Scavio for HTML Token Savings for RAG Pipelines

The Problem

RAG pipelines that retrieve URLs and feed raw HTML to the LLM burn ~10x the input tokens. Pre-LLM markdown extraction via Scavio /extract drops the cost dramatically without losing grounding quality.

How Scavio Helps

• 10x reduction in input tokens
• Cleaner LLM context = better answers
• Per-extract cost $0.0043
• Pairs with any LLM (Claude, GPT, DeepSeek)
• Free tier covers prototyping

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "extract markdown from 5 sources for RAG context":

import requests

API_KEY = "your_scavio_api_key"

response = requests.post(
    "https://api.scavio.dev/api/v1/search",
    headers={
        "x-api-key": API_KEY,
        "Content-Type": "application/json",
    },
    json={"query": query},
)

data = response.json()
for result in data.get("organic_results", [])[:5]:
    print(f"{result['position']}. {result['title']}")
    print(f"   {result['link']}\n")

Built for RAG pipeline maintainers, knowledge-base product teams, content-heavy LLM applications

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your html token savings for rag pipelines solution. The API returns structured JSON that is ready for processing, analysis, or feeding into AI agents.

Start with the free tier (50 credits on signup, no credit card required) and scale to paid plans when you need higher volume.