Beyond Search Engines: Amazon, Walmart, App Store, eBay, YouTube, Tripadvisor, Yelp APIs

The same providers that scrape Google search also scrape major marketplaces. Same API key, same JSON shape conventions, different engine parameter. This widens the "SERP-API" category from "search engines" to "any structured search-style page on the web."

This lesson tours the most common ones.

Amazon

• Provider engines: engine=amazon or similar. Returns product search results, product detail data, reviews, sellers.
• Use cases: price tracking, BSR (Best Sellers Rank) monitoring, review scraping, buy-box analysis, A9 ranking research, competitor product monitoring.
• Data depth: product search returns title, price, rating, review count, ASIN, image, Prime status. Product detail returns full description, features, variations, frequently bought together, sponsored offers.

data = search("noise-cancelling headphones", engine="amazon", gl="us")
for p in data["organic_results"]:
  print(p["asin"], p["title"], p["price"], p["rating"])

Amazon is the heavyweight. Hundreds of companies build on Amazon SERP-API data.

Walmart

• Provider engines: engine=walmart.
• Use cases: competitive pricing in US retail, marketplace seller monitoring.
• Data depth: product search, ratings, prices, availability, Walmart-specific fields (rollback prices, pickup availability).

App Store / Google Play

• Provider engines: engine=apple_app_store, engine=google_play.
• Use cases: ASO (App Store Optimization), keyword ranking, review monitoring, competitor app analysis.
• Data depth: app rank for a search term, ratings, install count (Google Play), reviews, screenshots, similar apps.

data = search("photo editor", engine="apple_app_store", gl="us")
for app in data["organic_results"][:5]:
  print(app["title"], app["rating"], app["reviews"])

eBay

• Provider engines: engine=ebay.
• Use cases: used-goods market analysis, vintage/collectibles pricing, seller monitoring.
• Data depth: auction listings, BIN prices, sold-listings data (for completed prices), seller feedback.

eBay is unique: BOTH live listings and completed-sale prices are valuable. The latter is harder to scrape on regular eBay, SERP APIs surface it cleanly.

YouTube

• Provider engines: engine=youtube.
• Use cases: video search rank tracking, influencer monitoring, trending topic discovery.
• Data depth: video search results with title, channel, view count, duration, upload date.

Distinct from the YouTube Data API (Google's official), SERP-API approach doesn't need a Google Cloud project and is quota-free (you pay per call).

Tripadvisor

• Provider engines: engine=tripadvisor.
• Use cases: hospitality monitoring, hotel review tracking, restaurant ranking, attraction popularity.
• Data depth: review counts, ratings, price ranges, availability.

Yelp

• Provider engines: engine=yelp.
• Use cases: local business monitoring, restaurant review tracking, competitor analysis for local SMBs.
• Data depth: business cards with name, rating, reviews, price level, categories, address.

Other engines worth knowing

Most providers also cover:

• Home Depot, Lowe's, home improvement retail.
• Best Buy, electronics retail.
• Etsy, handmade/vintage marketplace.
• Booking.com, Hotels.com, hospitality.
• Indeed, LinkedIn, jobs (LinkedIn often as a separate, premium-priced engine).
• Instagram, TikTok, Reddit, social (rare; usually specialized providers).

Coverage varies. Read your chosen provider's engine list.

Pricing variations

Some engines cost more than the standard Google SERP call:

• Premium engines (LinkedIn, sometimes Amazon, Tripadvisor): 2–3x standard pricing.
• Standard engines (Bing, DuckDuckGo, Brave): same as Google.
• Specialized data (BSR, completed eBay sales, app store reviews): sometimes extra.

Always check the provider's pricing page for your specific engines.

Schema differences

JSON shapes vary by engine, even with the same provider. For Amazon, you'll see fields the SERP doesn't have: asin, bestseller_rank, prime_eligible, variations. Walmart adds walmart_id, pickup_today. App Store adds bundle_id, developer, screenshots.

Build your normalizer with engine-aware branching:

def normalize_product(p, engine):
  base = {
  "id": _engine_id(p, engine),
  "title": p.get("title"),
  "price": p.get("extracted_price") or p.get("price"),
  "rating": p.get("rating"),
  "review_count": p.get("reviews") or p.get("review_count"),
  "url": p.get("link") or p.get("url"),
  }
  if engine == "amazon":
  base["asin"] = p.get("asin")
  base["bsr"] = p.get("bestseller_rank")
  base["prime"] = p.get("prime_eligible", False)
  elif engine == "walmart":
  base["walmart_id"] = p.get("walmart_id")
  base["pickup_today"] = p.get("pickup_today", False)
  return base

When this category beats specialized scrapers

• You're tracking many marketplaces. One provider, one API key, eight engines → less ops overhead than maintaining specialized scrapers.
• You don't need real-time. SERP-API freshness is "live at fetch time" but you typically poll, not stream.
• You're not doing anything that violates the marketplace's ToS. Like Google, marketplaces have ToS. SERP-APIs take some of the risk.

When to use a specialized scraper

• Real-time pricing at scale. Some price-intelligence companies maintain their own Amazon/Walmart scrapers because they need second-level freshness.
• Domain-specific depth. A Yelp-only review analytics platform may need data the generic SERP-API doesn't expose.
• Workflow tightly coupled to platform. E.g. an Amazon-only SaaS that builds on top of MWS/SP-API where applicable.

Hands-on lab

Pick one marketplace engine relevant to your interest (Amazon for ecom, App Store for mobile, YouTube for content, Tripadvisor for hospitality). Run 10 representative queries through your SERP-API in that engine. Compare the JSON shape against Google's. Build a small normalizer for the engine-specific fields. You've extended your SERP scraper into a multi-platform data tool.