10 Best SERP Scraper APIs & Google SERP API Alternatives

We benchmarked the best SERP scraper APIs using 18,000 requests across 3 leading search engines: Google, Bing, and Yandex. Each SERP API was tested for speed, accuracy, and reliability.

Follow the links below to learn why these solutions are the top SERP APIs. They are also great SerpApi alternatives which is another SERP API:

Best SERP Scraper APIs: Google, Bing & SerpApi alternatives

SERP scraper API benchmark results

Compare providers’ median response time and the average number of fields that they returned in our benchmark:

We run live requests every 15 min, caching off, with a 60s timeout, using 250+ queries across Google, Bing, and Yandex in the United States.

• Success Rate: closer to 100% = more reliable; dips indicate blocking/outages.
• Average Response Time: lower = faster; read it alongside success rate (fast but frequently failing ≠ good).

The chart shows the daily success rate and average response time (successful calls only).

For detailed information, see methodology.

Feature comparison of the best SERP APIs

Bright Data SERP API is a multi-engine, pay-per-success scraping tool that delivers structured results from all major search engines, including Google, Bing, Yahoo, DuckDuckGo, Yandex, Naver, and Baidu, as well as rich verticals such as Images, Google Maps, Shopping, and Hotels.

Key endpoints

• Google Search (Organic, Ads, Images, Maps, Trends, Hotels, Flights)
• Bing Search (Organic, Ads, Local)
• Yahoo, DuckDuckGo, Yandex, Naver, Baidu: Same schema for easy switching

Pros

• Granular geo & device targeting: Country, city, specific browser, and device profile for each call.
• Built-in anti-bot solutions: Rotating residential and ISP proxies plus automatic CAPTCHA solving.

Cons

• Pricing and onboarding skew towards the enterprise: The entry-level subscription is $499 per month.
• Complex feature set: Advanced parameters, such as async jobs and enhanced ad flags, add a learning curve for small teams.

Oxylabs SERP scraper API enables users to choose between raw HTML or parsed JSON, covering every key Google surface, organic results, Ads, Images, Maps, News, Travel/Hotels, Trends, and Lens. The SERP API supports a unified request schema that works for Bing, Baidu, and Yandex.

A one-week free trial is available, offering 5000 results. Oxylabs SERP scraper API pricing includes on-demand usage, which is billed at approximately $1.60 per 1,000 successfully delivered results. There are no extra fees for JavaScript rendering or proxies.

Pros

• Unified schema across engines: Change a single parameter to pivot from Google to Bing or Baidu, and the parsed JSON structure stays identical.
• Granular targeting: Country, state, city, and lat/long coordinate targeting
• Anti-bot stack: Proxy rotation, browser fingerprinting, and built-in CAPTCHA solving.

Cons

• Engine coverage is Google-centric.
• There is no true pay-as-you-go credit pack.

Decodo offers a SERP scraping API that integrates its SERP capabilities into its general-purpose web scraping API, which includes a built-in proxy network, CAPTCHA bypass, and real-browser rendering.

Decodo’s search engine endpoints include the Google Search Scraper and the Bing Search API. The SERP API supports Google in-depth and Bing (results, snippets, and rankings). All billing is a pay-on-success model.

Pros

• Geo and device granularity: City coordinates, desktop, and specific mobile OS profiles.
• Dual request modes: Real-time calls for instant data or asynchronous calls you can schedule and fetch later.

Cons

• It is a Google-centric API.
• There is no pay-as-you-go credit model.

NetNut provides a dedicated SERP API for Google. The tool enables users to specify the Google domain they want to target, for example, google.co.uk or google.fr. The entry plan, which costs $100,000, yields results within a month at a rate of $1.50 per $1,000.

Pros

• Users can customize their requests, including device type (mobile or desktop), the language of localized search results, the localization country, and the search type (organic results, ads, or images).
• Country, city, and coordinate-level targeting, plus desktop or specific mobile profiles.

Cons

• They offer an enterprise-level entry price.
• There’s no pay-as-you-go credit pack.

Nimbleway offers a full-stack scraping pipeline (proxy network, browserless rendering, and unblocker) that supports various targets, including search engine results pages. The SERP scraping API supports Google, Bing, and Yandex.

Pros:

• Dedicated and rotated IPs: Built-in dedicated residential proxies.
• Zip code-level targeting: Collect Google SERP data for a specific zip code area.

Cons:

• The unit cost is higher than that of enterprise-oriented providers like Bright Data and Oxylabs. Heavy users may still find Bright Data or Oxylabs cheaper at scale.

DataForSEO SERP API provides a unified request schema for Google, Bing, YouTube, Yahoo, Baidu, Naver, and Seznam. The company specializes in delivering SERP data solutions to SEO professionals and marketers.

Pros:

• You can take a screenshot of a Google page.
• The provider offers an AI Summary endpoint feature that summarizes search engine result pages and provides an LLM-generated synopsis for a fee.

Cons:

• Live mode costs 3-4 * the base rate.
• Not PAYG credits

Serpstack is a search-scraping platform that offers Google search API to scrape Google search results data in real-time. The tool includes built-in proxy rotation and automatic CAPTCHA handling.

