AI Gateway vs API Gateway: Which Secures Enterprise Data Better?

API gateways and AI gateways serve complementary roles in enterprise AI architectures: API gateways manage traditional API traffic through authentication, authorization, routing, transport security, and request-based rate limiting, while AI gateways govern interactions between applications and language models by inspecting prompts and responses, enforcing token budgets, filtering sensitive content, routing among models, and supporting streaming workloads. Because API gateways are generally not designed to interpret unstructured prompt content, they cannot directly address risks such as prompt injection, PII exposure, or unsafe model outputs, whereas AI gateways provide content-level controls for these concerns. The proposed layered approach places an API gateway at the network perimeter and an AI gateway between applications and model providers, with separate policies and logging for identity and content governance. Adoption is presented as most relevant for production-scale or regulated AI deployments with high call volumes, sensitive data, compliance obligations, or substantial token spending, while simpler prototypes may rely on conventional API controls. CData Connect AI is positioned as an additional managed MCP-based layer for giving models governed, real-time access to enterprise data through a secure connection.

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