Scraping 10M Tokens for RAG: What Actually Works in 2026

An r/Rag post asked which web scraper to use for ~10M tokens of tech articles, docs, blogs, and PDFs for a RAG pipeline. The honest 2026 answer: the question often has the wrong shape. For indexed public content, search-as-source beats scraping for cost and reliability.

The reframing

"What scraper do I use?" assumes you need a scraper. For tech articles + docs + blogs (well indexed, well structured), the cheaper approach is search-as-source: 200-500 seed queries → Scavio Google SERP → URL deduplication → Scavio /extract for top URLs → 8M tokens of clean Markdown. No Cloudflare arms race, no per-site parser maintenance.

The recipe

import requests, os
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}

# 200-500 seed queries covering the topic
seeds = [
    'ai agent infrastructure 2026',
    'agent memory patterns',
    'tool use mcp',
    # ... 200-500 entries
]

# Per seed: collect top URLs
urls = set()
for q in seeds:
    r = requests.post('https://api.scavio.dev/api/v1/search',
                      headers=H,
                      json={'query': q}).json()
    for o in (r.get('organic_results') or [])[:10]:
        urls.add(o['link'])

print(f'Unique URLs: {len(urls)}')  # Typically 3-5K from 200 seeds

# Per URL: extract clean Markdown
docs = []
for u in list(urls)[:2000]:
    d = requests.post('https://api.scavio.dev/api/v1/extract',
                      headers=H,
                      json={'url': u}).json()
    if d.get('text'):
        docs.append({'url': u, 'text': d['text']})

The cost

200 seeds × ~5 SERP credits + 2K extracts ≈ 11K credits ≈ $50-90 within Scavio Project tier credit usage. Compare to Firecrawl Standard tier, Apify per-actor compute, or DIY headless infra at the same scale — usually higher when you're paying per-page or per-compute-unit.

Why this shape works

Cloudflare and Akamai turnstiles block headless browsers at increasing rates through 2025-2026. Site selectors change faster than parsers can update. JS-heavy SPAs require expensive rendering. The scrape-then- extract pipeline pays all of these costs. Search-as-source pays none of them because Scavio (and other SERP APIs) have already done the work.

Where scraping is still right

Behind-auth content (paywalled academic, gated corporate docs). JS-heavy SPA targets where critical content only renders post-load. Niche sources Google doesn't index well. For these, dedicated scraping infra (Crawl4AI, Apify, Firecrawl crawl mode) is the right shape and Scavio doesn't replace it.

Multi-platform bonus

The same Scavio key handles Reddit threads, YouTube transcripts, and Amazon/Walmart product data. RAG corpora drawing from multiple platforms avoid stitching together separate scrapers.

# Reddit thread for community context
reddit = requests.post('https://api.scavio.dev/api/v1/search',
                       headers=H,
                       json={'platform': 'reddit',
                             'query': 'ai agent infrastructure 2026'}).json()

# YouTube transcript for tutorial content
youtube = requests.post('https://api.scavio.dev/api/v1/search',
                        headers=H,
                        json={'platform': 'youtube',
                              'query': 'building agent infrastructure',
                              'include_transcript': True}).json()

Quarterly refresh

Re-run the pipeline; diff URL set; embed only new/changed pages. Keeps the corpus fresh without re-paying for the entire build. Cron quarterly or monthly depending on topic velocity.

Honest ranking of alternatives

Firecrawl crawl mode: hosted infra, no Cloudflare for you, but per- page cost adds up at 10M tokens. Crawl4AI / DIY Playwright: free OSS but Cloudflare arms race on you. Apify marketplace: 1,500+ actors but compute units add up. Common Crawl: free but stale. Scavio search-as- source wins for indexed public content; pick by content type.

What to do this week

If your corpus is mostly tech articles + docs + blogs: switch to search-as-source via Scavio. Run the pipeline on a 1M-token subset first to validate the shape; then scale to 10M. Total elapsed time for a 10M-token build: 1-2 days including re-runs and dedup tuning.

Verified-online May 2026 against the Scavio API and the source post.