LLM Proxy vs API Gateway: Why a Generic Gateway Cannot See Model Traffic
A generic API gateway routes requests, checks a key, and counts calls, which is why teams reach for one in front of their LLM traffic. It stops short of the decisions AI traffic needs: who the caller is at a user grain, what the prompt contains, and a record of the AI decision. This piece compares the two on what each can see and enforce, and where the boundary between them sits.
A generic API gateway routes a request to a backend, validates an API key, applies a rate limit, and records that a call happened. Those are useful functions, which is why the first instinct when LLM traffic needs control is to put the API gateway you already run in front of it. The gateway does its job: the model endpoint is reachable, the key is checked, the call is counted. What it records is that a request went to a model host and returned. Who the individual caller was, what the prompt contained, and what the model was asked to do stay invisible to it.
I want to compare an LLM proxy and an API gateway on the specific axis that matters for AI traffic, which is not routing or throughput but what each layer can see and decide about the content of a model call.
TL;DR
An API gateway governs requests by their envelope: host, path, key, and rate. An LLM proxy governs model calls by their content and identity: which user or agent is calling, what data the prompt carries, which policy applies, and a per-decision record of the outcome. Use the API gateway for transport concerns; use the LLM proxy for the AI-specific decisions the gateway cannot make because they live in the request body it does not read.
What an API gateway sees and enforces
An API gateway operates on the HTTP envelope. It knows the destination host and path, the API key or token presented, and the shape of the request enough to route and rate-limit it. It can block a caller that lacks a valid key, cap how many calls a key makes per minute, and send traffic to the right backend. For most service-to-service APIs that is the right level, because the meaningful unit is the request and the caller is a service identified by its key.
The limits show up the moment the payload carries meaning the gateway is not built to read. The gateway treats the prompt as an opaque body. It cannot tell that one call carries a customer's health record and another asks for a weather summary, because distinguishing them requires reading and classifying the content, which is outside what a transport-layer gateway does. It also authenticates the key, not the person or agent behind it, so many users sharing one application key are one identity to the gateway.
What an LLM proxy adds
An LLM proxy reads the model call as an AI action. It resolves the individual identity behind the call rather than the shared application key, classifies the data in the prompt, applies policy specific to that identity and data class, and writes a per-decision record of what was decided. That is a different job from routing, and it depends on inspecting the request body the API gateway steps over.
Those capabilities are what let the proxy make AI-specific decisions: redact a sensitive field before the prompt reaches the model, block a class of data from a model that is not authorized to receive it, attribute a call to a named user for audit, and record the model, the destination, and the outcome per request. The distinction between an AI firewall, an AI gateway, and an AI proxy sharpens where these functions live, and an identity-aware proxy for LLMs is the identity half of the same idea.
The two are layers, not competitors
The comparison is not a contest, because the two operate at different layers and compose cleanly. The API gateway handles transport: routing, keys, rate limits, and the coarse traffic controls it is good at. The LLM proxy handles the AI decision: identity at a user grain, data classification, content policy, and per-decision audit. Running both means the transport layer stays where it belongs and the AI-specific decisions happen at a layer that can actually read the call. Trying to make the API gateway do the AI job fails not because the gateway is weak but because the decision requires reading and classifying content the gateway is not designed to interpret, a point the broader LLM gateway security model develops further.
DeepInspect
This is exactly what DeepInspect does. DeepInspect runs as a stateless LLM proxy between your applications, staff, agents, and any model endpoint. It resolves the individual identity behind each call, classifies the data in the prompt, applies per-role and per-route policy with detection and redaction of sensitive data, and writes a tamper-evident per-decision record of identity, data class, policy version, model, and outcome.
It sits alongside the API gateway you already run rather than replacing it: the gateway keeps handling routing and rate limits, and DeepInspect makes the AI-specific decisions on the content the gateway does not read. To see identity-aware policy applied to your model traffic, book a platform demo.
Frequently asked questions
- Can I just put my existing API gateway in front of the LLM?
You can, and it will route the traffic, check keys, and rate-limit it. What it will not do is read the prompt, attribute the call to an individual user rather than the shared application key, apply policy based on the data class in the request, or record the AI-specific decision. Those require inspecting and classifying the request body, which a transport-layer gateway is not built for. The API gateway covers transport; the AI decisions need a layer that reads the call.
- Is an LLM proxy a replacement for an API gateway?
No. They operate at different layers and are usually run together. The API gateway handles routing, authentication of keys, and rate limiting across your APIs. The LLM proxy handles the AI-specific decisions on model traffic: user-grain identity, data classification, content policy, and per-decision audit. Replacing one with the other loses function in both directions. The clean design keeps the gateway for transport and adds the proxy for the model-call decisions.
- Does an LLM proxy break streaming or standard model APIs?
An LLM proxy that speaks the model's protocol forwards requests and responses, including streamed responses, while evaluating identity and policy on the call. Because it sits inline on the same HTTP path the application already uses, applications point at the proxy endpoint and otherwise call the model as before. The proxy adds a decision and a record, not a change to the API contract, so streaming and standard completions continue to work.
- Where does rate limiting belong, the gateway or the proxy?
Coarse rate limiting by key or route belongs at the API gateway, which is built for it. An LLM proxy can add identity-aware limits, for example capping a specific user or agent rather than a shared key, because it resolves the individual behind the call. Running both gives you transport-level throughput control at the gateway and identity-level control at the proxy, each at the layer that can enforce it accurately.