# Why LowRouter exists

LLM inference is now a default building block. Most teams that ship
features on top of it end up writing the same gateway twice: once to
abstract the provider, again to track usage and bills. That gateway is
load-bearing — it sees every prompt and every response — but it is
rarely treated as a product. It is glue.

LowRouter is that gateway as a product, with two opinions baked in.

## Opinion one: the footprint of a request is part of its cost

Most billing dashboards show tokens and dollars. LowRouter also reports
the energy a request consumed and the grams of CO₂e the inference is
estimated to have produced. Both numbers are estimates — see
[methodology](../sustainable-ai/methodology) for the formula and its
limits — but having them visible changes how the request is thought
about. A request with a known carbon number is a request a developer can
actually choose differently.

We do not claim that every request is "green" or that the estimate is
exact. We claim it exists, that the formula is documented, and that the
inputs are auditable.

## Opinion two: routing should be explicit and sovereign

When you pick a model in most gateways, you pick a *brand*. The brand
hides who actually serves the tokens — which provider, which region,
which hardware tier. That hiding is convenient until something
matters: a region requires data residency, a provider has an outage, a
contract requires a specific operator.

LowRouter exposes the route. Every response says which provider served
the request and from which region, and the dashboard lets operators
choose policies (prefer-region, prefer-low-carbon, prefer-cheapest,
fixed-provider) that map to those constraints. The default is
`lowrouter/auto`; the override is always one field away.

## What LowRouter is not

It is not an inference engine. The actual work happens at OpenAI,
Anthropic, Mistral, and other providers. We forward, we measure, we
account.

It is not a benchmarking tool. The dashboard does not rank models on
quality. We expose what we can measure faithfully — usage, latency,
energy, carbon — and leave subjective judgements to you.

It is not a free service. The credits model is documented in
[credits and billing](../guides/credits-and-billing). When the costs of
running this kind of infrastructure are made invisible, the
sustainability story becomes hollow; we'd rather charge what running it
actually costs.

## Who it's for

- **Developers** who want one endpoint and one bill across multiple
  providers, plus enough metadata to debug and improve their app.
- **Operators** who need data residency, audit trails, and a clear
  picture of which provider served what.
- **Sustainability and compliance teams** who want a defensible number
  for the AI footprint of their organisation, not a marketing pledge.

If that is not you, that is fine. The dashboard and these docs are
public for a reason — read what we measure and how, and decide whether
the trade-offs fit.