What an AI Gateway Actually Does: Routing, Security, and Data Sovereignty
An AI gateway sits between an application and its model providers, processing requests through five stages: authentication, routing, security inspection, the upstream model call, and logging. Beyond basic proxying, a well-designed gateway handles load balancing, fallback chains, and cost-based routing to manage provider failures and reduce LLM spending without changes to application code. Security inspection at the gateway level enables prompt injection detection, PII scanning, and policy enforcement consistently across all services. A critical architectural choice is whether to use a single-process design, where traffic transits the vendor's cloud, or a split-plane design that keeps prompts within the organisation's own infrastructure. The split-plane approach is increasingly relevant as regulations like the EU AI Act, taking effect from August 2026, impose stricter obligations on data handling for high-risk AI systems.
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