DevOps Dictionary

AI Gateway is a centralized entry point that sits between applications and one or more AI model endpoints, providing a consistent way to route and control model requests. It solves the operational problems that arise when teams integrate models ad hoc: scattered credentials, inconsistent access rules, uneven logging, and hard-to-predict latency and cost. At a high level, an AI Gateway authenticates and authorizes callers, enforces policies such as rate limits and quotas, routes requests to the right provider, model, or version, and captures telemetry like prompts, responses, errors, and token usage for monitoring and audit.

With an AI Gateway, platform teams can standardize governance and observability across AI features; without it, integrations sprawl and it becomes easier to leak data, trigger outages, or rack up runaway spend without clear traceability. This gap exists because model providers and internal services expose different interfaces and controls, and the gateway normalizes them into a single enforceable layer.