Complete Guide to Metasearch API

A metasearch API aggregates results from multiple search engines into a single response. Instead of querying Google, Bing, and DuckDuckGo separately, you make one API call and get combined, deduplicated results -- useful for building search tools, AI agents, price comparison engines, and research platforms.

This guide explains what metasearch APIs are, how they work, which providers to consider, and how to build your own metasearch pipeline using SearchHive and other tools.

Key Takeaways

• Metasearch APIs combine results from multiple search engines into unified responses
• SearchHive SwiftSearch functions as a metasearch API, aggregating from multiple sources
• Brave Search API is an independent search index (not metasearch) but often compared
• Building your own metasearch requires aggregating 3+ search APIs and handling deduplication
• Cost per query varies from $0.001 (SearchHive) to $0.005 (Brave) to $0.025 (SerpAPI)

What Is a Metasearch API?

A metasearch engine doesn't have its own index. Instead, it queries multiple search engines simultaneously and merges the results. The concept dates back to early tools like Dogpile and MetaCrawler, but modern implementations are API-first and designed for programmatic access.

How it works:

1. User sends a query to the metasearch API
2. The API forwards the query to multiple backend search engines (Google, Bing, DuckDuckGo, etc.)
3. Results are collected, normalized, deduplicated, and ranked
4. Combined results return to the user

Key advantages over single-engine search:

• Broader coverage (no single engine indexes everything)
• Reduced bias (Google and Bing rank results differently)
• Redundancy (if one engine is down or returns poor results, others compensate)
• Cost optimization (route queries to cheaper engines when quality is sufficient)

Top Metasearch API Providers

1. SerpAPI

SerpAPI is the most established search API provider, offering Google, Bing, YouTube, and 20+ other engine results through a unified API.

Engines supported: Google (web, images, news, maps, shopping), Bing, YouTube, Yahoo, Baidu, DuckDuckGo, and more

Pricing: Free (250 searches/mo), Starter $25/mo (1K), up to Cloud plans at $3,750/mo (1M)

Strengths:

• Largest engine selection (20+)
• Consistent result format across engines
• Good documentation and SDKs (Python, Node, Ruby, etc.)

Weaknesses:

• Expensive at scale ($3,750/mo for 1M searches)
• Rate limited on lower tiers
• Google results can be inconsistent due to scraping

from serpapi import GoogleSearch

params = {
    "q": "best metasearch api 2025",
    "api_key": "YOUR_SERPAPI_KEY",
    "engine": "google",
    "num": 10
}

search = GoogleSearch(params)
results = search.get_dict()

for result in results.get("organic_results", []):
    print(f"{result['title']}: {result['link']}")

2. Serper.dev

Serper.dev provides fast Google search results via a clean API.

Engines supported: Google (web, images, news, maps, places, videos)

Pricing: Pay-as-you-go: $50/50K ($1/1K), $375/500K ($0.75/1K), $1,250/2.5M ($0.50/1K). 2,500 free on signup.

Strengths:

• Competitive per-query pricing at volume
• Fast response times
• Simple API design

Weaknesses:

• Google only (not a true metasearch)
• Credits expire after 6 months

3. SearchHive SwiftSearch

SearchHive provides a unified search API that aggregates from multiple sources, functioning effectively as a metasearch API.

Engines supported: Multiple search sources (aggregated)

Pricing: Free (500 credits), Starter $9/mo (5K credits), Builder $49/mo (100K credits), Unicorn $199/mo (500K credits)

Strengths:

• Cheapest at scale ($49/mo for 100K vs $725/mo on SerpAPI)
• Unified platform with scraping and research APIs
• Clean free JSON formatter output ready for LLM consumption

Weaknesses:

• Fewer individual engine options than SerpAPI
• No separate Google, Bing, YouTube endpoints

import requests

response = requests.get(
    "https://api.searchhive.dev/v1/search",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    params={
        "q": "best metasearch api comparison 2025",
        "limit": 10
    }
)

data = response.json()
for result in data.get("results", []):
    print(f"{result['title']}")
    print(f"  URL: {result['url']}")
    print(f"  Snippet: {result.get('snippet', 'N/A')[:100]}...\n")

4. Brave Search API

Brave Search API uses Brave's own independent web index (30B+ pages), not metasearch. It's included here because it's a common alternative.

