> ## 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.

# Overview

> Reduce token usage across any AI workflow without changing your prompts or models.

## What it does

The `/compress/raw/` endpoint takes a prompt and its supporting context, then returns a semantically equivalent version that uses significantly fewer tokens. The compression is lossless in intent - the downstream model receives the same information, just expressed more efficiently.

Compression is applied primarily to the `context` field, where redundancy tends to be highest. The `prompt` (your actual question or instruction) is kept intact wherever possible to preserve precise intent.

## When to use it

**You're spending too much on tokens.** If your AI calls include long system prompts, retrieved documents, or conversation history, a large share of your token bill is coming from context - not the actual question. Compressing that context by 50–70% directly reduces cost with no changes to your model or application logic.

**You're hitting context window limits.** Long documents, multi-turn conversations, and RAG pipelines frequently run into model context limits. Compression lets you fit more into a single call without truncating content or splitting requests.

**You're running latency-sensitive workloads.** Fewer input tokens means faster time-to-first-token from the model. For real-time or user-facing applications, this can make a meaningful difference.

**You want to use a smaller, cheaper model.** Smaller models have tighter context windows. Compression makes it practical to run workloads on smaller models that would otherwise require a larger one.

## Common use cases

| Use case               | How compression helps                                                     |
| ---------------------- | ------------------------------------------------------------------------- |
| RAG pipelines          | Compress retrieved chunks before they're included in the prompt           |
| Long document Q\&A     | Fit full documents into context without truncation                        |
| Multi-turn chat        | Compress conversation history to maintain context across long sessions    |
| Code review / analysis | Compress large codebases or diffs passed as context                       |
| Batch processing       | Reduce cost at scale when running the same pipeline across many documents |
| Reasoning model calls  | Reduce the token overhead of chain-of-thought and reasoning traces        |

## How it fits into your workflow

Compress sits between your data retrieval step and your model call. Your application logic doesn't change - you just route the context through ScaleDown before sending it to the model.

```
[Your app] → [Retrieve context] → [POST /compress/raw/] → [Call your AI model]
```

The `compressed_prompt` field in the response is a drop-in replacement for the combined context and prompt you would have sent directly.

## Pricing impact

Token savings translate directly to cost savings. At a 60% compression ratio, a workflow that previously cost $10/day in token spend drops to $4/day - with no changes to the model, the output quality, or the application.