> ## Documentation Index
> Fetch the complete documentation index at: https://runinfra.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> Create an account, describe your pipeline, optimize it, and deploy a live OpenAI-compatible inference endpoint, all without writing infrastructure code.

Build and deploy your first AI inference endpoint. Create an account, describe what you need in plain English, run optimization, deploy to a live URL, and make your first API call. About five minutes, start to finish.

<Columns cols={3}>
  <Card title="Free to start" icon="gift">
    \$10 in free credits on signup. No credit card required.
  </Card>

  <Card title="OpenAI-compatible" icon="plug">
    Works with OpenAI Python/JavaScript SDKs plus documented LangChain and LlamaIndex examples.
  </Card>

  <Card title="Under 2s cold start" icon="zap">
    Cached weights keep even scale-to-zero endpoints fast.
  </Card>
</Columns>

<Note>
  Your $10 in free credits let you build, optimize, and test pipelines in the playground. Deploying to a live endpoint requires a paid Core plan (custom amount from $50/mo).
</Note>

<Steps>
  <Step title="Create an account">
    Sign up at [runinfra.ai/sign-up](https://runinfra.ai/sign-up) using GitHub or Google. No credit card is required to get started.
  </Step>

  <Step title="Describe your pipeline">
    Open [the dashboard](https://runinfra.ai/~) and type what you need in plain English:

    ```text theme={"dark"}
    I need a fast chatbot using Llama 3.1 8B optimized for low latency
    ```

    The agent builds your pipeline, selects the model, and configures everything automatically. To make changes, just continue the conversation:

    ```text theme={"dark"}
    Add a response cache and switch to Qwen 2.5 7B instead
    ```
  </Step>

  <Step title="Optimize">
    The agent benchmarks your model across GPUs, searches for optimized variants (AWQ, GPTQ, FP8), and identifies the best configuration. You see real-time progress as experiments complete.

    You can set specific targets before optimization starts:

    ```text theme={"dark"}
    Optimize for latency, keep cost under $0.10 per request
    ```

    Optimization takes 2-5 minutes. When it finishes, review the results and select the configuration you want to deploy.
  </Step>

  <Step title="Deploy your endpoint">
    Click **Deploy** in the deploy tab. RunInfra provisions a GPU endpoint with scale-to-zero and fast cold starts (under 2 seconds). Your endpoint URL and API key appear as soon as the endpoint is ready.

    <Warning>
      Deployment requires a paid Core plan (custom amount from $50/mo). Your $10 in free credits let you build, optimize, and test in the playground without upgrading.
    </Warning>
  </Step>

  <Step title="Use your endpoint">
    Your endpoint is OpenAI-compatible. Use your RunInfra API key and the RunInfra base URL with OpenAI Python or JavaScript SDKs, or direct HTTP requests.

    <CodeGroup>
      ```python python theme={"dark"}
      import os
      from openai import OpenAI

      client = OpenAI(
          base_url="https://api.runinfra.ai/v1",
          api_key="YOUR_RUNINFRA_API_KEY",
      )

      response = client.chat.completions.create(
          model=os.environ["RUNINFRA_MODEL"],
          messages=[{"role": "user", "content": "What is RunInfra?"}],
      )

      print(response.choices[0].message.content)
      ```

      ```javascript javascript theme={"dark"}
      import OpenAI from "openai";

      const client = new OpenAI({
        baseURL: "https://api.runinfra.ai/v1",
        apiKey: "YOUR_RUNINFRA_API_KEY",
      });

      const response = await client.chat.completions.create({
        model: process.env.RUNINFRA_MODEL,
        messages: [{ role: "user", content: "What is RunInfra?" }],
      });

      console.log(response.choices[0].message.content);
      ```

      ```bash cURL theme={"dark"}
      curl https://api.runinfra.ai/v1/chat/completions \
        -H "Authorization: Bearer YOUR_RUNINFRA_API_KEY" \
        -H "Content-Type: application/json" \
        -d "{\"model\":\"${RUNINFRA_MODEL:?Set RUNINFRA_MODEL from GET /v1/models}\",\"messages\":[{\"role\":\"user\",\"content\":\"What is RunInfra?\"}]}"
      ```
    </CodeGroup>

    Replace `YOUR_RUNINFRA_API_KEY` with your actual API key from [Settings > API Keys](https://runinfra.ai/settings/api-keys), and set `RUNINFRA_MODEL` to a model id returned by `GET /v1/models`.
  </Step>
</Steps>

## Next steps

<Columns cols={2}>
  <Card title="Use cases" icon="layers" href="/use-cases/overview">
    Six pre-built workflows: voice, assistants, embeddings, RAG, document AI, transcription.
  </Card>

  <Card title="Prompting best practices" icon="sparkles" href="/prompting/best-practices">
    Write better prompts and get better pipelines from the agent.
  </Card>

  <Card title="Deployment" icon="server" href="/deployments/overview">
    Flex vs Active endpoints, scaling, and cold-start configuration.
  </Card>

  <Card title="API reference" icon="square-terminal" href="/api-reference/introduction">
    Supported OpenAI-compatible endpoints and fields.
  </Card>
</Columns>