Scavio for Large RAG Corpus Build (10M Tokens)

The Problem

An r/Rag post asked which web scraper to use for ~10M tokens of tech articles, docs, blogs. Often the question has the wrong shape; for indexed public content, search-as-source beats scraping for cost and reliability.

How Scavio Helps

• Avoids most scraper pain (Cloudflare, layouts, headless infra)
• Typed JSON throughout the pipeline
• 10M tokens typically $20-90 in Scavio + extract
• Predictable per-topic cost
• Scraping reserved for behind-auth and JS-heavy targets only

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "200 seed queries → Scavio Google SERP per query → top-N URL deduplication → Scavio /extract → 8M tokens of clean Markdown → embed → done":

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 AI engineers building RAG pipelines, RAG SaaS founders, research labs constructing domain corpora

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your large rag corpus build (10m tokens) solution. The API returns structured JSON that is ready for processing, analysis, or feeding into AI agents.

Start with the free tier (500 credits/month, no credit card required) and scale to paid plans when you need higher volume.