The short answer
An API gateway manages traffic between services. It handles routing, authentication, rate limits, and versioning for your APIs. An AI gateway sits in front of your models and adds the controls that model traffic needs and a normal API does not: prompt and output inspection, token cost tracking per team and per use case, model routing across providers, and an audit record of what the model was asked and what it returned. If you already run an API gateway, you still need a control point at the place where a request reaches a model. The two solve different problems, and one does not replace the other.
Why an API gateway alone leaves gaps
Route an LLM call through your existing API gateway and you get authentication and a rate limit. You do not get a record of the prompt, a way to catch a response that leaked customer data, or a per-use-case cost line your finance team can read. Those are the risks that make AI different from a normal service call. A team that bolts AI onto the API gateway it already owns has covered the networking and left the AI-specific exposure unmanaged. That gap is where a stalled pilot turns into an incident no one can explain after the fact.
What an AI gateway adds
Think of an AI gateway as the control point for production AI: one place to see which models are being called, by whom, at what cost, and with what result. It is where a regulated team can answer the questions an auditor or a board will ask. Under the EU AI Act, Article 50 transparency obligations and record-keeping expectations mean you need to show what a system did, not just that it was authenticated. A control point that keeps that record is the difference between proving what you run and hoping no one asks.
When you need which
You need an API gateway the moment you expose services to each other or the outside world. You need an AI gateway once AI calls carry real business value or touch regulated data, which for most enterprises is the point a pilot tries to reach production. Run both. Keep the API gateway for service traffic and put an AI-specific control layer where models are called, so cost, prompts, output, and an audit trail are visible from day one rather than reconstructed after something goes wrong.
Frequently asked questions
Is an AI gateway just an API gateway with extra features?
No. They overlap on routing and authentication, but an AI gateway adds prompt and output visibility, per-use-case cost tracking, model routing across providers, and an audit record built for AI traffic. Those controls do not exist in a standard API gateway.
Can I use my existing API gateway for AI traffic?
You can route the calls through it, but you will be missing the AI-specific controls: no prompt or output record, no cost-per-use-case view, no audit trail an auditor would accept. Most teams add an AI control point rather than stretch the API gateway to cover it.
Do I need an AI gateway for a pilot?
For a throwaway pilot, no. The moment the use case carries business value or touches regulated data, the controls an AI gateway provides are what let it move to production safely, so it pays to put them in place before you scale rather than after.