An LLM gateway that gives you routing, cost control and policy in one place
Stop scattering API keys and AI spend across teams. bluebill puts every model request behind a governed gateway with routing, caching, logging, budgets and enforced usage policy.
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Per-team cost attribution
Every request tagged to a team, application and feature, so AI spend has an owner before the invoice arrives.
Multi-provider routing
Route by cost, latency or capability across hosted and self-hosted models, with automatic fallback.
Full request observability
Model, tokens, latency, cost and policy decisions logged for every call in one audit trail.
What is an LLM gateway?
An LLM gateway is the single ingress and egress point between your applications and every large language model you use. Rather than embedding provider SDKs and API keys in each service, applications call the gateway, which authenticates the caller, applies policy, selects a model, forwards the request and records what happened.
That one indirection unlocks the things direct API calls make impossible: switching providers without a code change, enforcing the same safety and data rules everywhere, capping spend per team, caching repeated work, and answering 'which feature spent this money' with data instead of guesses.
A gateway is also where AI governance becomes enforceable. Policies written in a document are advisory; policies applied at the gateway are the only ones that actually hold at runtime.
What runs at the gateway layer
Access, routing, cost and policy handled once, centrally, instead of reimplemented in every service.
Centralized key and access control
Provider credentials held in one place with scoped, revocable access per team, service and environment.
Model routing and fallback
Send each request to the right model by cost, latency or capability, and fail over automatically when a provider degrades.
Budgets and cost attribution
Token spend broken down by team, application and feature, with soft alerts and hard limits before overruns compound.
Observability and audit logging
Every call recorded with model, tokens, latency, cost and policy outcome — the evidence base for audits and reviews.
Policy and guardrail enforcement
PII redaction, blocked categories and data-residency rules applied at the gateway so no service can bypass them.
Caching and prompt efficiency
Exact-match and semantic caching plus context trimming to cut repeated token spend without changing product behavior.
You need an LLM gateway when
- Provider API keys live in more than one repository, secret store or personal account.
- Nobody can say which product feature is responsible for last month's AI spend.
- Each team implements its own retries, redaction and safety filtering differently.
- Switching or adding a model provider would require changing multiple codebases.
- There is no single log of AI requests to hand an auditor or security reviewer.
- AI cost is growing monthly and the only control available is asking teams to be careful.
LLM gateway questions, answered
How gateways, proxies and routers differ, what they log, and how they reduce AI cost and risk.
Keep reading
AI Guardrails
The policy layer the gateway enforces: input, output, tool-use and data guardrails.
AI Governance
The full bluebill product for AI oversight, cost control and compliance readiness.
LLM gateway vs. AI proxy
Where a forwarding proxy stops and a governed gateway starts, criterion by criterion.