Using Search Data APIs for Faster Prototyping

Real-time search results power everything from SEO dashboards and product research tools to AI experiments. But collecting that data yourself is messy — scraping, captchas, rotating proxies, and constant HTML changes.

That’s exactly the problem SerpApi, the web Search API set out to solve. It gives developers structured, real-time search data from Google and over 40 other platforms — with a single, consistent API call.

Before diving into code, let’s take a quick look at what makes scraping search engines so frustrating and why it’s worth doing right.

Why Scraping Search Engines Breaks So Easily

Try scraping Google once, and you’ll quickly see why most people give up. Even good scrapers fail after a few days because search results change constantly and every layer of protection fights automation.

• HTML changes all the time: A small update in Google’s structure can break your parser overnight.
• Anti-bot walls: Captchas, IP bans, and bot detection require proxies, rotations, and hours of maintenance.
• Location matters: The same keyword can show completely different results depending on your region and language.
• JavaScript rendering: Modern SERPs — Maps, AI answers, Shopping — load data dynamically inside a browser.

SerpApi handles all of that automatically. Each request runs in a real browser, uses geo-targeted proxies, solves captchas, and returns structured JSON you can plug straight into your code.

What Makes Search Data Worth It

Despite the hassle, search data is gold. It shows what people care about, what products lead the market, and how information surfaces online. With a reliable source like SerpApi, it becomes an instant data feed you can use anywhere.

• Web Search API: Automate SERP data collection for SEO tracking, analytics dashboards, and research projects — no captchas or parsing required.
  • Example: Monitor keyword rankings, featured snippets, and competitor visibility across multiple regions in real time.
• AI Search Engine API: Analyze or visualize how large language models shape Google’s AI Overviews to inform AI analytics and model training.
  • Example: Build dashboards comparing AI-generated summaries against traditional search listings in real time.
• AI SEO (GEO): Use geo-targeted search data to understand how AI answers and SERPs vary by location, device, or language — ideal for localized SEO campaigns.
  • Example: Measure AI-generated visibility and ranking performance across global markets and devices.
• Product Research API: Compare live pricing, reviews, and availability from Amazon and other marketplaces to uncover trends and market gaps.
  • Example: Identify trending products, compare sellers, and monitor real-time shifts in e-commerce data.
• Maps API: Find and filter local businesses from Google Maps with ratings, categories, and contact info for lead generation and local analytics.
  • Example: Build regional lead lists or power location-based dashboards with verified business data.
• AI Training & Data Analytics: Feed structured, real-world search data into models, dashboards, or experiments to enhance machine learning accuracy and insight generation.
  • Example: Train AI systems or data pipelines with fresh, labeled search and market data.

These use cases show how structured, real-time search data moves far beyond raw scraping, becoming a foundation for analytics, automation, and AI-driven insights across industries.

Next, we’ll look at how this works in practice. We’ll start with the Google Search API, the simplest way to fetch full search results, complete with titles, links, snippets, and rich elements, all in structured JSON.

Google Search API

The Google Search API is SerpApi’s flagship endpoint. It lets you fetch complete Google Search results — including organic listings, featured snippets, images, ads, knowledge graphs, and local packs — in real time.

You can access it from any programming language that supports HTTP requests (e.g., Python, JavaScript, Ruby, Go, or even cURL), since it’s just a standard API call that returns structured JSON. SerpApi also supports no-code tools such as n8n and Make.com, as well as integration with Google Sheets.

Each request follows the same simple pattern. You just need to change the engine parameter to switch between API endpoints, such as engine=google for Google Search or engine=google_ai_mode for Google AI Mode.

You can even send the GET request directly in your browser for quick testing:

https://serpapi.com/search?engine=google&q=best+laptops+2025&location=United+States&api_key=YOUR_API_KEY

Or, if you prefer the command line, here’s the equivalent cURL command:

curl -G "https://serpapi.com/search" \
  -d engine=google \
  -d q="best+laptops+2025" \
  -d location="United States" \
  -d api_key=YOUR_API_KEY

Let’s have a look at an example using Python. For that we’ll first install the package like this:

pip install google-search-results

Our request in Python looks like this:

from serpapi import GoogleSearch

params = {
  "engine": "google",
  "q": "best laptops 2025",
  "location": "United States",
  "api_key": "YOUR_API_KEY"
}

search = GoogleSearch(params)
results = search.get_dict()
print(results["organic_results"][0])

Check out more details on how to integrate with Python in this guide.

