Common Crawl vs Real-Time Search API: When to Use Each

Common Crawl provides petabytes of archived web data for free. Real-time search APIs return live results for $0.005-0.03 per query. The choice depends on whether your application needs historical breadth or current accuracy.

What Common Crawl gives you

Common Crawl archives roughly 3.5 billion web pages per monthly crawl. The data is stored on AWS S3 as WARC files and is free to access (you pay only for S3 egress or Athena queries). It is the largest open web dataset available.

• Petabytes of historical web data going back to 2008
• Free to access (S3 egress costs only)
• Full HTML content, not just snippets
• Good for training data, academic research, and backfill
• Athena-queryable via cc-index for URL-level lookups

What Common Crawl cannot do

• Data is 2-8 weeks old by the time a crawl is published
• No ranking signals -- just raw pages, no relevance scoring
• No SERP features (AI Overviews, PAA, knowledge panels)
• Processing WARC files requires significant compute
• Coverage gaps: paywalled, JavaScript-rendered, and recently published pages are missing

When to use Common Crawl

Common Crawl is the right choice for batch analysis where freshness does not matter:

• Training data for ML models
• Historical link analysis and domain authority estimation
• Large-scale content analysis (language distribution, technology adoption)
• Backfilling a knowledge base with static facts

# Query Common Crawl index via Athena
import boto3

athena = boto3.client("athena", region_name="us-east-1")
query = """
SELECT url, warc_filename, warc_record_offset
FROM ccindex.ccindex
WHERE crawl = 'CC-MAIN-2026-18'
  AND url_host_name = 'example.com'
LIMIT 100
"""
response = athena.start_query_execution(
    QueryString=query,
    ResultConfiguration={"OutputLocation": "s3://my-bucket/athena-results/"},
)

When to use a real-time search API

Any application where users or agents need current information:

• AI agent grounding (pricing, availability, news)
• Competitive monitoring and rank tracking
• Lead generation with current business data
• Content verification and fact-checking
• Anything where a 2-week delay makes the data wrong

import requests, os

# Real-time search: current results with ranking
resp = requests.post(
    "https://api.scavio.dev/api/v1/search",
    headers={"x-api-key": os.environ["SCAVIO_API_KEY"]},
    json={
        "query": "best CRM software pricing 2026",
        "num_results": 10,
    },
)
results = resp.json().get("organic_results", [])
# Results reflect today's rankings and content

Hybrid architecture

The most cost-effective approach for data-heavy applications is using Common Crawl for baseline data and real-time APIs for freshness-sensitive queries. Pre-populate your knowledge base from Common Crawl, then use live search to verify and update stale entries.

def smart_search(query, kb_store):
    # Check local knowledge base first (populated from Common Crawl)
    local_results = kb_store.search(query, threshold=0.8)

    if local_results and local_results[0]["age_days"] < 7:
        return {"source": "local", "results": local_results}

    # Stale or missing: hit real-time API
    resp = requests.post(
        "https://api.scavio.dev/api/v1/search",
        headers={"x-api-key": os.environ["SCAVIO_API_KEY"]},
        json={"query": query, "num_results": 10},
    )
    fresh = resp.json().get("organic_results", [])

    # Update knowledge base with fresh data
    kb_store.upsert(query, fresh)
    return {"source": "api", "results": fresh}

Cost comparison at scale

For 1 million lookups: Common Crawl Athena queries cost roughly $5-15 in S3 scan charges. The same volume via real-time search API costs $5,000-30,000. The 100-1000x cost difference makes Common Crawl essential for batch workloads, but the freshness gap makes it unsuitable for anything real-time.