always use Gemini for long-context, always use GPT-4o-mini for chat. Most routers let you build these rules.

Real Numbers: What Does Routing Actually Save?

Let's get specific. Here's a rough breakdown based on typical production workloads:

In this scenario, your blended cost per input token drops by roughly 60-80%. And for the tasks where a cheaper model runs, you often don't notice any quality difference. A classification task doesn't get "better" because you threw a more expensive model at it.

When You Need a Router (And When You Don't)

Routing isn't for everyone. Here's my honest take:

You probably need a router if:

• You're spending more than $500/month on LLM APIs
• You have diverse task types (chat, code, summarization, extraction)
• You want provider redundancy (if OpenAI goes down, traffic shifts automatically)
• You're evaluating multiple models and want easy A/B testing
• You need to comply with data residency requirements by routing to specific providers

You probably don't need a router if:

• You have a single, well-defined use case that one model handles perfectly
• Your API spend is under $100/month — the optimization juice isn't worth the squeeze
• You're building a prototype and just need something that works

Comparing LLM Routers

The router landscape is still young, but a few serious players have emerged. Here's how they stack up on the things that matter:

For a deeper comparison, check out our router comparison page which tracks features, pricing, and supported models in real time.

Getting Started in 5 Minutes

The beauty of modern LLM routers is that most of them are OpenAI SDK compatible. Here's a complete example using the OpenAI Python SDK with routing:

import openai

# Point to your router instead of OpenAI directly
client = openai.OpenAI(
    api_key="your-router-api-key",
    base_url="https://router.requesty.ai/v1",
)

# Option 1: Let the router pick the best model
response = client.chat.completions.create(
    model="router",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What's the capital of France?"},
    ],
)

# Option 2: Request a specific model through the router
response = client.chat.completions.create(
    model="anthropic/claude-sonnet-4-20250514",
    messages=[
        {"role": "user", "content": "Review this code for bugs..."},
    ],
)

# Same response format as OpenAI — nothing changes
print(response.choices[0].message.content)

That's genuinely it. If you're already using the OpenAI SDK, you're two lines of code away from intelligent routing across every major LLM provider.

Don't Hardcode Your AI Provider

Here's my actual advice: even if you're happy with GPT-4o today, don't hardcode it. The LLM landscape shifts every few months. A new model drops, prices change, a provider has an outage. If your infrastructure can only talk to one provider, you're locked in.

Routing gives you optionality. You can switch models, try new providers, optimize costs, and handle failures — all without changing your application code. It's the same reason you use a CDN instead of serving assets from a single server, or a message queue instead of direct HTTP calls.

The LLM space is moving fast. Your infrastructure should be able to keep up. Start with a router, set sensible defaults, and let the routing layer handle the complexity of a multi-model world.

Check out our router comparison to find the right fit, or browse model benchmarks to see which models perform best for your use case.