> ## Documentation Index
> Fetch the complete documentation index at: https://docs.inference.net/llms.txt
> Use this file to discover all available pages before exploring further.
# Gateway Quickstart
> Route your first LLM request through the Catalyst Gateway and see it land in the dashboard.
This page is the gateway-focused quickstart. Point your SDK at `https://api.inference.net/v1`, add a couple of headers, and Catalyst captures every request with cost, latency, and full request/response payloads. If you'd rather see the higher-level Get Started flow, start with [Record your first LLM call](/get-started/record-first-call).
The example below uses OpenAI. For other providers (Anthropic, Vertex AI, Gemini, OpenRouter, Cerebras, Groq, LangChain, ElevenLabs), see the [Gateway overview](/integrations/gateway/overview#supported-providers).
## Choose a setup path
Installing with AI is the quickest. Use the manual flow if you want to wire it up yourself.
Use the [Inference CLI](/cli/overview) to launch a coding agent like [Claude Code](https://code.claude.com/docs/en/overview), OpenCode, or Codex to scan your codebase, update your LLM clients, and add the routing headers.
Install the Inference CLI globally and log in. Your browser will open to authenticate.
```bash theme={"system"}
npm install -g @inference/cli && inf auth login
```
From your project root, run instrumentation in gateway mode.
```bash theme={"system"}
cd /path/to/your/project && inf instrument --mode gateway
```
The command guides you through the following workflow:
* Select a coding agent: Claude Code, OpenCode, or Codex.
* Scan your codebase for LLM clients such as OpenAI, Anthropic, LangChain, etc.
* Redirect base URLs to the Catalyst Gateway.
* Add routing headers so requests are authenticated, forwarded, and tagged.
* Add task IDs so each call site is grouped automatically in the dashboard.
* Review the generated changes before applying them.
Pick `both` instead of `gateway` to also install the tracing SDK in the same pass. Run `inf instrument --dry-run` to preview changes without modifying any files.
Run your application how you normally would. Requests now flow through Gateway and appear in the dashboard.
Open the [dashboard](https://inference.net/dashboard) to see request details and analytics.
Want the full canonical guide for this workflow? See [Install with AI](/integrations/install-with-ai).
Use this path if you want to wire it up yourself. The example below uses OpenAI. For Anthropic, Vertex AI, Gemini, OpenRouter, Cerebras, Groq, LangChain, and ElevenLabs, see the per-provider guides linked from the [Gateway overview](/integrations/gateway/overview#supported-providers).
You need two keys:
* **Inference Catalyst project API key** from your [dashboard](https://inference.net/dashboard) under **API Keys**
* **Provider API key** (in this example, OpenAI) from your [OpenAI account](https://platform.openai.com/api-keys)
Set them as environment variables:
```bash theme={"system"}
export INFERENCE_API_KEY=
export OPENAI_API_KEY=
```
Point your SDK at `https://api.inference.net/v1` and use your Catalyst project API key as the SDK's `apiKey`. Your provider's API key goes in the `x-inference-provider-api-key` header so the gateway can forward it. The gateway adds roughly 10ms of latency and forwards your requests to the provider as-is.
```typescript TypeScript theme={"system"}
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.inference.net/v1",
apiKey: process.env.INFERENCE_API_KEY,
defaultHeaders: {
"x-inference-provider-api-key": process.env.OPENAI_API_KEY,
"x-inference-provider": "openai",
},
});
const response = await client.chat.completions.create({
model: "gpt-4.1",
messages: [{ role: "user", content: "Hello, world!" }],
});
console.log(response.choices[0].message.content);
```
```python Python theme={"system"}
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.inference.net/v1",
api_key=os.environ["INFERENCE_API_KEY"],
default_headers={
"x-inference-provider-api-key": os.environ["OPENAI_API_KEY"],
"x-inference-provider": "openai",
},
)
response = client.chat.completions.create(
model="gpt-4.1",
messages=[{"role": "user", "content": "Hello, world!"}],
)
print(response.choices[0].message.content)
```
```bash cURL theme={"system"}
curl https://api.inference.net/v1/chat/completions \
-H "Authorization: Bearer $INFERENCE_API_KEY" \
-H "x-inference-provider-api-key: $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-H "x-inference-provider: openai" \
-d '{
"model": "gpt-4.1",
"messages": [{"role": "user", "content": "Hello, world!"}]
}'
```
Run the snippet above. Once the request completes, Catalyst captures it automatically.
