Scavio for Scrape vs Search Decision for RAG

The Problem

An r/Rag post asked which scraper to use for huge data. The honest 2026 framing: most of what people scrape is already in SERP and returns as typed JSON.

How Scavio Helps

• Decision rule per content type
• Avoids the scraper arms race when not needed
• Honest about behind-auth / JS-heavy edge cases
• Multi-platform under one key for the search side
• Predictable per-doc cost vs variable scraper-cost

Relevant Platforms

Quick Start: Python Example

Here is a quick example searching Google for "Per topic: search-first (Scavio Google), then /extract top URLs, then fall back to dedicated scraper only for behind-auth or JS-heavy targets that survive the cut":

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, RAG SaaS founders, research labs, anyone making the build-vs-buy scraping call

Scavio handles the search infrastructure — proxies, CAPTCHAs, rate limits, and anti-bot detection — so you can focus on building your scrape vs search decision for rag 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.