Multi-Source Data Aggregation: API vs Individual Scrapers

Building individual scrapers for each data source is a losing strategy. You write a scraper for Site A, it breaks when they redesign. You fix it, then Site B changes their anti-bot measures. By the time you have scrapers for ten sources, you are spending more time maintaining scrapers than using the data. A search API flips the approach: query publicly indexed data across all sources through a single endpoint.

Why per-site scrapers break

Every scraper is a contract with a specific HTML structure. Change a CSS class, add a JavaScript render step, or deploy Cloudflare Turnstile, and the scraper stops working. Octoparse (Standard $69/mo, Pro $249/mo) and similar visual scraping tools abstract some of this complexity, but they still break when sites change. The fundamental problem is coupling your data pipeline to individual site implementations.

The aggregation layer approach

Search engines already crawl and index billions of pages. When you query a search API, you are accessing data that Google, Bing, or other engines have already extracted and structured. Instead of scraping ten competitor pricing pages individually, search for "competitor name pricing 2026" and get the data from search results. The search engine handles the crawling, rendering, and extraction.

What works and what does not

Search API aggregation works for: publicly available data that search engines index (pricing pages, product listings, news, blog posts, job listings, company info). It does not work for: data behind logins, real-time feeds, raw datasets, or content that search engines deliberately exclude. Know the boundary before building your pipeline.

Multi-platform data collection

Different platforms index different slices of public data. Google has the broadest coverage. Amazon has product and pricing data. YouTube has video metadata and transcripts. Reddit has community discussions. A multi-platform search aggregates signals that no single platform captures alone.

import requests, os
from concurrent.futures import ThreadPoolExecutor

API = "https://api.scavio.dev/api/v1/search"
H = {"x-api-key": os.environ["SCAVIO_API_KEY"]}

def search_platform(query: str, platform: str, num: int = 5) -> dict:
    """Search a single platform and return structured results."""
    resp = requests.post(API, headers=H, json={
        "query": query,
        "platform": platform,
        "num_results": num
    })
    resp.raise_for_status()
    return {"platform": platform, "results": resp.json()}

def aggregate_search(query: str, platforms: list[str]) -> dict:
    """Search across multiple platforms in parallel."""
    aggregated = {}
    with ThreadPoolExecutor(max_workers=4) as pool:
        futures = {
            pool.submit(search_platform, query, p): p
            for p in platforms
        }
        for future in futures:
            platform = futures[future]
            try:
                data = future.result(timeout=15)
                aggregated[platform] = data["results"]
            except Exception as e:
                aggregated[platform] = {"error": str(e)}
    return aggregated

# Example: research a competitor across platforms
competitor = "Octoparse"
results = aggregate_search(
    f"{competitor} pricing reviews 2026",
    ["google", "youtube", "reddit"]
)

for platform, data in results.items():
    organic = data.get("organic_results", [])
    print(f"\n--- {platform.upper()} ({len(organic)} results) ---")
    for r in organic[:3]:
        print(f"  {r.get('title', 'N/A')}")
        print(f"  {r.get('snippet', '')[:100]}")

Structured extraction from search results

Raw search results are semi-structured: title, URL, snippet, date. To get structured data, add an LLM extraction step. Feed the search results into GPT-4o or Claude with a schema of what you want extracted. The LLM handles the parsing that would otherwise require per-site scraping logic.

import json
from openai import OpenAI

llm = OpenAI()

def extract_structured(search_results: list[dict], schema: dict) -> dict:
    """Use LLM to extract structured data from search results."""
    results_text = json.dumps(search_results[:10], indent=2)
    prompt = f"""Extract structured data from these search results.

Search results:
{results_text}

Return JSON matching this schema:
{json.dumps(schema, indent=2)}

Only include data explicitly present in the search results.
Set null for fields you cannot determine from the results."""

    resp = llm.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}],
        response_format={"type": "json_object"}
    )
    return json.loads(resp.choices[0].message.content)

# Example: extract competitor pricing
pricing_schema = {
    "company": "string",
    "plans": [{"name": "string", "price_monthly": "number|null",
               "price_annual_monthly": "number|null",
               "key_features": ["string"]}],
    "free_tier": "boolean",
    "last_verified": "string"
}

google_results = results.get("google", {}).get("organic_results", [])
pricing = extract_structured(google_results, pricing_schema)
print(json.dumps(pricing, indent=2))

When to still build a scraper

Search API aggregation does not replace all scraping. Build a dedicated scraper when you need: real-time data (search indices lag by hours to days), data behind authentication, complete datasets (not just what ranks in search), or very high-frequency monitoring of a single page. The search API approach covers the 80% of use cases where you need publicly available data from scattered sources without maintaining per-site infrastructure.

Cost comparison

Octoparse Pro at $249/mo gives you visual scraping for multiple sites but requires maintenance when sites change. A proxy service for direct scraping runs $50-200/mo depending on volume. Search API aggregation at $30/mo (7,000 credits) covers 7,000 queries across any platform -- no maintenance, no proxy management, no broken selectors to fix.

How Scavio fits

Scavio supports Google, Amazon, YouTube, Walmart, Reddit, and TikTok as search platforms through the same API endpoint. One integration, six data sources. The free tier at 250 credits/mo lets you prototype the aggregation pipeline. At $0.005/credit, a daily job that queries 5 platforms for 10 topics costs 50 credits/day -- $7.50/month. Cheaper and more reliable than maintaining five separate scrapers.