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

# Quickstart

# Getting Started with ScaleDown: Your AI Cost Optimization Guide

## What is ScaleDown?

ScaleDown is a suite of task-specific small language models (SLMs) that reduce your AI token usage through context extraction - identifying and retaining only the information that matters for your task. You get the same quality responses while paying significantly less.

***

## Before You Start

To use ScaleDown, you'll need:

* An API key
* Basic knowledge of making API calls
* Your existing AI prompts that you want to optimize

Ready to get your API key? [Contact our sales team](https://app.reclaim.ai/m/neal-scaledown/25-min).

***

## Your First ScaleDown Request

### Step 1: Set Up Your Request

Here's how to make your first API call to compress a prompt.

<Tabs>
  <Tab title="Python" icon="python">
    ```python theme={null}
    import requests
    import json

    # ScaleDown API endpoint
    url = "https://api.scaledown.xyz/compress/raw/"

    # Your headers (replace YOUR_API_KEY with your actual key)
    headers = {
        'x-api-key': 'YOUR_API_KEY',
        'Content-Type': 'application/json'
    }
    ```
  </Tab>

  <Tab title="TypeScript" icon="typescript">
    ```typescript theme={null}
    // ScaleDown API endpoint
    const url = "[https://api.scaledown.xyz/compress/raw/](https://api.scaledown.xyz/compress/raw/)";

    // Your headers (replace YOUR_API_KEY with your actual key)
    const headers = {
        'x-api-key': 'YOUR_API_KEY',
        'Content-Type': 'application/json'
    };
    ```
  </Tab>

  <Tab title="JavaScript" icon="javascript">
    ```javascript theme={null}
    // ScaleDown API endpoint
    const url = "[https://api.scaledown.xyz/compress/raw/](https://api.scaledown.xyz/compress/raw/)";

    // Your headers (replace YOUR_API_KEY with your actual key)
    const headers = {
        'x-api-key': 'YOUR_API_KEY',
        'Content-Type': 'application/json'
    };
    ```
  </Tab>
</Tabs>

### Step 2: Configure Your Compression

Separate your context from your main prompt and set the compression rate to `"auto"` for the best results.

<Tabs>
  <Tab title="Python" icon="python">
    ```python theme={null}
    payload = {
        "context": "Context about your specific topic or instructions here",
        "prompt": "Your actual query or question here",
        "scaledown": {
            "rate": "auto" # Automatic compression rate optimization
        }
    }
    ```
  </Tab>

  <Tab title="TypeScript" icon="typescript">
    ```typescript theme={null}
    interface CompressRequest {
        context: string;
        prompt: string;
        scaledown: {
            rate: string;
        };
    }

    const payload: CompressRequest = {
        context: "Context about your specific topic or instructions here",
        prompt: "Your actual query or question here",
        scaledown: {
            rate: "auto" // Automatic compression rate optimization
        }
    };
    ```
  </Tab>

  <Tab title="JavaScript" icon="javascript">
    ```javascript theme={null}
    const payload = {
        context: "Context about your specific topic or instructions here",
        prompt: "Your actual query or question here",
        scaledown: {
            rate: "auto" // Automatic compression rate optimization
        }
    };
    ```
  </Tab>
</Tabs>

### Step 3: Make the Request

With your request set up and configured, you can now execute the API call.

<Tabs>
  <Tab title="Python" icon="python">
    ```python theme={null}
    response = requests.post(url, headers=headers, data=json.dumps(payload))
    result = response.json()

    print(result)
    ```
  </Tab>

  <Tab title="TypeScript" icon="typescript">
    ```typescript theme={null}
    // Assuming you're using a fetch-like library (e.g., node-fetch)
    const response = await fetch(url, {
        method: 'POST',
        headers: headers,
        body: JSON.stringify(payload)
    });
    const result = await response.json();

    console.log(result);
    ```
  </Tab>

  <Tab title="JavaScript" icon="javascript">
    ```javascript theme={null}
    // Using Fetch API in a browser or Node.js environment
    fetch(url, {
        method: 'POST',
        headers: headers,
        body: JSON.stringify(payload)
    })
    .then(response => response.json())
    .then(result => {
        console.log(result);
    })
    .catch(error => console.error('Error:', error));
    ```
  </Tab>
</Tabs>

That's it! Your prompt is now compressed and ready to be used with your AI model.

***

## Understanding the Response Structure

The API response provides the compressed prompt along with useful metadata about the operation.

```json theme={null}
{
  "compressed_prompt": "Your optimized context here...",
  "original_prompt_tokens": 150,
  "compressed_prompt_tokens": 65,
  "successful": true,
  "latency_ms": 2341,
  "request_metadata": {
    "compression_time_ms": 2341,
    "compression_rate": "auto",
    "prompt_length": 425,
    "compressed_prompt_length": 189
  }
}
```