Tested the Best SERP Scraper APIs in 2026

We benchmarked the leading SERP providers using 18,000 live requests across Google, Bing, and Yandex. See the top 6 providers outperforming in our speed and data richness tests:

SERP scraper API benchmark results

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

1,200 queries were used in this benchmark, including a total of 200 query result pages from the two main search engines: Google.com and Bing.com. Queries were the top 100 Google searches in the United States. Read the full methodology.

Stability of SERP scraper solutions

We have run 18,000 queries in this benchmark so far. We run live requests every 15 min, with caching disabled, a 60s timeout, and 250+ queries across Google, Bing, and Yandex in the United States.

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

Pricing of the top 6 SERP scrapers

Pros and cons of the best SERP scraper APIs

Bright Data SERP API is the most powerful tool. In our tests, it returned 2x as many data fields (maps, coordinates, rich snippets) as the industry average. It is the go-to choice if you need deep, structured data rather than simple titles and URLs.

Pros:

• Unmatched data depth: Captures granular details that others miss.
• Massive scale: Built-in proxy network handles millions of requests.
• Global coverage: Targets any country, city, or coordinate.

Cons:

• Learning curve: The dashboard is feature-rich and can be overwhelming for beginners.

In our test, Oxylabs proved to be the most stable provider. While it doesn’t top the chart for raw speed (like Zyte) or data depth (like Bright Data), it delivers consistent results with a near-zero failure rate.

It strikes the perfect balance for large-scale projects where reliability is more critical than millisecond speed.

Pros:

• Unified schema: Switch from Google to Bing scraping by changing one parameter.
• Granular targeting: Scrape SERP data down to specific cities or coordinates.

Cons:

• Google-centric: While it covers others, its features are heavily optimized for Google.

Decodo is the best low-cost alternative. In our tests, it delivered performance close to that of the premium providers (~0.4s slower than Oxylabs) at a fraction of the cost.

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:

• Cost-effective: One of the cheapest entry points in the market ($29).
• Granularity: Supports city-level and coordinate-based targeting.

Cons:

• Google-Centric: Focuses heavily on Google; other engines are secondary.

SerpApi is a SERP data provider focused on high-speed retrieval and broad engine support. According to our benchmarking data, the service maintained a consistent average response time of approximately 2ms, making it the fastest provider during that period.

While some tools are limited to standard search results, SerpApi provides structured data for niche engines, including Google Scholar, Google Flights, and YouTube.

Pros:

• Measured speed: Recorded a stable average response time of ~2ms in our latest performance tests.
• Engine range: Supports data extraction from Google (Images, News, Shopping, Scholar, Flights), YouTube, Yahoo, and Baidu.
• Developer-friendly: Documentation and libraries for Python, PHP, Ruby, and Go.

Cons:

• Using the basic features is easy, but making the most of the advanced options and understanding the different results for each search type take some time.

If your application requires real-time data (e.g., a user clicks a button and waits for a result), Zyte is the winner. In our tests, it clocked a median response time of under 1.5 seconds.

Pros:

• Speed: The fastest option on the market, period.

Cons:

• Cost: Achieving this speed can be pricier than standard proxy-based solutions.
• Data depth: Returns standard SERP data; less granular than Bright Data.

Apify offers a platform where you can rent ready-made “Actors” (scrapers) for Google Maps, Shopping, and more.

Pros:

• Low entry: You can start for as little as $5/month.
• Community: Excellent open-source libraries and developer support.

Cons:

• Latency: Not recommended for real-time, user-facing applications due to slower response times.

Our benchmark showed NetNut to be slower than the market leaders, and the pricing model is strictly geared towards enterprise users with high budgets.

Pros:

• ISP network: Accessing real ISP IPs reduces the chance of being flagged as a bot.

Cons:

• High entry barrier: Not suitable for small projects or individual developers.
• Speed: Slower response times compared to Zyte or Bright Data in our tests.

AI-powered SERP scraping

SERP AI Tools use machine learning to adjust to changing layouts in real time, unlike older systems that rely on fixed selectors. Switching to SERP automation offers several benefits:

• AI tools can read page structures, so your scraper keeps working even if Google moves featured snippets or ad blocks.
• In addition to retrieving HTML, these tools use LLMs to summarize results and explain why certain pages rank higher.
• Automation means you do not have to update things manually.

SERP scraper benchmark methodology

Multiple search engine results page (SERP) providers are evaluated against 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.
• 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.

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Cite this research

Pick the format that matches where you're publishing. Pasting the link version into your CMS preserves the backlink.

@misc{karatas2026,
  author = {Karatas, Gulbahar},
  title  = {{Tested the Best SERP Scraper APIs in 2026}},
  year   = {2026},
  month  = jun,
  howpublished    = {\url{https://aimultiple.com/serp-scraper-api}},
  note   = {AIMultiple. Retrieved June 2, 2026}
}

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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