ScrapingBee Review 2026: Benchmark Tests, Pricing & Top Alternatives

Federico Trotta

Updated Oct 12, 2026

Sep 30, 2026

ScrapingBee is a web scraping platform that helps developers extract data without managing the infrastructure. Its APIs can return raw or rendered HTML, extract selected fields using CSS, XPath, or AI instructions, and provide structured data through dedicated endpoints for supported sources.

However, not every web-data workflow needs a predefined JSON response. Sometimes, the main challenge is reaching the target page in the first place. This is because the page may require JavaScript rendering, a residential IP, CAPTCHA handling, geographic targeting, or a consistent session.

This is where Gologin Web Unlocker takes a different approach. Instead of focusing primarily on predefined data schemas, it unlocks and renders supported target pages, then returns their content as HTML, Markdown, text, or JSON with metadata. Then. you can use that output with your own parser, database schema, AI agent, or research workflow.

This difference is important when comparing the two products. ScrapingBee can take you further into the extraction process, especially when one of its dedicated APIs or extraction features matches your requirements. Gologin Web Unlocker is designed to give you reliable access to the page itself, with the flexibility to decide what happens next.

In this review, you will explore ScrapingBee’s features and performance through five hands-on tests. You will also compare its page-retrieval and extraction workflows with Gologin Web Unlocker to understand when Goloing is a better choice.

ScrapingBee vs. Gologin Web Unlocker: Key Takeaways

ScrapingBee and Gologin Web Unlocker overlap in some features, but they are optimized for different stages of a web-data workflow:

ScrapingBee combines page retrieval with several extraction options: You can request raw or rendered HTML, apply CSS/XPath rules, use AI extraction, or call dedicated APIs that return structured data for supported sources.
Gologin Web Unlocker focuses on unlocking and rendering the page: It handles the access layer and returns the target content as rendered HTML, Markdown, text, or JSON with metadata.
Choose ScrapingBee when you want ready-to-use structured data: Its dedicated APIs and extraction features are useful when you already know which fields you need, and the target source is supported.
Choose Gologin when page access is the main challenge: It is a strong fit when JavaScript rendering, residential IPs, CAPTCHA handling, geographic targeting, or page-level access matters more than receiving a predefined schema.
Gologin gives freelancers and teams more control over downstream processing: You can retrieve a page once and decide whether to parse it with your own code, convert it into a database record, send it to an AI model, or preserve it for later analysis.
Gologin is particularly useful for supported targets such as Google Search, Amazon, LinkedIn, Zillow, and Reddit: You can retrieve localized or rendered page content without building separate access workflows for each target.
Markdown is useful when the output is intended for an AI or research workflow: Instead of forcing a page into a rigid schema, Gologin can provide readable content that preserves the broader context of a search result, listing, company page, or discussion.
Neither product is the universal winner: ScrapingBee is attractive when you want a structured extraction workflow. Gologin is the better choice when you need reliable access to supported pages and want to control the extraction process yourself.

What Is ScrapingBee?

ScrapingBee provides managed web scraping APIs for developers and teams that want to extract data without maintaining their own infrastructure.

Its offering can be divided into three approaches

Approach	What you request	What you receive	Who defines the extraction?
HTML API	A target page and retrieval options	Raw or rendered page content	Your application, if extraction is needed
Rule-based or AI extraction	A page plus selectors or extraction instructions	Selected structured fields	You define the desired fields; the service performs extraction
Dedicated scraper API	A request to a supported source-specific endpoint	Structured source-specific data	The endpoint provides the supported schema

This means that using ScrapingBee does not necessarily eliminate parsing. If you request HTML, your application still needs to extract fields unless whole-page content is the desired output.

