LLM Router vs LiteLLM Proxy vs Hosted Gateways: A Practical Guide

Why this matters

Multi-model routing is the default advice for anything calling more than one model, and much of it skips the first question: does your app need a router at all?

A router sits between your code and a model and decides — which model, which provider, which parameters. That decision can be made by a function in your service, by a proxy you run, or by a gateway somebody else operates. The right one depends on where that information lives.

Be precise about what routing is for. It is not primarily a latency optimisation, nor a quality one. People reach for it for four reasons: an outage must be survivable, tasks deserve different price points, tenants need cost ceilings, and a pricing change should not require code changes.

What I compared

Control granularity — how finely you can express it
Failure and fallback behaviour — what happens when the model fails
Cost optimisation — whether the router can pick cheaper models
Observability — what you see about which model was chosen
Portability — how hard it is to leave
Operational burden — what you run and patch
Latency cost — the overhead it adds

The comparison table

Dimension	Hand-built router	LiteLLM Proxy	Hosted gateway
Where the decision lives	In your service code, next to the request context	In proxy configuration, applied to passing requests	In the vendor’s config or dashboard
Granularity of routing	Whatever your logic can express — task type, tenant, budget	Aliases, weighted deployments, fallbacks, per-key budgets	Per-model or per-endpoint rules, coarser
Fallback on provider error	Yours to implement, including retry and backoff	Configured fallbacks and retries, with health-based routing	Usually built in, on vendor health signals
Cost control	Full: you decide the price tier per call and tenant	Per-key and per-team spend limits, cost attribution per call	Spend dashboards and limits, by plan
Observability	Whatever you log yourself	Built-in logging, spend tracking, tracing integrations	Vendor dashboards, data held on their side
Operational burden	Yours, but only code you already own	A service to deploy, secure, upgrade, monitor	None — the vendor runs it
Best fit	You know the per-task model mapping and want control	Teams share models, budgets and failover	You want failover without running anything

Where a hand-built router wins

When the decision depends on request content. If you route on prompt difficulty, document type, tenant tier or what the last attempt returned, that context lives in your service. A proxy sees the request but not the reasoning, so the logic becomes crude rules or stays in your layer.
When you want zero extra infrastructure. A function returning a model name and provider costs nothing and adds no network hop. It also cannot itself be a single point of failure, which matters for a component every request needs.
When fallback behaviour needs to be unusual. Some fallbacks should retry, some degrade to a cheaper model, some should fail loudly so a user retries. A policy function says that more clearly than proxy config.

Where LiteLLM Proxy wins

When several teams share one set of credentials. One endpoint, one place for per-team budgets and rate limits, one place to look when someone asks what a department spent. Shared governance is the strongest argument for a proxy, and the one thing a hand-built router cannot solve.
When you want failover across providers without writing it. Fallback lists, retry counts, backoff and health-aware routing are configuration, not code. The reasoning about which provider to try next is already written and tested by people who have run it for years.
When cost attribution is the actual problem. Per-key spend tracking and budget limits without instrumenting every call site is a real saving, and the kind of work that never gets prioritised otherwise.

Where hosted gateways win

When you want failover without operating anything. If the requirement is “the pipeline should not stop when one provider has a bad hour,” and you would rather not run a service, a managed gateway is the shortest path, and availability becomes their problem.
When you want spend visibility across an organisation. A dashboard of spend by team, key and model is available on day one with no instrumentation. For teams under finance or compliance review, existing reporting removes an argument that takes weeks.
When your usage is small and varied. A handful of calls across a few providers is not worth a deployment. Hosted gateways price on volume, and at low volume the marginal cost rounds away.

The tiebreaker

If you route on request content or your rules are still being tested, build it in code — a function returning a model name, with an evaluation set checking the decision. If several teams need shared credentials, budgets and failover, run LiteLLM Proxy and keep your application on an OpenAI-compatible endpoint. If you want failover without running it, take the hosted gateway, but read what happens to your prompts in its logs.

The condition that overrides everything: if you call one model, from one place, with a stable prompt, route nothing. Add a second model only for a reason — a fallback target, a cost tier that pays for itself, a tenant ceiling — and remember each added model multiplies the evaluation surface. The evaluation harness tells you whether a cheaper model is cheaper once retries are counted.

What broke along the way

Our first router optimised for the wrong thing. We routed on prompt length, then found long prompts were not the expensive ones — the expensive ones triggered a rerun after a quality check. The rule was correct and beside the point. Any routing decision needs validating against measurement.

A fallback hid a real outage for two days. The proxy failed over to the secondary model, the pipeline kept running, so nothing alerted. Answers were slightly worse and nobody noticed. A fallback must be visible as one, meaning the tracer records which model served the request — the discipline in LLM observability.

Routing rules were never tested against the failure they existed for. We had a fallback for provider errors and no test that forced one. The first rate-limit event exposed a bug in the fallback path: the retry dropped the structured output format. Forcing a failover in a test environment is the only way to find that.

Pros and cons

What it gets you

Provider outages and deprecations stop being production incidents
A per-task price tier, which usually pays for the routing layer by itself
Spend attribution and per-tenant ceilings without instrumenting calls
One place to change the model mapping as vendors move

What it costs

A component every request depends on, which can fail or lag
Extra latency from the hop, plus cold routing caches
A config surface that can disagree with the app and drift

Who this is for

This guide is for a team already calling more than one model, or about to. With one model and one prompt, the shortest path to a better outcome is a better prompt and smaller context, not infrastructure.

If routing feels necessary, work out which of the four reasons applies first: resilience, price tiering, tenant ceilings or vendor independence. The reason determines which approach fits, and is worth an hour of thought before an afternoon of config.

FAQ

Q: Do I need an LLM router at all?

A: Probably not yet. If you call one model from one place with a stable prompt, routing adds a component that can fail, plus latency, and buys nothing usable.

Q: What is the difference between a router and a proxy?

A: A proxy is a stable endpoint in front of your model calls; a router is the decision logic inside it. LiteLLM Proxy is both, and a hosted gateway is a proxy someone else runs.

Q: How do I pick a fallback model without hurting quality?

A: Decide the order by capability tier, not by price, and treat a fallback as a degraded mode rather than an equivalent substitute. Keep the fallback able to follow your output schema, and verify that path on a schedule.

Q: Does routing help with latency?

A: Only indirectly. Routing can improve tail latency by steering away from a model that is currently slow, but it adds a hop of its own and usually a cold routing cache to maintain.