# I Built a Prompt Compressor That Saves 65% on LLM Costs — Here's the Story

> Source: <https://dev.to/arjunkshah/i-built-a-prompt-compressor-that-saves-65-on-llm-costs-heres-the-story-2bdp>
> Published: 2026-06-26 19:45:49+00:00

I've been working on a side project called **SuperCompress** — an intelligent prompt compression system for LLMs. The idea is simple: most tokens you send to an LLM never need to be processed. They're padding, boilerplate, irrelevant context. But they still burn GPU cycles.

I wanted to fix that.

Working with LLM agents, I noticed something: every agent loop was sending massive context through the GPU. 10K tokens. 50K tokens. Sometimes more. Most of it was irrelevant to the specific task.

Truncation (keeping head + tail) was the standard approach, but it regularly dropped critical information from the middle of the context.

I thought: what if we could score each line of context for relevance BEFORE sending it to the GPU? A tiny CPU model that decides what matters.

The technical challenge was:

After a lot of iteration, the results surprised even me:

| Policy | KV Saved | Oracle Recall |
|---|---|---|
| Truncation | 65% | 25% |
| H2O | 65% | 98% |
| SuperCompress | 65% | 100% |

100% oracle recall at the same token savings. The policy never dropped a line the answer depended on.

Here's what hit me hardest: at 50M agent turns per day (a conservative estimate for the industry), we're wasting 100B tokens daily. That's 24K GPU hours, 1,526 tons of CO₂, 6.5M liters of cooling water. Every day.

Per 1 million compressions, SuperCompress saves:

It's tiny per call. It's enormous at scale.

Currently looking for:

Live demo: [https://supercompress.vercel.app](https://supercompress.vercel.app)

GitHub: [https://github.com/arjunkshah/supercompress](https://github.com/arjunkshah/supercompress)

Docs: [https://arjunkshah-supercompress-55.mintlify.app](https://arjunkshah-supercompress-55.mintlify.app)

**The ask:** If you're building with LLMs, try compressing your next prompt. See if the answers stay the same. I'd love to hear what you think.

**Now available on PyPI!** `pip install supercompress`