AI Gateways: One Front Door for Every LLM You Run

The first AI feature you ship talks to one model with one API key. It feels simple. Eighteen months later you are calling one provider for chat, another for cheap classification, a third for vision, and a self-hosted model for anything sensitive. The keys are scattered across services, nobody can say what this month's token bill actually pays for, and when one provider has an outage at 9am your product goes down with it. This is the mess an AI gateway is built to clean up.

It is common enough now to have a name and a market. Roughly 37% of enterprises already run five or more models in production at once, and the LLM gateway market hit about $2.76 billion in 2026 on its way to a projected $7.2 billion by 2030. If you have shipped more than one AI feature, you are heading toward this problem whether you have named it yet or not. Here is what a gateway is, what it buys you, and when it is worth adding.

What an AI gateway actually is

An AI gateway is a single service that sits between your applications and every model you use. Instead of each app holding its own provider keys and its own retry logic, they all call the gateway, and the gateway decides where the request really goes.

Think of it as a reverse proxy, but for models instead of web servers. One endpoint in front, many providers behind it, and a control point in the middle where you can enforce policy, watch spend, and swap providers without redeploying a single app. Your code stops caring whether a request lands at Anthropic, OpenAI, Google, or a model running on your own infrastructure.

The four problems it solves

Gateways earn their place by fixing four things that get painful at scale.

• Failover and routing. If one provider is down or slow, the gateway reroutes to another automatically. No single provider can take your product offline.
• Cost control. Every call is metered in one place. You can set per-team budgets, route cheap tasks to cheaper models, and cache repeated requests. Semantic caching alone can cut token spend by around 40%.
• Security and governance. Provider keys live in the gateway, not in a dozen services. You get one place to enforce rate limits, redact sensitive data, and log who called what.
• Observability. Latency, error rates, and token usage per feature, per model, in one dashboard, instead of stitched together from four provider consoles.

Gateway, not just a wrapper

It is tempting to think a thin internal library that picks a provider does the same job. It does not, and the gap shows up under load.

A library lives inside each app, so every app has to be redeployed to change routing, and each carries its own copy of the keys and the retry logic. A gateway is a shared service. Change a routing rule once and every app follows it immediately. Rotate a key once and you are done. The difference is the same as the one between copying config into every service and having a single control plane, and it is exactly where teams get burned when they postpone it.

There is a cost angle too. Centralising calls is what makes real AI cost optimization possible, because you cannot cut spend you cannot see, and per-app wrappers hide it.

When it is worth it (and when it is not)

Do not build a gateway for your first AI feature. One model, one app, one key is genuinely simpler, and a gateway would be premature infrastructure.

Add one when a second or third model shows up, when more than one team is calling models, or the moment a provider outage or a surprise bill actually hurts. At that point the gateway stops being overhead and starts being the thing that keeps AI spend predictable and the product online. It also pairs naturally with the security controls every AI agent needs, since a single choke point is far easier to secure than scattered direct calls.

You can adopt an open-source or managed gateway, or run a lean one yourself. The right answer depends on your stack, your data-residency rules, and how much routing logic you actually need. If you are past one model and the sprawl is starting to bite, we can help you put the right front door in front of your models before the bill or the next outage forces the issue.