"AI gateway," "model router," "inference cloud," and "AI platform" get used interchangeably in a lot of marketing copy, which makes evaluating any specific product harder than it needs to be. They're not the same thing, and knowing which category something actually falls into tells you what to expect before you read a single feature list.
The four categories, roughly
| Category | What it actually is | Example shape |
|---|---|---|
| Consumer AI app | A ready-made chat interface for talking to a model | ChatGPT, Claude.ai |
| Inference cloud | Broad infra: deployment, GPUs, fine-tuning, hosting your own models | Together AI, Replicate |
| AI gateway | Enterprise-facing control layer: routing + policy + governance + observability across providers | Portkey, Kong AI Gateway |
| Model router | Developer-facing: one API, multiple models, minimal ceremony | OpenRouter, GonkaRouter |
The line between "gateway" and "router" is genuinely blurry in practice, a lot of products are both. But the useful distinction is intent: a gateway usually markets itself on governance and policy controls for platform teams, a router markets itself on developer speed, get one endpoint working against multiple models fast.
Where this actually matters: picking the right tool for the job
| You need | Reach for |
|---|---|
| A/B testing models for a specific agent step | A router, not a full gateway |
| Org-wide policy enforcement, PII masking, audit logs across teams | A gateway, not just a router |
| Deploying and fine-tuning your own model weights | An inference cloud, neither of the above |
| Just talking to a model in a UI | A consumer app, you don't need an API layer at all |
Picking the wrong category wastes time. Adopting a governance-heavy gateway when you just want to A/B test two models for one agent step adds ceremony you don't need yet. Adopting a lightweight router when you actually need org-wide policy enforcement means you'll outgrow it and migrate later.
GonkaRouter as a worked example of the "router" category
GonkaRouter is a clean example of the developer-first router shape: one OpenAI- and Anthropic-compatible endpoint, currently routing to Qwen, Kimi, and MiniMax model families, built on the Gonka decentralized compute network. It's explicitly not positioning itself as an inference cloud (it doesn't host your custom model weights) or a governance-heavy gateway (no enterprise policy engine mentioned), it's the "get one endpoint working against several models, fast" category.
Use-case fit, concretely:
| Use case | Fits a router like this? |
|---|---|
| Agent steps needing different models (planning vs. summarization) | Yes, this is the core case |
| A/B testing model quality for one feature | Yes |
| Enterprise-wide policy enforcement across many teams | No, that's gateway territory |
| Hosting your own fine-tuned model | No, that's inference-cloud territory |
| Just prototyping, no real traffic yet | Yes, especially with a trial credit to test before committing |
Getting started, if the fit is right
from openai import OpenAI
client = OpenAI(
api_key="sk-xxxxxx",
base_url="https://api.gonkarouter.io/v1",
)
response = client.chat.completions.create(
model="Qwen/Qwen3-235B-A22B-Instruct-2507-FP8",
messages=[{"role": "user", "content": "Hello!"}],
)
If you're OpenAI-client-shaped already, that's the entire integration. Current model list and live pricing: gonkarouter.io/models.
Curious how others here draw the router-vs-gateway line in practice, is it purely about governance features, or does self-hosted vs. hosted matter more to how you'd categorize a given tool?
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