What is an LLM router?

A large language model (LLM) router is a single API endpoint that sends each AI request to whichever model fits the job, based on cost, quality, or availability. If you've read our guide to what an LLM gateway is, the concept will feel familiar. "Router" and "gateway" describe the same category of tool—router just emphasizes the decision logic a bit more than the single-endpoint piece.

What is an LLM router?

An LLM router's job is the decision logic—which of your connected models actually handles a given request, once it's already coming through a single endpoint across providers. That decision comes down to three factors: task complexity, per-model cost, and whether your usual provider is available right now.

A router that's doing its job well picks the cheapest model that can still do the task correctly, not the most expensive one by default.

How LLM routers decide which model to use

Most routers work one of two ways: rule-based or learned.

Rule-based routing follows fixed logic you set up front—send code-generation tasks to Model A, send simple classification to Model B, and so on. It's predictable and easy to audit, but it doesn't adapt when a new model launches or pricing changes.

Learned routing uses live performance data to make the call instead of a fixed rulebook. Ramp's own router uses a Thompson-sampling approach: it treats each model as a bet and tracks how well that bet pays off on real traffic. It shifts more requests toward whichever model is winning, without needing anyone to hand-write new rules every time pricing or quality shifts.

That's the same system covered in Ramp's engineering blog on Thompson sampling model routing. This kind of routing is also what makes AI agents practical at scale—an agent can call the right model for each step without a human choosing it manually.

Open-source vs. managed routers

You don't have to build a router from scratch or buy one outright—there's a real spectrum in between.

Open-source routing libraries give you full control and no vendor dependency, but you own the infrastructure, the failover logic, and the cost-tracking yourself. Managed routers (Ramp Router included) handle that operational layer for you and usually ship as an OpenAI-compatible endpoint, so switching is a one-line base-URL change rather than a rewrite.

Which one makes sense depends on how much engineering time you want to spend maintaining routing logic versus building your actual product.

What to look for in an LLM router

Not every router does the same job well, so a few criteria matter more than a feature checklist.

Cost visibility. You want to see the model, tokens, and estimated cost behind every request, not just a monthly bill you have to reverse-engineer. This is the same visibility problem covered in what drives AI token cost increases and how to reduce AI token costs.

Fallback and reliability. If one provider has an outage or hits a rate limit, the router should be able to send that request somewhere else automatically instead of letting it fail.

Compatibility. An OpenAI-compatible endpoint means you can adopt a router with a base-URL change instead of rebuilding your integration.

Latency overhead. Routing adds a decision step before your request reaches a model—make sure that overhead is small enough not to matter for your use case.

How to set up an LLM router

1. Point your integration at the router's endpoint. Most routers are OpenAI-compatible, so this is a base-URL change rather than a rewrite.
2. Decide which factors matter most for your workload. Cost, latency, and quality don't all matter equally for every use case—weight them accordingly.
3. Start with a fallback list even if you want learned routing eventually. A simple rule-based safety net protects you while you evaluate a more adaptive approach.
4. Monitor actual routing decisions, not just cost. Confirm the router is sending requests where you'd expect before trusting it with production traffic.

Common routing mistakes to avoid

Routing on cost alone. The cheapest model that fails the task costs more than the expensive model that succeeds on the first try—quality has to be part of the decision, not an afterthought.

Sticking with rule-based routing after volume grows. Fixed rules that made sense at low volume become a maintenance burden at scale—hand-writing a new rule every time pricing or model quality shifts is exactly the gap learned routing is built to close.

Picking learned routing before you can audit it. If you can't explain why a request went where it did, you're not ready to trust it with production traffic.

Getting routing right

Getting routing right is what separates a cost-saving router from a slow, unaudited middle layer—the setup steps above (fallback, monitoring, revisiting the rules) get you there. Ramp built its own router around exactly this discipline: three years of production traffic, tuned toward the cheapest model that still clears the quality bar, at roughly a 30% cost reduction with no meaningful quality drop.