LLMOps · 9 min read
Designing an LLM gateway for a large organisation
When dozens of teams call AI models directly, you lose control of cost, security and quality. A central LLM gateway brings it back. Here's what a good one does.
In most large organisations, AI adoption starts bottom-up. Teams get their own API keys, call their favourite models directly, and ship. It’s fast — until security asks who is sending customer data where, finance asks why the bill tripled, and nobody can answer either question.
An LLM gateway is a single service that sits between your applications and every model provider. Here’s what a good one does.
One interface, many models
Applications call the gateway with a consistent API; the gateway translates to each provider. Switching or adding a model becomes a configuration change, not a code change.
Identity and policy
Every request carries the identity of the calling application and, ideally, the end user. The gateway enforces policy centrally: which teams can use which models, which data classifications are allowed where, and which requests must be blocked or redacted.
Cost attribution and budgets
Because every request passes through one place, the gateway can tag usage by team, application and feature, enforce budgets and rate limits, and feed a cost dashboard. This is the foundation of AI FinOps.
Reliability
Providers have outages and rate limits. A gateway can retry, fail over to a backup model, and queue or shed load gracefully, so individual applications don’t each have to solve this.
Observability
Log requests, latency, token counts and errors centrally — with care for sensitive content. Combined with evaluation results, this gives you a real picture of how AI is performing across the organisation.
Start small
You don’t need every feature on day one. Start with a single interface, identity on every request, and cost attribution. Those three alone give you control you didn’t have before — and a platform to add policy, routing and caching as you grow.