SerpApi alternative: ScrapingBot vs SerpApi

If you run a few thousand Google searches a month and read the organic results, People also ask and related searches, the price gap between SerpApi and ScrapingBot is large. If you need many engines, every SERP feature parsed, or a legal indemnity, SerpApi is built for that and ScrapingBot is not.

The short version

ScrapingBot vs SerpApi at a glance

Per-1,000 figures: ScrapingBot $49.99 ÷ 275,000 credits × 10 credits × 1,000 = $1.82; SerpApi Developer $75 ÷ 5,000 × 1,000 = $15. SerpApi also sells faster "Ludicrous Speed" versions of each plan at two and four times the price.

What you get back

A ScrapingBot Google web search for best espresso machine in the US, trimmed:

{
  "success": true,
  "searchParameters": { "q": "best espresso machine", "gl": "us", "hl": "en", "type": "search", "engine": "google" },
  "organic": [
    {
      "position": 1,
      "title": "The 7 Greatest Espresso Machines in 2026 [No *BS Guide]",
      "link": "https://coffeechronicler.com/gear/espresso-machines/",
      "snippet": "That being said, Rancilio Silvia is the gold standard when it comes to home espresso makers, …",
      "date": "Jul 11, 2026"
    },
    …
  ],
  "peopleAlsoAsk": [ { "question": "What is the highest rated espresso machine for home use?" }, … ],
  "relatedSearches": [ { "query": "Best espresso machine for home" }, … ],
  "duration": "0.85",
  "statusCode": 200,
  "creditsUsed": 10
}

That covers what most rank trackers, SEO tools and AI agents read. SerpApi's Google response goes further, with knowledge graph, local results, pagination metadata and many more blocks, so check which ones your code depends on before you switch. ScrapingBot's separate endpoints cover images, news, shopping, places, maps and place reviews, each at 10 credits.

Migrating from SerpApi

SerpApi takes a GET with the engine and key in the query string. ScrapingBot takes a POST with the key in a header:

# Before: SerpApi
import requests

res = requests.get("https://serpapi.com/search.json", timeout=60, params={
    "engine": "google", "q": "best espresso machine", "gl": "us", "hl": "en",
    "api_key": SERPAPI_KEY,
})
serp = res.json()
results = [(r["position"], r["title"], r["link"]) for r in serp.get("organic_results", [])]
questions = [q["question"] for q in serp.get("related_questions", [])]

# After: ScrapingBot
import requests

res = requests.post("https://scrapingbot.io/api/v1/google", timeout=60,
    headers={"x-api-key": SCRAPINGBOT_KEY},
    json={"endpoint": "/search", "params": {"q": "best espresso machine", "gl": "us", "hl": "en"}})
serp = res.json()
results = [(r["position"], r["title"], r["link"]) for r in serp.get("organic", [])]
questions = [q["question"] for q in serp.get("peopleAlsoAsk", [])]

Run both side by side on a sample of your real queries for a week before you switch: compare positions, check the SERP blocks your code reads, and watch the time-sensitive queries. The Google results guide has a full rank tracker, and the RAG guide shows how to hand results to an LLM.

Common questions