How Does an LLM Gateway Work, Step by Step

An LLM gateway works by sitting between your application and one or more language model providers, intercepting every request to add authentication, routing, logging, and normalization before the request ever reaches the underlying model. Instead of your code calling OpenAI, Anthropic, or another provider's API directly, it calls the gateway, which handles the provider-specific details and hands back a consistent response.

The short version: your app sends a request with an API key, the gateway validates it, decides which provider/model to use, transforms the request into that provider's expected format, streams or returns the response, and logs everything for usage and billing. The rest of this article walks through each of those steps in order, since that's usually what people actually want to understand when they ask "how does an LLM gateway work."

The request lifecycle

1. Authentication

Every request starts with a key check. Instead of embedding a raw provider API key in your client, you send a gateway-issued key (something like sub_live_... in SubToAPI's case) in the Authorization header:

curl https://api.subtoapi.app/v1/messages \
  -H "Authorization: Bearer $SUBTOAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-5",
    "max_tokens": 1024,
    "messages": [{"role": "user", "content": "Summarize this ticket."}]
  }'

The gateway validates the key, checks it against rate limits or team quotas, and rejects anything that doesn't pass before spending a single token on the actual model call. This is also where scoped keys matter — different keys for different apps or environments mean you can revoke one without touching the others.

2. Routing

Once the request is authenticated, the gateway decides where it goes. Routing can be as simple as "this key maps to Claude" or as complex as picking between multiple models based on cost, latency, or availability. Some gateways route by explicit model name in the request body; others do dynamic routing — falling back to a secondary provider if the primary one is slow or returning errors.

Routing logic typically checks:

• Which provider/model the request specifies (or defaults to)
• Whether that provider is healthy right now
• Team or plan-level restrictions (e.g., a Solo plan key shouldn't hit team-only endpoints)

3. Request transformation

Providers don't all speak the same dialect. Message formats, streaming protocols, and tool-calling schemas differ between vendors. A gateway normalizes the incoming request into whatever shape the target provider expects, and normalizes the response back into a consistent format your app can rely on regardless of which model actually answered.

This is the part that saves the most integration work. If you've ever migrated between providers and had to rewrite your parsing logic, you've felt the absence of this layer firsthand.

4. The actual model call

With the request transformed, the gateway makes the outbound call to the provider's API. This is where your actual usage happens — tokens get consumed, the model generates output, and the response starts coming back. For streaming use cases, the gateway typically keeps the connection open and relays chunks as they arrive rather than waiting for the full response:

const response = await fetch("https://api.subtoapi.app/v1/messages", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.SUBTOAPI_KEY}`,
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    model: "claude-sonnet-4-5",
    max_tokens: 1024,
    stream: true,
    messages: [{ role: "user", content: "Draft a release note." }]
  })
});

const reader = response.body.getReader();
// read and forward chunks as they arrive

Streaming matters for anything user-facing — chat UIs, live agents, tool-use loops — because it lets you show partial output instead of a blank screen while the model finishes.

5. Tool use handling

If your request includes tool definitions, the gateway passes them through to the model and relays back tool-call instructions in a structured format. Your application executes the tool, sends the result back through the gateway, and the loop continues. The gateway itself doesn't run your tools — it just makes sure the request/response contract for tool calls stays consistent across calls.

6. Logging and metadata

After (or during, for streaming) the response, the gateway records what happened: which key made the request, which model handled it, how many input/output tokens were used, latency, and whether it succeeded or errored. This is what powers usage dashboards, per-key cost tracking, and team-level reporting. Without this layer, you're stuck manually reconciling provider invoices against application logs.

7. Failover and retries

Some gateways add a resilience layer: if a provider call times out or returns a 5xx, the gateway can retry automatically or fall back to a secondary model, often transparently to the caller. This isn't universal — check what a specific gateway actually guarantees before relying on it — but it's a common reason teams adopt a gateway instead of calling providers directly.

Why this architecture matters in practice

The value of understanding this flow isn't academic. It explains why a gateway can offer things a raw provider API can't on its own:

• A single key format across environments and team members, instead of sharing one raw provider secret.
• Consistent request/response shapes even if you route between different models.
• Usage visibility per key or per team member, not just a single opaque provider bill.
• Streaming and tool use that work the same way regardless of which model answered.

If you're evaluating whether to add a gateway in front of your Claude usage, SubToAPI implements this exact flow: you get application API keys, streaming, tool support, and per-key usage metadata out of the box. You can see the request/response shape in the docs or start with the quickstart to send your first request in a few minutes. Pricing starts with a Solo plan at €9, with Team and Scale tiers for shared usage — details on /pricing.

questions

Does an LLM gateway slow down responses? It adds a small amount of latency for auth and routing, typically a few milliseconds, which is negligible compared to model generation time. Streaming responses are relayed as they arrive, so perceived latency for the first token is close to calling the provider directly.

Can a gateway work with multiple LLM providers at once? Yes, if it's built to route by provider or model name. Some gateways focus on a single provider (like Claude) with a cleaner, more predictable feature set; others support multiple providers with a unified interface. Check what a given gateway actually supports before assuming multi-provider routing is included.

Do I need a gateway if I'm the only developer on a project? Not necessarily, but even solo projects benefit from scoped API keys, usage tracking, and a stable request format if you expect to add team members, switch models, or need clear cost visibility later. Start with the signup flow if you want to try it without committing to a plan first.