AI Gateway Architecture Explained: How LLM Routing Works

An AI gateway is a specialized reverse proxy for LLM traffic that authenticates callers, selects models, inspects prompts and responses for security risks, makes upstream calls, and records detailed operational logs. Its routing engine can balance requests across model instances, use fallback providers during failures, and direct simpler workloads to lower-cost models based on configurable rules for cost, latency, capability, and availability. The text emphasizes that observability, cost attribution, PII detection, prompt-injection protection, and auditability should be built into this control layer before AI systems reach production. It contrasts centralized SaaS, sidecar, and split-plane deployments, arguing that split-plane designs separate policy management from request processing so that traffic remains within a customer’s infrastructure while centrally managed policies are enforced locally. TrustGate, NeuralTrust’s open-source gateway, is presented as an example of this model, using a Go-based data plane deployed in customer environments and a control plane that distributes policy updates without handling LLM traffic.

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