ScrapingBee Features Overview and Full API Parameters Reference

ScrapingBee packs the following into one API:

HTML API: You provide the target URL and get back raw or rendered HTML. No need to stand up your own fetching layer, handle retries, or figure out why a request failed.
JavaScript rendering: By default, ScrapingBee fetches web content via a headless browser. That executes the JavaScript code on the page, so you’re not scraping empty divs. You can wait for specific selectors to appear before ScrapingBee returns the response, which avoids the flaky timing issues you would otherwise handle yourself with Puppeteer or Playwright.
Custom scenarios: Define a sequence of clicks, scrolls, and form fills, and ScrapingBee executes them in a real browser before extraction. This covers scenarios like infinite scroll and multi-step forms without maintaining browser automation scripts or session state.
Screenshots: You can request a screenshot alongside your scrape to get a visual snapshot of what the browser rendered. This is useful for debugging failed extractions, verifying anti-bot bypass actually worked, or building visual monitoring into a pipeline without adding a separate screenshot service.
CSS/XPath extraction: Pass selector rules in the request and get back only the fields you need, already parsed. This allows you to skip downloading full HTML and writing your own parsing logic downstream.
AI extraction: Describe the fields you want in plain language instead of writing and maintaining selectors. When a site’s markup changes and breaks your CSS rules, AI extraction adapts without a code deploy.
Proxy rotation: Requests route through rotating residential and datacenter proxies automatically, with premium and stealth options for sites running aggressive anti-bot detection.
Geotargeting: Set a country parameter and the request routes through a proxy in that location, returning geo-specific content like localized pricing or region-locked pages.
Dedicated scraper APIs: ScrapingBee offers purpose-built endpoints for Amazon, Google Search, YouTube, Walmart, ChatGPT, and Gemini that return structured data.
No-code integrations: Native connections to Zapier, Make, and n8n let non-engineers or lightweight workflows pull scraped data into existing automations.
MCP server and CLI: Connects directly to Claude Desktop, Cursor, Codex, ChatGPT, and more services, giving AI agents live web access via tool calls.

Below is a table with the main API parameters:

Parameter	Data Type	Required	Description
`api_key`	String	Yes	Your ScrapingBee authentication key, passed with every request to identify your account and deduct credits.
`url`	String	Yes	The full target URL to scrape.
`render_js`	Boolean	No	Enables JavaScript rendering via headless Chrome. Defaults to true. Set to false for static pages to save credits and reduce response time.
`premium_proxy`	Boolean	No	Routes the request through premium residential proxies for harder-to-scrape sites. Increases credit cost but lowers block rates.
`stealth_proxy`	Boolean	No	Uses the strongest anti-bot bypass tier, aimed at sites with aggressive detection systems. Costs more credits than premium proxies.
`country_code`	String	No	Two-letter ISO code that geotargets the request through a proxy in that country. Useful for localized content.
`wait`	Integer	No	Milliseconds to wait before returning the response, giving async content time to load.
`wait_for`	String	No	A CSS selector the API waits for before returning the response, more reliable than a fixed wait time for dynamic content.
`js_scenario`	JSON Object	No	Defines a sequence of browser actions (clicks, scrolls, form fills, key presses) executed before extraction, for multi-step interactions.
`block_resources`	Boolean	No	Blocks images, CSS, and fonts from loading to speed up rendering. Defaults to true. Disable if visual elements are needed.
`screenshot`	Boolean	No	Returns a screenshot of the rendered page instead of HTML. Useful for debugging or visual monitoring pipelines.
`extract_rules`	JSON Object	No	Defines CSS or XPath selectors mapped to output field names. Returns structured JSON instead of raw HTML.
`ai_query`	String	No	A natural-language description of the data you want extracted. Lets the API return structured fields without selectors.

ScrapingBee Pricing Plans and Credit System Explained

ScrapingBee provides several monthly subscription plans, each with several offerings. The table below reports the plans and their prices per 100 requests, at the time of writing this article:

Plan	Monthly credits	Monthly price per 1k requests
Hobby	75k	0.019$
Freelance	250k	0.049$
Startup	100k	0.099$
Business	300k	0.249$
Business+	800k	0.599$

Note that their pricing offer should be taken into account both on the side of monthly credits and dollars spent on the subscription. This is because the actual consumption is at the credits level. In other words, not every call costs one credit. This accounts for basic requests that do not require JavaScript rendering or advanced parameters. If you want to elevate your scraping to the next level, you have to account for the following:

JavaScript rendering: Each successful request costs 5 credits.
AI query: Adding the AI query parameter costs 5 credits.
Stealth proxies: Each successful request triggering stealth proxies costs 75 credits.
Premium proxies: Each successful request triggering premium proxies costs:
- 10 credits without JavaScript rendering.
- 25 credits using JavaScript rendering.

