What Is an AI Gateway?

How it works

Without a gateway, each app connects straight to a model provider’s API (the connection point that lets software send requests to its models) using its own key. With a gateway, apps send their requests to the gateway instead. It checks who is asking, applies the company’s rules, forwards the request to the right model, and records what happened.

Features vary by product, but most gateways offer some mix of these controls:

• Access control: one place to issue and revoke keys, so provider keys aren’t copied into every app.
• Usage and cost tracking: logs of usage by team, app, or user (often counted in tokens, the small chunks of text providers bill by), with estimated costs, budgets, and alerts. Estimates can differ from provider invoices and don’t cover spending outside the gateway, such as per-seat subscriptions.
• Rate limits: caps on the requests or tokens an app or person can use in a set period.
• Data screening: rules that try to detect, mask, or block sensitive details, such as card numbers, before they leave. Filters can miss things, so they reduce exposure rather than remove it.
• Routing and fallback: sending requests to different models, or to a backup when one is down.

Some gateways also reuse stored answers for repeated or similar requests, which may cut response time and cost but only suits questions that should always get the same answer.

Why companies add one

AI use rarely starts in one place. A few teams sign up for different tools, developers add models to internal apps, and soon there are keys in many systems, bills from several vendors, and no shared record of what data went where. A gateway turns those scattered connections into one managed path.

That helps answer the questions leaders and security teams ask. Who is using which models? What are we spending, and on what? Can we show what was sent? Can we change providers without breaking things? With usage logged in one place, you can compare costs across models and set limits before a bill surprises you. And because many gateways offer one interface across providers, switching models can be a settings change rather than a rewrite. The new model still needs testing, since its answers and features can differ.

A gateway also gives people an approved route to AI, which can reduce shadow AI. It cannot see tools people use outside it, such as a personal chatbot account in a browser. And it controls what leaves your side, not what a provider does with the data afterward. That is set by the provider’s terms, such as a zero data retention agreement.

AI gateway vs. guardrails and orchestration

These terms often show up together, but they do different jobs.

• AI guardrails are the checks themselves, such as blocking personal data or filtering harmful output. A gateway is one convenient place to apply some of them. Others live inside the app, the prompt, or the model provider’s own settings.
• AI orchestration coordinates the steps of a task: which model or tool runs when, and how results pass between them. A gateway does not plan the work. It manages and governs the requests once they are made.
• A model provider’s API is the connection point it offers for its models. A gateway sits in front of one or more of these APIs, usually offers its own API to your apps, and adds company-wide rules on top.

Some products blend these roles, for example an orchestration platform with gateway-style cost controls built in. When comparing tools, ask which job each one is actually doing for you.

Do you need one? Buying vs. building

A small team using a single AI assistant usually does not need a gateway. Gateways manage requests that apps send to models through APIs, so they usually can’t sit in front of a vendor’s own chat app anyway. For one assistant, that tool’s admin settings are where you control user access and see basic usage reporting.

A gateway starts to make sense when several apps call AI models through APIs, when you use more than one provider, when costs are hard to assign to teams, or when you need a reliable record of what was sent for security or compliance reasons.

Buy or build? Adopting an existing gateway, whether a paid managed service or an open-source one your team runs, is the faster path and comes with ready-made features. With a self-hosted open-source gateway, your team still runs, updates, and secures it; a managed service shifts most of that work to the vendor. Some cloud and AI platforms include gateway features too. Building your own is possible, since at its core a gateway forwards requests, but your team then owns every feature, plus maintenance, security, and keeping up with each provider’s changes.

Either way, every request now depends on the gateway, so it needs to be reliable. If a vendor runs it, check where it runs and what it stores, because it will see your AI traffic.

Further reading

• AI ImplementationCovers connecting systems and deploying AI across a business, where a decision like adding a gateway tends to come up.
• AI Fluency AssessmentA free way to see where your business stands on AI adoption, useful context before deciding on central controls like a gateway.