Response Example

The organic_results field contains the main list of standard Google Search results. Each entry includes structured data such as title, link, snippet, and position, plus optional fields like thumbnails, ratings, and rich snippets.

"organic_results": [
  {
    "position": 1,
    "title": "Best Laptops 2025 - Top Picks and Reviews",
    "link": "https://example.com/best-laptops-2025",
    "displayed_link": "https://example.com › best-laptops-2025",
    "snippet": "Discover the top laptops of 2025, featuring models from Apple, Dell, and Lenovo...",
    "sitelinks": {
      "inline": [
        { "title": "Apple MacBook Air M3", "link": "https://example.com/apple-macbook-air-m3" },
        { "title": "Dell XPS 13", "link": "https://example.com/dell-xps-13" }
      ]
    }
  }
]

This structured output makes it easy to integrate SerpApi into any stack or workflow — from a quick prototype to a large-scale SEO tool or research platform.

💡 Pro Tip: You can use additional parameters like hl (language), gl (country), or start (pagination) to fine-tune your queries and replicate real Google Search behavior precisely.

Common use cases are: rank tracking, SEO monitoring, keyword research, and competitive analysis.

You can find out more here: Google Search API documentation

Google AI Mode API

Google’s new AI Mode introduces an experimental search experience powered by large language models — generating summarized, conversational answers above traditional search results.

SerpApi’s Google AI Mode API captures this AI-powered view, returning the generated summaries, text blocks, references, and any inline media in clean JSON format — ready for integration into your own apps or dashboards.

As mentioned before, you can access it from any programming language that supports HTTP requests using the engine=google_ai_mode parameter.

Example in Python

from serpapi import GoogleSearch

params = {
  "engine": "google_ai_mode",
  "q": "how does solar power work",
  "api_key": "YOUR_API_KEY"
}

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

for block in results.get("text_blocks", []):
    print(block.get("snippet"))

Response Example

The response includes AI-generated content as structured text blocks, along with supporting references and media. Each text block represents part of Google’s AI-generated summary — a paragraph, heading, list, table, or even embedded code.

{
  "text_blocks": [
    {
      "type": "paragraph",
      "snippet": "Solar power works by converting sunlight into electricity using photovoltaic cells, which are typically made from silicon."
    },
    {
      "type": "heading",
      "snippet": "Key Components"
    },
    {
      "type": "list",
      "list": [
        { "snippet": "Solar panels: capture sunlight and generate direct current (DC)." },
        { "snippet": "Inverter: converts DC into usable alternating current (AC)." },
        { "snippet": "Battery storage: saves excess energy for later use." }
      ]
    }
  ],
  "references": [
    {
      "title": "How Solar Energy Works - U.S. Department of Energy",
      "link": "https://www.energy.gov/solar/how-solar-energy-works",
      "source": "energy.gov"
    }
  ]
}

This structured JSON lets developers extract and visualize Google’s AI-generated search summaries, compare them against traditional SERP results, or analyze how AI-mode answers evolve across topics.

Common use cases:

• Comparing AI-generated summaries vs. classic search results
• Monitoring how Google’s AI cites or links to sources
• Building dashboards or trend analysis tools around AI search behavior
• Studying how AI overviews structure content by topic

💡 Pro Tip: You can combine google_ai_mode results with classic google or google_news results to track how AI responses evolve alongside traditional organic search visibility.

Read the Google AI Mode API documentation for more details.

Google Maps API

The Google Maps API from SerpApi lets you extract live business listings, ratings, reviews, and location data directly from Google Maps — all returned as structured JSON you can query or visualize instantly.

This is especially useful for local SEO monitoring, lead generation, competitor research, or building location-based apps without needing to maintain complex scraping infrastructure.

The API replicates any Maps search you’d perform manually — like “coffee near me” — and returns business details such as name, address, rating, hours, and coordinates.