Pros:

• Navigates to subsequent pages and extracts structured data from search engines.
• Offers a dedicated location API that lets you specify the exact location to which you intend to send a request.

Cons:

• If you intend to scrape hundreds of thousands of keywords, it would not be the most cost-effective option at scale.

ScraperAPI offers Google SERP API with built-in proxy servers. It extracts structured JSON data from Google search results. ScraperAPI doesn’t provide API endpoints for any search engines other than Google.

Pros:

• Ability to perform JavaScript rendering. It retrieves and returns JavaScript-heavy search result pages.
• Offers a free plan with a 7-day trial with 5,000 credits

Cons:

• If you need full-browser rendering for every request, the unit cost rises on heavy pages.

SEO tools for a one-stop shop SEO data solution

Semrush offers several API endpoints that allow users to retrieve web data, including SERP rankings, domain analytics, and keyword insights. The SEMrush API Batch Keyword Overview feature allows access to historical data.

Key features:

• Obtain keyword metrics at both national and local levels.
• Gather organic and paid search results.
• Perform bulk analysis of collected SERP data.

SE Ranking API

SE Ranking provides an API that retrieves the top 100 Google search results.

Key features:

• Gathering historical data for keyword research
• Accessing detailed SERP information based on specific locations, devices, and search engines
• Extracting particular SERP elements like featured snippets, local packs, and ads.

Free SERP API options & open-source alternatives

API free tiers:

Many of the paid SERP scrapers listed above, such as SerpApi (200 free requests/month) and DataForSEO (50,000 free credits), offer free tiers or trials for testing low-volume SERP data collection.

Web scraping libraries:

For developers with technical expertise, you can build your own basic SERP scraper using Python or JavaScript/Node.js. However, there are some limitations, such as IP blocking, CAPTCHA, and scalability. This approach is not scalable for high-volume data collection or critical production environments.

Open-source tools:

While there isn’t a widely maintained “free SERP API” in the same vein as the commercial ones, some open-source web scraping frameworks can be adapted:

• Scrapy: A robust framework for building complex web crawlers. You’d still need to integrate proxy management and CAPTCHA solutions.
• Requests-HTML: A library that aims to make HTML parsing and interaction more intuitive, including JavaScript support.

How to scrape Google AI Mode using Python

Scraping Google is now more difficult, especially with the new Google AI Overview and AI Mode features. Traditional Python scrapers or SERP scraper APIs are often blocked after a few requests due to Google’s advanced detection systems.

With regular Selenium, you might get only a few searches before facing CAPTCHA. This is not scalable when extracting hundreds of AI Overview results for SEO or research.

To handle this, we use Bright Data’s Web Unlocker, a tool for Google AI scraping. It rotates millions of real IPs, bypasses Google’s bot detection, and automatically solves CAPTCHA. This enables smooth scraping of AI-generated results, SERP data, and AI Mode snippets.

Step 1: Install the required Python libraries

Step 2: Importing libraries for Google AI Mode scraping

First, we’ll set up our Python Google scraper by importing the necessary libraries. These libraries automate browser actions, send requests, and process data for scraping Google AI Mode and AI Overview results.

We use Selenium to automate Chrome and interact with Google Search. The webdriver_manager tool automatically downloads ChromeDriver, eliminating the need for manual setup.

We use the requests library to access the Web Unlocker API, which manages CAPTCHA and reduces detection risk. The JSON library processes and loads HTML data from Web Unlocker into Selenium.

Step 3: Defining API information

These values connect your SERP Scraper API with Bright Data Web Unlocker for authenticated scraping.

Step 4. Creating the main class

Initialize the class with two options:

• headless=True runs Chrome in invisible mode, useful when working on servers.
• debug=True enables detailed console logs for troubleshooting.

Use headless=False during testing to see browser actions, then switch to True for automation.

Step 5. Setting up the Chrome browser

This method configures Chrome browser options for web scraping.

• The options –no-sandbox and –disable-dev-shm-usage help avoid errors that can occur on Linux servers.
• The –disable-gpu option turns off GPU rendering, which is unnecessary when running in headless mode.
• Setting –window-size=1920,1080 keeps the viewport consistent and helps prevent layout changes in Google’s interface.

These settings optimize performance and stability when running the SERP Scraper API in headless mode.

These settings help avoid Google’s bot detection:

• excludeSwitches hides automation flags.
• Using disable-blink-features=AutomationControlled prevents Chrome from showing that Selenium is in use.
• Setting a custom user-agent helps the browser look more like a regular Chrome session.

This section automatically installs and initializes ChromeDriver.

• implicitly_wait(10) waits up to 10 seconds for elements to load.
• set_page_load_timeout(60) gives the browser extra time to load pages when using Bright Data Web Unlocker, which can sometimes be slower.
• The script opens Google’s homepage and then pauses briefly to ensure the browser is ready to use.

Step 6. Debug logging function

This simple helper function handles debug logging. It prints messages only if debug mode is on. This lets you follow what the scraper is doing without filling up the console during regular use.

This feature helps when you need to troubleshoot, check how ChromeDriver works, or watch API calls in the SERP Scraper setup.

