Firecrawl vs Search API: What Agents Actually Need
Firecrawl extracts known URLs. Search APIs find unknown pages. Most agents need both. When to use which and the combined architecture.
Firecrawl converts known URLs into Markdown for RAG pipelines. Search APIs return structured JSON from search engines for live queries. They solve different problems. Agents building knowledge bases from specific pages need Firecrawl. Agents querying for current information need a search API. Most production agents need both.
What Firecrawl does well
- URL-to-Markdown conversion with JS rendering
- Full site crawling for knowledge base construction
- Clean extraction through anti-bot defenses
- Pricing: free 1,000 credits, Hobby $16/month (5K), Standard $83/month (100K)
What search APIs do well
- Return structured data for search queries (titles, URLs, snippets, prices)
- Cover multiple platforms (Google, Amazon, Reddit, YouTube, TikTok)
- AI Overview and SERP feature data included
- No URL needed upfront (the query finds the relevant pages)
When to use which
Task | Tool
-----------------------------|------------------
"What does this page say?" | Firecrawl
"What's the latest on X?" | Search API
Build RAG from docs site | Firecrawl
Find competitor pricing | Search API
Index a knowledge base | Firecrawl
Monitor brand mentions | Search API
Read a specific article | Firecrawl
Get Amazon product data | Search APIProduction agent architecture
import os, requests
SCAVIO_H = {"x-api-key": os.environ["SCAVIO_API_KEY"],
"Content-Type": "application/json"}
def search_web(query: str) -> list:
"""Step 1: Find relevant pages via search API."""
resp = requests.post(
"https://api.scavio.dev/api/v1/search",
headers=SCAVIO_H,
json={"query": query, "country_code": "us"},
)
return resp.json().get("organic_results", [])[:3]
def extract_page(url: str) -> str:
"""Step 2: Get full content from a specific page."""
resp = requests.post(
"https://api.scavio.dev/api/v1/extract",
headers=SCAVIO_H,
json={"url": url},
)
return resp.json().get("content", "")
def research(topic: str):
results = search_web(topic)
for r in results:
content = extract_page(r["link"])
print(f"Source: {r['link'][:50]}")
print(f"Content: {content[:200]}...")
print()Cost comparison at 5,000 requests/month
If your agent makes 3,000 search queries and 2,000 page extractions per month: Firecrawl Hobby ($16) covers extractions, Scavio Project ($30) covers searches. Total: $46/month. Using Firecrawl alone for search (by crawling Google results pages) would cost more and produce worse structured data.