Example in Python

from serpapi import GoogleSearch

params = {
  "engine": "google_maps",
  "q": "coffee",
  "ll": "@40.7455096,-74.0083012,14z",  # New York City
  "type": "search",
  "api_key": "YOUR_API_KEY"
}

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

for shop in results.get("local_results", []):
    print(f"{shop['position']}. {shop['title']} – {shop.get('rating', 'N/A')}⭐")

Response Example

The API returns a local_results array containing detailed business data from Google Maps.

"local_results": [
  {
    "position": 1,
    "title": "Grace Street Coffee & Desserts",
    "rating": 4.5,
    "reviews": 3108,
    "type": "Coffee shop",
    "address": "17 W 32nd St, New York, NY 10001",
    "phone": "(917) 540-2776",
    "website": "https://www.bygracestreet.com/",
    "open_state": "Closes soon ⋅ 11 PM ⋅ Opens 11 AM Thu",
    "gps_coordinates": {
      "latitude": 40.7477172,
      "longitude": -73.9865302
    },
    "service_options": {
      "dine_in": true,
      "takeout": true,
      "no_contact_delivery": true
    }
  }
]

Each result includes the business’s name, category, rating, total reviews, hours, address, phone, and coordinates, along with structured subfields for operating hours, service options, order links, and photos.

Common Parameters

Use Cases

• Local SEO tracking: Monitor top businesses for target keywords in specific cities or zip codes.
• Lead generation: Collect names, phone numbers, and websites of local businesses automatically.
• Market intelligence: Compare ratings and review counts for competitors.
• Mapping & visualization: Combine gps_coordinates data with GIS or mapping tools.

💡 Pro Tip: Results may vary slightly depending on map zoom (z value) and Google’s local ranking logic. For more structured business details like reviews or photos, you can combine this with the Google Maps Reviews API or Photos API.

Amazon Search API

The Amazon Search API lets you fetch structured product data directly from Amazon’s search results — including titles, prices, ratings, delivery info, and seller details.

Instead of parsing HTML manually, SerpApi returns everything in clean JSON, so you can focus on analyzing data rather than maintaining scrapers. It’s ideal for price comparison tools, market research, competitive intelligence, or e-commerce analytics.

Example in Python

from serpapi import GoogleSearch

params = {
  "engine": "amazon",
  "k": "wireless headphones",
  "api_key": "YOUR_API_KEY"
}

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

for product in results.get("organic_results", []):
    print(f"{product['title']} - {product.get('price', {}).get('raw', 'N/A')}")

Response Example

The organic_results array contains structured product listings from Amazon’s search results. Each item includes key details like title, price, rating, and thumbnail.

"organic_results": [
  {
    "position": 1,
    "title": "Sony WH-1000XM5 Wireless Noise-Canceling Headphones",
    "link": "https://www.amazon.com/dp/B09NM4R3NX",
    "thumbnail": "https://m.media-amazon.com/images/I/71o8Q5XJS5L._AC_SX679_.jpg",
    "price": {
      "raw": "$398.00",
      "extracted": 398.00
    },
    "rating": 4.7,
    "reviews": 12500,
    "badge": "Best Seller",
    "delivery": "FREE delivery Thu, Oct 17",
    "availability": "In Stock"
  }
]

This structured format makes it easy to compare prices, extract product data, or monitor competitors at scale.

Use Cases

• Price comparison – Track product pricing across time or categories
• Market research – Analyze competitor offerings or discover emerging brands
• E-commerce analytics – Monitor best sellers and review trends
• Product catalog enrichment – Automatically collect product info for listings

💡 Pro Tip: You can refine results using additional parameters such as page, amazon_domain (e.g., amazon.co.uk), or sort (price-asc-rank, price-desc-rank, review-rank, etc.) to target specific regions or sorting orders.

Find out more in the Amazon Search API documentation.

See the APIs in Action

You can explore these APIs in action using SerpApi’s interactive Playground, where you can test queries and instantly see structured JSON results.

Conclusion

Scraping search data doesn’t need to be complicated, unreliable, or risky. With SerpApi, you can access structured, real-time results from Google, Amazon, Maps, and dozens of other engines — all with a single, consistent API call.

Whether you’re building SEO tools, analyzing markets, or powering AI systems, SerpApi gives you accurate data from real browser sessions, at scale, and without the hassle.

Explore more by signing up at SerpApi to get free 250 searches/month or jump straight into the API Playground to test it yourself.