// compare
Functional AI vs an LLM router
Verdict
An LLM router picks a model for each request: a trained router model or task classifier predicts which candidate gives acceptable quality at the lowest cost, so easy prompts go to cheaper models. Functional AI makes that choice per version instead — every version, including one on a cheaper model, is scored against your own dataset before it ships, and at runtime it fails over to a backup model if the primary fails. Use a router when your prompts vary widely in difficulty; use Functional AI when you want every model change proven on your data before users see it.
Functional AI is in private beta. LLM router details reflect the published pages of RouteLLM, Not Diamond, OpenRouter Auto Router and NVIDIA LLM Router as of 2026-09; individual products differ, and corrections are welcome.
Choose Functional AI for
- Model changes proven on your own dataset before they ship, not predicted per request
- Predictable behaviour: a version answers with its model, plus a backup if that model fails
- Hosting the whole agent or workflow, not only choosing its model
- Rolling a model change back without redeploying your app
Choose an LLM router for
- Traffic of widely varying difficulty, where sending easy prompts to cheap models pays off
- A per-request cost/quality dial across many candidate models
- Open-source routers you run yourself (RouteLLM, NVIDIA's LLM Router blueprint), or custom routers trained on your data (Not Diamond)
- Adding routing to model calls your own code already makes
Side by side
| Feature | Functional AI | LLM router |
|---|---|---|
| What it is | Agentic Function Runtime — hosts and runs your prompt, agent, or workflow | A decision layer that picks a model for each request |
| When the model is chosen | Per version, before it ships | Per request, at runtime |
| How the choice is made | Scored against your dataset and quality threshold | Predicted by a trained router model or task classifier |
| Cost/quality control | Ship the cheaper model only if it passes your thresholds | A threshold or dial — e.g. a cost tier or cost/quality setting |
| Makes the model call | Yes — and runs the whole agent around it | Some do (OpenRouter Auto Router, RouteLLM's server); some only recommend (Not Diamond, NVIDIA v2) |
| Fallback when a model fails | Yes — automatic, to a backup model | OpenRouter Auto Router falls back; recommend-only routers leave it to you or your gateway |
| Hosts your agent | Yes — prompts, agents, and multi-agent workflows | Not advertised — routing is one decision per call |
| Stage and pricing | Private beta; meters steps, model tokens at cost | Open source (RouteLLM, NVIDIA), usage-based (Not Diamond), or the chosen model's rate (OpenRouter) |
At a glance
- An LLM router chooses a model for each request at runtime; Functional AI chooses the model for each version, using evals on your dataset, before that version ships.
- Routers predict quality with a trained router model or task classifier; Functional AI measures it against your own quality thresholds.
- Both can cut cost by using cheaper models where quality holds — per request for a router, per version for Functional AI.
- Several routers only recommend a model and leave the call, and any fallback, to your code or gateway; Functional AI makes the call and fails over automatically.
- RouteLLM and NVIDIA's LLM Router blueprint are Apache-2.0 open source you run yourself; Not Diamond and OpenRouter's Auto Router are hosted.
- The routers here can be used today; Functional AI is in private beta.
Honest pros and cons
Functional AI pros
- Every model change is proven on your data before users see it
- One model per version is easier to reason about, debug, and roll back
- Fallback built in, with no gateway to wire up
- Hosts the agent too — model choice is one decision inside it
Functional AI cons
- Private beta — access is via the waitlist or the design-partner program
- No per-request routing: easy and hard prompts both go to the version's model
- Model choice is only as good as your eval dataset, and you have to build one
- No published customer stories or benchmarks yet
LLM router pros
- Per-request savings on traffic of mixed difficulty
- A cost/quality trade-off you can tune per call
- Open-source options you can run and inspect yourself
- Fits existing code: recommend-then-call, or an OpenAI-compatible endpoint
FAQ
- What is an LLM router?
- An LLM router decides which model should answer each request. It uses a trained router model or a task classifier to predict which candidate will give acceptable quality at the lowest cost, so simple prompts go to cheaper models and hard ones to stronger models. Some routers also make the call; others return a recommendation for your code or gateway to execute.
- What is the difference between an LLM router and an AI gateway?
- A router decides which model to use; a gateway gives you access to models and carries the call — one API, fallbacks, budgets, logging. Router vendors describe them as complementary: the router's choice is often executed through a gateway. Functional AI covers a different layer again — it hosts the agent that makes the calls, chooses its model per version through evals, and fails over at runtime. See Functional AI vs an LLM gateway for the gateway side.
- Which LLM router is best?
- It depends on where you want the decision to live. RouteLLM and NVIDIA's LLM Router blueprint are open source and run in your own infrastructure; Not Diamond is a hosted router that can be trained on your own data; OpenRouter's Auto Router picks a model inside OpenRouter's API. If your traffic does not vary much in difficulty, you may not need per-request routing at all — one model per version, chosen by evaluation and backed by automatic fallback, is simpler to run and debug.
- Does Functional AI route between models?
- Not per request. Each version of an Agentic Function runs on the model chosen for it, and that choice is scored against your dataset before it ships — including a switch to a cheaper model. At runtime, Functional AI monitors every call and falls back to a backup model if the primary fails. Per-request routing by prompt difficulty is what a dedicated router adds.
- Can an LLM router reduce costs?
- That is its main purpose: sending simpler prompts to cheaper models. Router vendors publish their own savings figures, measured on their own benchmarks, so test on your own traffic before relying on one. The saving depends on how much your prompts vary in difficulty — on uniform traffic, a per-request decision has less to exploit.
Evaluating options? Join the Functional AI waitlist or become a design partner — design partners shape the roadmap and get early access.