Step 7: Fetching HTML with Web Unlocker

• The API token in the header authenticates your request.
• zone specifies the Web Unlocker zone.
• format: “raw” requests direct HTML output.
• Setting the country to “us” uses U.S. IP addresses and returns search results in English.

If the request succeeds, it returns the HTML and True. Otherwise, it returns None and False. If Web Unlocker does not work, the scraper can switch to Selenium to ensure smooth scraping.

Step 8. Searching on Google

This creates the Google Search URL. Spaces in the query are replaced with +, and &hl=en ensures English-language results.

Here, we first try to fetch the HTML via Bright Data Web Unlocker. If this works, the content loads directly in the browser with JavaScript, so there is no need to send requests to Google. If Web Unlocker fails, the scraper uses Selenium for standard navigation.

This part addresses Google’s cookie consent pop-up, which sometimes appears before the search results. The scraper looks for button options used in different regions and clicks Accept if it finds one.
If there is no pop-up, the code continues as usual.

Step 9. Clicking the “show more” button

This function clicks the “Show more” button to reveal full AI Overview content. The function checks several XPaths because Google’s layout changes frequently. When it finds the button, it uses JavaScript to click it. It then waits a few seconds for the content to appear.

Step 10. Expanding sources

This step expands all source links inside the AI Overview. The “Show all” button appears only after you select “Show more.” For this reason, this function runs second and uses a method similar to the previous step.

Step 11. Extracting Google AI mode

First, it checks if “AI Overview” exists on the page. If found, it clicks both buttons to expand the entire content and sources.

This locates the main AI Overview container by checking parent elements with enough text content to ensure it’s the correct section.

The text is cleaned, split, and filtered to remove short or empty lines as well as the title.
Finally, it keeps the first 20 lines for a clear, structured AI Overview summary.

Step 12. Extracting sources

Invalid or internal links (Google, YouTube) are skipped to keep only real external sources.

If the link text is too long or contains nested elements, it extracts and cleans innerHTML to get a readable title.

If no valid text is found, it uses the domain name as the title or defaults to “Source”.

Each source is added to the list, limiting titles to 100 characters and URLs to 200 for consistency.

Duplicate URLs are removed using a set-based filter, ensuring unique sources remain.

The function creates a dictionary that includes the AI Overview content and a neatly formatted list of sources, making it easy to use or save.

Step 13. Analyzing the query

This function combines all previous steps. It performs the Google search, then extracts the AI Overview content and sources. If something goes wrong, it returns empty results instead of causing an error.

Step 14. Closing the browser

Closes the ChromeDriver instance after scraping completes. The try-except block prevents the program from crashing if the browser is already closed.

Step 15. Main function

Here, we initialize the GoogleAIOverviewScraper object. Setting headless=False displays the browser window during testing.

The scraper prints each query, the AI Overview content, and any sources it finds.

In the finally block, the browser is always closed. The final condition makes sure the script runs when you execute it directly. This acts as the entry point for the SERP Scraper AI Overview module.

What is SERP API?

SERP scraper APIs are interfaces that enable users to automatically extract search engine results for a specific query.

They are sometimes referred to as web search APIs, as both enable access to search engine data. However, their technical implementations and use cases differ.

While general-purpose search APIs provide structured, often officially supported access to search results through a predefined index, SERP scraper API services offer more granular control by extracting data directly from live result pages, including ads, featured snippets, and organic rankings.

If the target site or search engine provides an official search API, it may be preferable for applications where compliance is a top priority.

SERP scraper API benchmark methodology

Multiple search engine results page (SERP) providers are evaluated in comparison to Google, Bing, and Yandex. Caching is disabled to ensure that each request retrieves current results.

A pool of over 250 unique queries is used to generate more than 900 URLs across the evaluated search engines. Each iteration randomly samples one or more queries from this pool.

Requests are executed at 15-minute intervals. Each request is subject to a 60-second timeout period.

During each iteration, a provider is paired with a supported search engine, and the corresponding live search URL is retrieved. The benchmark is conducted in single mode.

A response is considered successful if it meets the following criteria:

1. The response returns non-empty data, and
2. At least one engine-specific Cascading Style Sheets (CSS) selector is present in the returned HTML.

The following CSS selectors are used for validation:

• Google: .tF2Cxc, .yuRUbf, #search
• Bing: .b_algo, .b_caption, .b_title
• Yandex: .serp-item, .Organic, .content__left

The following metrics are recorded:

• The daily success rate is calculated as the number of successful requests divided by the total number of requests, multiplied by 100, both per engine and overall.
• The average response time is computed as the mean of successful request response times only.
• Timeout frequency and error distribution are also monitored throughout the evaluation process.

All errors encountered during the process are logged.

• The types of errors recorded include timeouts (fixed at 60 seconds), network or connection errors, parsing or decoding failures, and empty responses.

The following fields are recorded for each request:

• query, search_engine, url, success (1/0), response_time_s, mode=single, batch_id.

FAQs about Google scraping

Reference Links

1.

https://policies.google.com/terms

Industry Analyst

Gulbahar Karatas

Industry Analyst

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Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security.

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