Pricing: $5/1K searches, $4/1K answers, $5 free credits/month

Strengths:

• Independent index (not scraping Google)
• Multiple endpoints (web, images, videos, news, local, answers)
• Good AI integration (LLM context, MCP server)

Weaknesses:

• Not metasearch -- relies on Brave's own index
• No scraping or research capabilities
• Expensive at scale compared to SearchHive

5. DuckDuckGo (Unofficial APIs)

DuckDuckGo doesn't offer an official API, but several unofficial wrappers exist:

• duckduckgo-search Python package
• DDGS from the duckduckgo_search library

from duckduckgo_search import DDGS

with DDGS() as ddgs:
    results = ddgs.text("metasearch api comparison", max_results=10)
    for r in results:
        print(f"{r['title']}: {r['href']}")

Pros: Free, no API key needed. Cons: No SLA, may break without notice, rate limited.

Building Your Own Metasearch API

For teams that need full control, combining multiple search APIs into a custom metasearch is straightforward:

import requests
import time
from concurrent.futures import ThreadPoolExecutor, as_completed

def query_searchhive(query, api_key):
    try:
        resp = requests.get(
            "https://api.searchhive.dev/v1/search",
            headers={"Authorization": f"Bearer {api_key}"},
            params={"q": query, "limit": 10},
            timeout=10
        )
        return [("searchhive", r) for r in resp.json().get("results", [])]
    except Exception:
        return []

def query_brave(query, api_key):
    try:
        resp = requests.get(
            "https://api.search.brave.com/res/v1/web/search",
            headers={"X-Subscription-Token": api_key},
            params={"q": query, "count": 10},
            timeout=10
        )
        return [("brave", r) for r in resp.json().get("web", {}).get("results", [])]
    except Exception:
        return []

def metasearch(query, searchhive_key, brave_key=None):
    all_results = []

    # Query multiple engines in parallel
    engines = [("searchhive", lambda: query_searchhive(query, searchhive_key))]
    if brave_key:
        engines.append(("brave", lambda: query_brave(query, brave_key)))

    with ThreadPoolExecutor(max_workers=3) as executor:
        futures = {executor.submit(fn): name for name, fn in engines}
        for future in as_completed(futures):
            all_results.extend(future.result())

    # Deduplicate by URL
    seen_urls = set()
    unique_results = []
    for source, result in all_results:
        url = result.get("url", result.get("link", ""))
        if url not in seen_urls:
            seen_urls.add(url)
            result["_source"] = source
            unique_results.append(result)

    return unique_results[:20]

# Usage
results = metasearch("python web scraping 2025", SEARCHHIVE_KEY, BRAVE_KEY)
for r in results:
    title = r.get("title", "N/A")
    url = r.get("url", r.get("link", ""))
    source = r.get("_source", "unknown")
    print(f"[{source}] {title} - {url}")

Deduplication Strategies

When merging results from multiple engines:

• URL-based: Deduplicate by canonical URL (most common)
• Title-based: Catch near-duplicates with different URLs
• Fuzzy matching: Use string similarity for titles and snippets
• Source weighting: Prioritize results that appear in multiple engines

Ranking Merged Results

Common approaches:

• Round-robin: Alternate between engine results (simplest)
• Score aggregation: Average or sum normalized relevance scores
• Reciprocal rank fusion: A well-studied method that outperforms simple averaging
• Source overlap boosting: Results from multiple engines rank higher

def reciprocal_rank_fusion(result_groups, k=60):
    scores = {}
    for group in result_groups:
        for rank, item in enumerate(group):
            url = item.get("url", item.get("link", ""))
            scores[url] = scores.get(url, 0) + 1.0 / (k + rank + 1)
    return sorted(scores.items(), key=lambda x: x[1], reverse=True)

Pricing Comparison

Best Practices

1. Cache aggressively: Metasearch means multiple API calls per user query -- cache results with TTL
2. Timeout and fallback: Set per-engine timeouts (5-10s) and return partial results if an engine is slow
3. Parallel queries: Always query engines concurrently with ThreadPoolExecutor or asyncio
4. Monitor engine health: Track which engines are returning errors and route accordingly
5. Rate limit awareness: Respect each engine's rate limits to avoid bans
6. Result format normalization: Each engine returns different field names -- normalize early

Conclusion

Metasearch APIs provide broader, less biased results than any single search engine. For most projects, SearchHive SwiftSearch offers the best value -- metasearch-quality results at a fraction of the cost of SerpAPI or Brave, with the added benefit of integrated scraping and research APIs.

If you need results from specific engines (Google Images, YouTube, Google Maps), SerpAPI's breadth of engine support justifies its premium pricing. For production systems, building a custom metasearch layer on top of SearchHive + one other provider gives you redundancy and coverage without vendor lock-in.

Start with SearchHive's free tier (500 credits, no credit card) and test your metasearch pipeline today. The unified SwiftSearch + ScrapeForge + DeepDive platform handles search, content extraction, and deep research in one API.