Below is a breakdown of costs, in different single-request scenarios, considering the Hobby plan, in terms of credits:

Request Tier	Features Included	Cost per Request	Total Requests
Basic	Standard request	1 credit	75,000
JS Rendering	JavaScript rendering only	5 credits	15,000
JS + Premium	JavaScript rendering + premium proxies	30 credits	2,500
JS + Stealth	JavaScript rendering + stealth proxies	80 credits	937

The tests coming up in the next paragraph will help you better understand the credits consumption in different scraping scenarios.

ScrapingBee APIs Hands-On Testing: Methodology and Results

In this section, you will learn how to use ScrapingBee’s APIs with different target URLs, reporting the following per each call:

Average latency
Credit cost

Requirements

To reproduce the upcoming tests, you need:

An active ScrapingBee account with a valid API key.
Python 3.10+ installed on your machine.

Prerequisites

Create a folder called scrapingbee-tests/ on your machine:


    mkdir scrapingbee-tests

Navigate into the folder:


    cd scrapingbee-tests

Create a virtual environment:


    python -m venv venv

Activate the virtual environment:


    source venv/bin/activate # On Ubuntu/Mac. On Windows:
    ./venv/Scripts/activate

In the activated virtual environment, install ScrapingBee’s SDK:


    pip install scrapingbee beautifulsoup4 requests

Alright. You are now ready to write the code and run the tests.

Test #1: Basic ScrapingBee API Call on a Static Page

As a first test, use a target website that has no JavaScript-rendered content, like quotes.toscrape.com:

Inside scrapingbee-tests/, create a file named basic-call.py. Inside it, write the following code:


    from scrapingbee import ScrapingBeeClient
    
    SCRAPINGBEE_API_KEY = "<YOUR-SCRAPINGBEE-API-KEY>"
    TARGET_URL = "https://quotes.toscrape.com/"
    
    client = ScrapingBeeClient(api_key=SCRAPINGBEE_API_KEY)
    response = client.html_api(
        TARGET_URL,
        params={
            "render_js": "false",
        },
    )
    
    print(f"HTTP status : {response.status_code}")
    print(f"HTML length : {len(response.text):,} characters")

This snippet:

Defines the API key with SCRAPINGBEE_API_KEY and the target URL with TARGET_URL.
Uses the method html_api() to call the HTML API. Inside it, the parameter render_js is set to false because the target website does not use JavaScript to render the content. This way, you will save on credits.
Prints the status and the response length, in terms of characters.

The result is the following:

In terms of latency and credits spent, the dashboard presents the following data:

So, you have:

A call duration of 0.64 seconds.
Spent one credit, as you expected.

Test #2: Scraping JavaScript-Rendered Content With ScrapingBee

After a basic call, it is time to go further and test a website that renders content. In that case, you can use scrapingcourse.com/javascript-rendering as the target website. In this case, the code needs to intercept a selector using BeautifulSoup. For example, you can intercept the product name:

In that case, you can set up a benchmark-like code snippet that makes two calls, one using JavaScript rendering and one not using it, as follows:


    from scrapingbee import ScrapingBeeClient
    from bs4 import BeautifulSoup
    
    SCRAPINGBEE_API_KEY = "<YOUR-SCRAPINGBEE-API-KEY>"
    TARGET_URL = "https://www.scrapingcourse.com/javascript-rendering"
    
    PRODUCT_SELECTOR = "span.product-name"
    
    client = ScrapingBeeClient(api_key=SCRAPINGBEE_API_KEY)
    counts = {}
    
    for render_js in ("false", "true"):
        response = client.html_api(
            TARGET_URL,
            params={
                "render_js": render_js,
                "wait_for": "span.product-name",
            },
        )
    
        print(f"\nrender_js={render_js}")
        print(f"HTTP status : {response.status_code}")
        print(f"HTML length : {len(response.text):,} characters")
    
        # Stop on an HTTP error
        response.raise_for_status()
    
        soup = BeautifulSoup(response.text, "html.parser")
        elements = soup.select(PRODUCT_SELECTOR)
    
        # Diagnostics: distinguish missing elements from empty placeholders
        page_title = (
            soup.title.get_text(strip=True)
            if soup.title
            else "(absent)"
        )
    
        print(f"Page title : {page_title}")
        print(f"Matching elements, including empty ones : {len(elements)}")
        print(
            "First matching element :",
            str(elements[0]) if elements else "(absent)",
        )
    
