API gateway vs. AI gateway: what IAM teams need to know

TL;DR: Traditional API gateways handle routing, auth, and microservice traffic well, but they do not count tokens, manage streaming responses, or enforce content-level controls for LLM workloads, according to Kong. AI gateways shift governance closer to the workload, where cost, security, and policy enforcement now depend on AI-specific telemetry and controls.

NHIMG editorial — based on content published by Kong: API Gateway vs. AI Gateway: The Definitive Guide to Modern AI Infrastructure

By the numbers:

• Organizations will develop 80% of GenAI business applications on existing data management platforms by 2028, reducing complexity and delivery time by 50%.
• The OWASP ranked prompt injection as the top security risk in its 2025 OWASP Top 10 for LLM Applications report.

Questions worth separating out

Q: How should security teams govern AI workloads that use both API and AI gateways?

A: Treat the API gateway as the transport control and the AI gateway as the inference control.

Q: Why do traditional API gateways fall short for LLM and agentic AI traffic?

A: They were built for request-response traffic, not for token streams, semantic reuse, or content-aware policy enforcement.

Q: What do security teams get wrong about AI gateway security?

A: They often focus on model access and ignore the governance of the data and outputs moving through the gateway.

Practitioner guidance

• Define the control boundary for AI inference Map where API routing ends and inference governance begins, then assign ownership for model access, token policy, and output inspection to a named team.
• Instrument token usage by identity and workload Track tokens consumed by user, service account, application, and model so budget enforcement and abuse detection can operate at the right granularity.
• Test streaming and content controls separately Validate SSE and WebSocket handling, then run prompt injection and PII leakage tests to confirm the gateway can inspect meaning, not just transport.

What's in the full article

Kong's full blog covers the operational detail this post intentionally leaves for the source:

• Feature-by-feature breakdown of token-level routing, semantic caching, and streaming support for LLM traffic
• Implementation guidance for content-aware security controls and model routing decisions
• Cost and performance examples that help teams estimate the impact of AI gateway adoption
• Architectural comparison points for teams deciding where API gateway policy should stop and AI gateway policy should begin

👉 Read Kong's guide to API gateway and AI gateway design for modern AI infrastructure →

API gateway vs. AI gateway: what IAM teams need to know?

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