What does an LLM gateway in front of providers centralize?

When several services each call OpenAI or Anthropic directly, they all end up reimplementing the same plumbing: storing API keys, retrying on errors, logging cost, enforcing rate limits. That logic drifts apart and nobody has a single view of spend or traffic. An **LLM gateway** is a proxy that sits between your services and the providers. Every call goes through it. Now the keys live in one place, routing and failover happen in one place, and every request is logged and costed consistently. The payoff is org-wide consistency. You can rotate a key, add a new provider, or change a policy once instead of in ten codebases. You get a single dashboard for cost and latency across every team. The cost is one extra network hop, and the gateway itself must be highly available — if it goes down, every LLM call goes down with it.

A gateway turns a set of cross-cutting concerns that every caller shares into a single shared component, so services don't each reinvent them. It centralizes several concerns. **API-key and credential management** keeps keys in the gateway, not scattered across repos. It also handles provider routing and failover, rate limiting and per-team quotas, and caching (prefix and semantic). On top of that it does cost accounting with budgets, request/response logging and tracing, and guardrails like PII redaction or output filtering. Services call one internal interface and stop carrying that logic themselves. The win is consistency and control at the org level. There's one place to add a provider, rotate a key, attribute cost per team, or enforce a new policy — instead of logic that drifts across ten services. Observability is uniform because every call is traced through the same component. The tradeoff is real. You add a network hop, and you create a component that every LLM call now depends on. If the gateway isn't fast and highly available, it becomes a single point of failure for your entire LLM surface. So it must be horizontally scaled, have its own fallbacks, and ideally degrade gracefully rather than hard-fail.