What is an AI gateway?

An AI gateway is the software layer placed between enterprise applications and language models, centralizing every call instead of letting each team make its own. It routes requests across multiple providers (OpenAI, Anthropic, Google, self-hosted open source models), manages API keys in one place instead of scattering them across dozens of repositories, applies rate limiting and quotas per team or project, tracks costs per cost center, caches recurring responses to save tokens, filters input and output to block prompt injection or sensitive data leaving the company, and automatically falls back to an alternative provider when the primary one is unreachable or erroring. It is not a single named product: it is a category with different implementations, from existing API gateways extended for this purpose (Kong AI Gateway, Apigee) to platforms built specifically for it (Portkey, Cloudflare AI Gateway, LiteLLM, Helicone). The common trait is always the same: one single point through which all traffic to models passes, instead of direct connections scattered between applications and providers.

Why it emerged as its own layer

As long as a company has one team calling one model, a direct API call works fine. The problem appears once the teams integrating LLMs grow to five, ten, twenty: each picks its own provider, manages its own keys, has no visibility into what the team next door is spending, and no way to know whether another group already solved the same reliability problem. The AI gateway answers exactly this fragmentation: it becomes the technical boundary beyond which no application talks directly to an external provider, and everything needed to govern that use, from keys to spend limits, lives in one place instead of being replicated in every team.

Enterprise example

An industrial group with ten divisions let each one integrate LLMs on its own: some went directly to OpenAI, others used Anthropic through developers' personal keys, with no central visibility into overall spend and no control over what data was being sent to the models. Placing an AI gateway in front of all integrations gives IT a single point from which to see spend by division, revoke a compromised key without touching application code, enforce a filter that blocks personal data from leaving in prompts, and trigger automatic fallback to a second provider when the first times out, without any application needing to change.

Why it matters for decision makers

Without an AI gateway, governing LLM use across a company remains a promise on paper: each team integrates its own way, costs surface only on the monthly bill, and a prompt security incident found by one team rarely becomes a lesson for the others. The AI gateway is the layer that makes that governance enforceable rather than declared, and it is also the most direct technical check against shadow AI: if the gateway is the only path to reach a model, unsanctioned use stops being invisible.