        # Count only product-name elements containing actual text
        names = [
            element.get_text(strip=True)
            for element in elements
            if element.get_text(strip=True)
        ]
    
        counts[render_js] = len(names)
    
        print(f"Populated product names : {len(names)}")
        print(f"First product : {names[0] if names else '(absent)'}")
    
    print("\nConclusion:")
    
    if counts["false"] == 0 and counts["true"] > 0:
        print(
            "Product names were absent from the HTML with JavaScript rendering "
            "disabled, but present with rendering enabled. "
            "This supports using render_js=true to extract these HTML elements."
        )
    elif counts["false"] > 0:
        print(
            "Product names were present without JavaScript rendering. "
            "This test does not demonstrate that rendering is required "
            "to extract these elements."
        )
    else:
        print(
            "No populated product names were found in either response. "
            "The comparison is inconclusive. Inspect the diagnostic output "
            "and returned HTML to check the selector, page identity, "
            "and rendering timing."
        )

This snippet:

Initializes the API client and extraction target. PRODUCT_SELECTOR = "span.product-name" defines the DOM element to intercept, used to evaluate extraction success.
Fetches the same page in two modes. A for loop runs with render_js="false" and render_js="true", using the same selector and parsing logic in both cases. This compares the availability of actual product content in both modes.
Adds wait_for="span.product-name" when render_js="true". This gives ScrapingBee a content-specific readiness condition before returning the HTML. Executing JavaScript alone is often insufficient. You need to set the code up so that it waits for JavaScript to render the content.
Uses response.raise_for_status() to raise an exception on an HTTP error instead of misclassifying it as “no products.”
Parses the returned HTML locally with BeautifulSoup(response.text, "html.parser").
Uses soup.select(PRODUCT_SELECTOR) to locate the target elements and calls get_text(strip=True) to extract their text.

Below is the result:

As you can see, in both cases, the scraper returns 200 and rendered HTML. But only in the case with render_js="true" it fetches the products’ names. Specifically, it fetches 12.

The dashboard reports the following:

So, in this case:

The duration with JavaScript-rendered content is 3.95 seconds, with 5 credits spent.
The duration without JavaScript-rendered content is 0.91 seconds, with 1 credit spent.

Test #3: ScrapingBee vs. Cloudflare-Protected Websites

To elevate the tests, try ScrapingBee against Cloudflare-protected websites. One of these is ssense.com:

To see if the website is aggressively protected, you can start with a first call without using proxies. To do so, create a ssense-call.py file and write the following code in it:


    from scrapingbee import ScrapingBeeClient
    
    SCRAPINGBEE_API_KEY = "<YOUR-SCRAPINGBEE-API-KEY>"
    TARGET_URL = "https://www.ssense.com/en-us/men/designers/acne-studios"
    
    client = ScrapingBeeClient(api_key=SCRAPINGBEE_API_KEY)
    response = client.html_api(
        TARGET_URL,
        params={
            "render_js": "false",
        },
    )
    
    print(f"HTTP status : {response.status_code}")
    print(f"HTML length : {len(response.text):,} characters")

The result you will obtain is:

So, a simple call was sufficient to go beyond Cloudflare. The dashboard reports:

In other words, it took:

1,74 seconds to get the data
1 credit.

Test #4: ScrapingBee vs. DataDome-Protected Websites

Let’s now try the product with L’Occitane, which is protected by DataDome:

Let’s first try with the simple call. Write the following in a Python file named loccitaine-call.py:


    from scrapingbee import ScrapingBeeClient
    
    SCRAPINGBEE_API_KEY = "<YOUR-SCRAPINGBEE-API-KEY>"
    TARGET_URL = "https://www.loccitane.com/en-us/immortelle-divine-youth-oil-27DH030I22.html"
    
    client = ScrapingBeeClient(api_key=SCRAPINGBEE_API_KEY)
    response = client.html_api(
        TARGET_URL,
        params={
            "render_js": "false",
        },
    )
    
    print(f"HTTP status : {response.status_code}")
    print(f"HTML length : {len(response.text):,} characters")

If you run this code, you will get:

