The term "AI gateway" is relatively new and underspecified. Different vendors mean very different things by it. Some mean LLM request routing and cost management. Some mean prompt injection detection. Some mean output filtering. Almost none mean all of these — and none mean "replaces your API security."
What an API Gateway Does
A traditional API gateway handles the protocol layer: routing requests to the right backend, enforcing rate limits, validating JWT tokens, managing API keys, handling load balancing and circuit breaking. It is infrastructure — it operates at the request level without semantic understanding of the content.
What an AI Gateway Does
An AI gateway operates at the semantic layer: it understands that a request is going to an LLM and has specific concerns about LLM interactions — cost per token, which model to route to, whether the input contains a prompt injection attempt, whether the output matches a declared schema, whether PII should be redacted before the request reaches the model.
The Overlap and the Gap
Both need rate limiting — but API rate limiting is per-key-per-endpoint, while AI rate limiting is per-session-per-model with token budget tracking. Both need logging — but API logs capture HTTP metadata, while AI gateway logs need to capture model version, token count, and prompt context. Both need authentication — but AI gateways also need to scope which models a given key can access and with what constraints.
The gap is in semantic validation. An API gateway does not know that "Ignore all previous instructions" is a different kind of input than "Get me all orders for customer 123." An AI gateway does.
G8KEPR's four-pillar architecture addresses this directly: API Security (protocol layer), MCP Security (tool call layer), AI Gateway (semantic layer), and Verification Engine (output validation layer). Each layer is independent and has distinct concerns.