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 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. 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.