Question 1

What is an LLM router?

Accepted Answer

An LLM router is the layer between your application and model providers that decides which model, and which provider behind it, serves each request. Routing decisions can weigh cost, latency, quality, availability, region, or task type, so applications stop hardcoding a single model. Opper routes across 700+ models with explicit primitives: decision models, fallbacks, routing rules and model access rules.

Question 2

What is the difference between an LLM router and an LLM gateway?

Accepted Answer

The router is the decision, which model serves this request, and the gateway is the broader layer around it that adds authentication, observability, and an audit trail. On Opper the router lives inside the LLM gateway, so a routing decision, its fallbacks, and its trace all share one API.

Question 3

What is a semantic router?

Accepted Answer

The term covers two different things. The original semantic-router library routes requests to intents and tools rather than models, it embeds example utterances and picks the closest route by vector similarity without waiting for an LLM. The vLLM semantic-router project applies the idea to model selection instead, a programmable routing layer that picks a model path per request to improve quality, cost, and latency. On Opper you get both styles: rules and fallbacks for explicit routing, and a decision model that classifies the prompt itself, so every decision stays inspectable.

Question 4

What is a model router?

Accepted Answer

A model router picks a suitable model for each prompt, usually to save cost on easy requests while keeping quality on hard ones. Microsoft Foundry ships one as a product, a trained language model you deploy that routes each prompt to the most suitable underlying model in real time. The alternative is explicit routes you can read, test, and audit. That is the approach Opper takes, with a decision model such as Kev or Jev picking the branch and returning a probability for it.

Question 5

Can a decision model route my requests?

Accepted Answer

Yes. In a dynamic route, a decision model such as Kev, Jev or Clef reads the prompt, picks one of up to ten cases you describe, and the route sends the request down that branch. Decision models answer typed questions with a probability instead of writing text, so the classification is quick and inexpensive. You can also call them directly in the OpenAI Decisions or TypeSafe System One format and route in your own code.

Question 6

Does an LLM router add latency?

Accepted Answer

Very little. A plain model call adds only the gateway hop, and a route with a decision model adds one short classification call before the model answers. Where the request lands matters more: the same open model can run at very different speeds on different providers, which is why a routing rule can put the fastest provider first.

Question 7

How do fallback chains work?

Accepted Answer

Send backup models in the models field of a Chat Completions call, the same shape OpenRouter uses, or build the chain into a dynamic route on Control Plane and call the route by name. If a model fails, the next one in the list answers, and only the successful attempt is billed. Models served by more than one provider are pooled automatically, so a single name keeps working when one provider is busy, and measured any-route availability is published on our uptime page.

Question 8

Can I split traffic between models?

Accepted Answer

Yes. A traffic split in a dynamic route sends a percentage of requests down each branch, with the weights adding up to 100, which is how you canary a new model or compare two. Each response's routing metadata names the route version and the model that answered.

Question 9

Does a conversation stay on the same provider?

Accepted Answer

Yes, while it pays off. When a model is served by several providers, a conversation keeps going to the provider that served it while its prompt cache is warm, so cached prompts stay cheap. A routing rule's provider order applies to new conversations.

Question 10

Can an LLM router enforce region or compliance rules?

Accepted Answer

Yes, on Opper this is a model access rule. You can restrict routing to EU-only providers, to specific regions or countries, or to zero data retention providers, and anything that breaks a rule is rejected at the gateway before it reaches a provider. That turns residency requirements into routing policy instead of a code review checklist.

Question 11

Do I need a trained router model, or are rules enough?

Accepted Answer

Learned routers are real, RouteLLM reports up to 85% cost reduction while maintaining 95% GPT-4 performance by training a router between a strong and a cheap model, and they shine at high volume with stable traffic patterns. Most production teams still start with explicit rules and fallbacks because they are debuggable, auditable, and cheap to change. Opper follows that model and adds decision models to pick the branch, so a route can branch on the prompt without you training a router of your own.

Question 12

How is an LLM router different from a load balancer?

Accepted Answer

A load balancer spreads identical requests across identical backends. An LLM router makes two extra decisions, which model should answer, and which provider serving that model should get the request. Opper does both, some models are served by more than one provider and the gateway balances across them, so a single model name keeps working even when one provider is busy.

Question 13

Is there an open-source LLM router?

Accepted Answer

Several. RouteLLM trains a router between a strong and a cheap model, the vLLM Semantic Router is a programmable routing layer, and LiteLLM's router is the common self-hosted gateway choice. They fit when you want to run the infrastructure yourself. Opper is hosted in the EU, so decision-model routing, fallbacks and compliance rules come without a service to run.

Question 14

Which LLM router is best?

Accepted Answer

It depends on what you optimize for. OpenRouter is a popular hosted router with a large catalog, LiteLLM is the common self-hosted choice if you can run the infrastructure, and RouteLLM fits teams that want learned cost-quality routing. Opper is built for production teams that want European hosting and explicit routing, decision models, fallbacks, routing rules and model access rules across 700+ models, with no markup on provider prices and a 3% fee on credit purchases.

Send every request
to the right AI model

Build a route once, call it like a model

Call a route like any model

Let a decision model pick the route

Fall back to another model when one fails

Call a model by name and any provider that serves it can answer

Block any request that breaks your EU or retention rules

Decision models to pick the route

LexBox: litigation AI with 30× throughput headroom

LLM router FAQ

What is an LLM router?

What is the difference between an LLM router and an LLM gateway?

What is a semantic router?

What is a model router?

Can a decision model route my requests?

Does an LLM router add latency?

How do fallback chains work?

Can I split traffic between models?

Does a conversation stay on the same provider?

Can an LLM router enforce region or compliance rules?

Do I need a trained router model, or are rules enough?

How is an LLM router different from a load balancer?

Is there an open-source LLM router?

Which LLM router is best?

Send every request to the right AI model

Call a route like any model

Let a decision model pick the route

Fall back to another model when one fails

Call a model by name and any provider that serves it can answer

Block any request that breaks your EU or retention rules

LexBox: litigation AI with 30× throughput headroom

What is an LLM router?

What is the difference between an LLM router and an LLM gateway?

What is a semantic router?

What is a model router?

Can a decision model route my requests?

Does an LLM router add latency?

How do fallback chains work?

Can I split traffic between models?

Does a conversation stay on the same provider?

Can an LLM router enforce region or compliance rules?

Do I need a trained router model, or are rules enough?

How is an LLM router different from a load balancer?

Is there an open-source LLM router?

Which LLM router is best?

Send every request
to the right AI model