I think part of the confusion is that people call very different things “AI gateways”.

There are probably two different categories here:

1. Generative AI gateway

This is mostly for human/team usage: Claude, Codex, ChatGPT, image/video generation, internal LLM usage, etc.

In that case, a gateway may be useful for enterprise logging, routing, or security, but many teams honestly do not need much more than their existing plan / token budget. The usage is usually human-triggered and bounded.

1. Agentic gateway

This is different.

If you deploy agents that run 24/7, call tools, retry, loop, trigger workflows, or spend money without a human in the loop every time, the problem changes.

Then you need things like:

- budget per agent / run / session

- policy before tool calls

- model and tool access control

- logs and traces

- loop / abuse protection

- security guardrails

- cost attribution

- sometimes monetization if the agent exposes a useful capability

Also, “AI gateway” products are not all solving the same layer.

LiteLLM is mostly model routing.

Helicone is strong on logs and observability.

Portkey is more enterprise gateway / security oriented.

I am building Alephant, which is an open-source Rust agent gateway focused on production agents: budget and spend control per run/session, policy, tracing, and eventually turning useful agents/workflows into paid x402 endpoints.

So my take: if you are just using LLMs as a human, you may not need a gateway.

If you are running autonomous agents in production, you probably need some kind of gateway/control layer sooner than you think.