Open the [dashboard](https://inference.net/dashboard) to inspect the request and metrics.
Need a different provider? See the [Gateway overview](/integrations/gateway/overview#supported-providers) for per-provider guides, or use any OpenAI-compatible endpoint through the `x-inference-provider-url` header.
That's it. Every request now flows through Gateway and gets captured automatically.
## Headers
These headers control routing, authentication, and how the request gets tagged in the dashboard. The only one required for every request is `Authorization`. Add the others as needed.
| Header | Required | Description |
| ------------------------------ | ------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `Authorization` | Yes | `Bearer `. Authenticates the request to Catalyst and selects the project scope. For OpenAI-compatible SDKs, set this as the SDK's `apiKey`. |
| `x-inference-provider-api-key` | When proxying a provider | Your downstream provider's API key (OpenAI, Groq, Cerebras, etc.). The gateway forwards it as bearer auth so your code never has to. For Anthropic's native `/v1/messages` route, use `x-api-key` instead. |
| `x-inference-provider` | Optional | Forces routing to a specific provider (`openai`, `anthropic`, `groq`, `cerebras`, `vertex-ai`, `gemini`). When omitted, the gateway infers the provider from the SDK path or base URL. Set this only when you want to override that inference. |
| `x-inference-provider-url` | Optional | Routes to any OpenAI-compatible provider by base URL, even one without a dedicated integration. For third-party OpenAI-compatible URLs, the gateway infers OpenAI automatically. Pair with `x-inference-provider` only when you want to force a specific provider name. |
| `x-inference-environment` | Optional | Tags the request with an environment name like `production`, `staging`, or `development`. Filterable in the dashboard. |
| `x-inference-task-id` | Optional | Groups requests under a logical task such as `summarize-docs` or `chat-support`. Useful for filtering, analytics, and building datasets. |
| `x-inference-metadata-*` | Optional | Attach arbitrary metadata to a request. The `x-inference-metadata-` prefix is stripped to form the key (e.g., `x-inference-metadata-chat-id: abc123` stores `chat-id: abc123`). Filter inferences and create datasets in the dashboard using these keys. |
### Provider base URLs
The base URL you point your SDK at determines which provider the gateway forwards to. Most providers don't need an explicit `x-inference-provider` header, the gateway figures it out from the URL.
| Provider | Base URL | Note |
| ------------- | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |
| OpenAI | `https://api.openai.com/v1` | Default routing, no provider header needed. |
| OpenRouter | `https://openrouter.ai/api` | No provider header needed unless you want to force `openai`. |
| Anthropic | `https://api.anthropic.com/v1` | No provider header needed for `/v1/messages` or `api.anthropic.com`. |
| Google Gemini | `https://generativelanguage.googleapis.com` | Use `/v1beta/models/*` native paths, or `/v1beta/openai` for OpenAI-compatible calls. |
| Vertex AI | `https://aiplatform.googleapis.com/v1/projects/{project}/locations/global/endpoints/openapi` | Set `x-inference-provider: vertex-ai`. |
| Azure OpenAI | `https://{resource}.openai.azure.com/openai/deployments/{deployment}` | |
| Groq | `https://api.groq.com/openai/v1` | |
| Together AI | `https://api.together.xyz/v1` | |
| Fireworks AI | `https://api.fireworks.ai/inference/v1` | |
| Perplexity | `https://api.perplexity.ai` | |
| Mistral | `https://api.mistral.ai/v1` | |
| DeepSeek | `https://api.deepseek.com/v1` | |
| Cerebras | `https://api.cerebras.ai/v1` | |
| Inference.net | `https://api.inference.net/v1` | |
## What gets captured
Once traffic is flowing, Catalyst records:
* The full request and response payloads
* Cost per call and aggregate spend
* Latency, including time to first token (TTFT) and tokens per second
* Token counts (input and output)
* Error rates and status codes
* Model and provider
## Where to find your data
* **[Metrics Explorer](/platform/gateway/metrics-explorer)** for cost, latency, errors, and usage across all your LLM calls
* **[Inference Viewer](/platform/gateway/inference-viewer)** to browse and filter individual requests and responses
## Next steps
Routing headers, supported providers, and the full set of OpenAI-compatible base URLs.
Set up Anthropic, Vertex AI, Gemini, OpenRouter, Cerebras, Groq, and more.
Group LLM calls by feature or objective to track metrics separately.
Turn captured traffic into datasets for evals and training.