What is an AI gateway?
An AI gateway is a service that all requests to AI models pass through on their way to the model. Applications call the gateway instead of the model endpoint. The gateway checks who is calling, applies the rules for that user or system, forwards the request to the right model, and records what happened. It presents one consistent interface, usually the OpenAI-compatible API, regardless of whether the model behind it is a hosted service, a self-hosted inference server, or several of each.
Typical gateway functions include authentication tied to single sign-on, role-based access to specific models and tools, rate limiting and spending caps per team, routing by task or by cost, retries and failover between endpoints, prompt and response logging for audit, PII redaction before a prompt leaves the network, and collection of latency and token metrics for observability.
Why an AI gateway matters
Without a gateway, each application holds its own API keys, each team’s usage is invisible until the invoice arrives, and no one can say which prompts contained customer data. A gateway puts identity on every request and makes usage, cost, and content reviewable in one place. It also makes the models interchangeable: when a better open-weight model arrives or a vendor changes terms, the switch happens in the gateway’s routing table and applications do not change. In regulated settings, the gateway’s log is often the first artifact an auditor asks for.
AI gateway in practice
A managed service provider runs AI features for forty client companies. A gateway authenticates each client’s users through the client’s own identity provider, tags every request with the client and user, enforces a monthly token allowance per client, blocks prompts containing card numbers, and routes requests to a shared self-hosted model or to a dedicated instance for clients that require isolation. The log feeds both billing and each client’s compliance reports. In Kirk Tech Solutions engagements, a gateway with identity, access control, and audit logging is treated as a baseline component of any deployment.
What to watch for
- Decide what the gateway logs and for how long, because full prompts are useful for audit but are themselves sensitive data.
- The gateway becomes a single point of failure, so run it redundantly and monitor it as production infrastructure.
- Check that streaming, tool calling, and long requests pass through without being buffered or truncated.
- Keep policy in the gateway and out of individual applications, or the controls will drift apart.