Use cases

Why put a gateway in front of your AI?

Every request in one place is what makes spend controllable, attributable, and portable. Here's what that looks like in practice.

01

Stop runaway spend before it happens

For platform and infrastructure teams

An agent stuck in a retry loop, a prompt that ballooned past its context budget, a side project that quietly became your biggest OpenAI line item — AI spend fails silently, and the invoice arrives weeks later.

A gateway is the only place spend can actually be stopped. TokenRouter enforces daily and monthly budgets per team, member, and API key, with hard caps that block requests the moment a budget is exhausted — not an alert after the fact.

02

One endpoint for every provider

For engineering organizations

Thirteen providers means thirteen dashboards, thirteen key rotations, and thirteen billing relationships scattered across the company. Consolidating them behind one endpoint turns that sprawl into a single integration.

Point your OpenAI or Anthropic SDK at TokenRouter, add provider keys once, and every app in the org can reach any model through one tr_ key — with the same request shape it already uses.

03

Coding agents without shared keys

For engineering managers

Claude Code, Codex, and opencode are the fastest-growing AI line items at most companies — and the easiest to lose track of when everyone pastes the same provider key into their environment.

With TokenRouter every developer gets their own key with its own rate limits and budget. Point ANTHROPIC_BASE_URL at the gateway and each session is metered, attributed, and capped individually.

04

Cost attribution that matches your org chart

For finance and FinOps

Provider invoices tell you what the company spent, not who spent it. Chargeback from a flat bill means spreadsheets and guesswork.

TokenRouter maps organizations, teams, and members onto your real structure. One key per app makes attribution automatic, and CSV exports drop straight into your existing cost reporting.

05

Swap models without a migration

For product teams

The best model for a task changes every quarter. Hard-coding one provider's SDK means every improvement elsewhere is a migration project.

Behind a gateway, a model change is a string change — or no change at all with model="auto", which routes each request by cost, latency, and quality. Your integration stays put while the model market moves.

06

Rate limits for internal tools

For internal platform teams

Internal AI tools rarely get the same guardrails as production services — until one of them floods a provider and takes everyone else's quota with it.

Per-key RPM and TPM limits at the gateway keep any single app inside its lane, and plan-level quotas keep the whole org inside the number you agreed to spend.

One base URL away

Swap the base URL in your existing SDK, invite your teams, and set the first budget in under five minutes.