This may suggest that you only needed 1 credit to go beyond DataDome’s anti-bot, but it is not true. To verify this, add a screenshot parameter:


    from scrapingbee import ScrapingBeeClient
    
    SCRAPINGBEE_API_KEY = "<YOUR-SCRAPINGBEE-API-KEY>"
    TARGET_URL = "https://www.loccitane.com/en-us/immortelle-divine-youth-oil-27DH030I22.html"
    
    client = ScrapingBeeClient(api_key=SCRAPINGBEE_API_KEY)
    response = client.html_api(
        TARGET_URL,
        params={
            "render_js": "false",
            "screenshot": "true",
        },
    )
    
    with open("loccitaine-screenshot.png", "wb") as f:
        f.write(response.content)
    print("Screenshot saved successfully as screenshot.png")
    
    print(f"HTTP status : {response.status_code}")
    print(f"HTML length : {len(response.text):,} characters")

Below is what the screenshot captured:

So, the scraper has been blocked by the anti-bot. This is what the call cost:

This website is highly protected. To overcome its anti-bot system, you need to use stealth proxies with US geolocalization as follows:


    from scrapingbee import ScrapingBeeClient
    from bs4 import BeautifulSoup
    
    SCRAPINGBEE_API_KEY = "<YOUR-SCRAPINGBEE-API-KEY>"
    TARGET_URL = "https://www.loccitane.com/en-us/immortelle-divine-youth-oil-27DH030I22.html"
    
    client = ScrapingBeeClient(api_key=SCRAPINGBEE_API_KEY)
    response = client.html_api(
        TARGET_URL,
        params={
            "render_js": "true",
            "screenshot": "true",
            "stealth_proxy": "true",
            "country_code": "us"
        },
    )
    
    with open("loccitaine-screenshot.png", "wb") as f:
        f.write(response.content)
    print("Screenshot saved successfully as screenshot.png")
    
    print(f"HTTP status : {response.status_code}")
    print(f"HTML length : {len(response.text):,} characters")

In that case, the result is the fully rendered target page:

This call cost the following:

So, in this case, it needed:

72.26 seconds. This is because it needed to render JavaScript in a time that was not too short to trigger the anti-bot.
75 credits.

As expected, that was the most expensive one!

Test #5: Scraping Amazon Products With ScrapingBee

Another test you can run is using one of the dedicated APIs. For example, you can test their Amazon API to search for a product, given a specific ASIN:

Create an amazon-call.py file and write the following in it:


    import requests
    
    def send_request():
        response = requests.get(
            url='https://app.scrapingbee.com/api/v1/amazon/product',
            headers={
                'Authorization': 'Bearer <YOUR-SCRAPINGBEE-API-KEY>',
            },
            params={
                'query': 'B0DPDRNSXV',
            },
        )
        print('Response HTTP Status Code: ', response.status_code)
        print('Response HTTP Response Body: ', response.content)
    
    send_request()

In this case, the snippet above:

Defines the main function, send_request(), which is the one in charge of managing the call to the specific endpoint.
Specifies the specific endpoint for scraping Amazon products via the url parameter.
The query parameter specifies the product’s ASIN so that the scraper, under the hood, searches for it on Amazon.

Below is the partial result you will obtain via the command line:

So, the basic call was sufficient to retrieve data from Amazon. Below is how much it cost:

So, it took:

3.38 seconds.
5 credits.

ScrapingBee Test Results Summary: Success Rate, Latency, and Cost

The table below summarizes the results obtained for each test:

	Success rate (%)	Time spent (s)	Credits spent
Basic call (static content)	100	0.64	1
JavaScript-rendered content	100	3.95	5
Against Cloudflare	100	1.74	1
Against DataDome	100	72.26	75
Amazon call	100	3.38	5

As you can see, the credits spent increase when the request is not a basic one.

ScrapingBee vs Gologin: A Flat-Rate ScrapingBee Alternative

What Is Gologin Web Unlocker?

Gologin Web Unlocker is a managed API for accessing and rendering web pages. You send a URL, let the service handle the access infrastructure, and receive the page content.

The service handles JavaScript rendering, residential IP routing, fingerprint-related access checks, and supported CAPTCHA challenges.

Its output formats include:

Rendered HTML for custom parsers.
Markdown for document and AI workflows.
Plain text for text processing.
JSON with page metadata.

The last format requires particular care in comparisons with other tools. This is because JSON with metadata is not automatically a source-specific dataset, reporting structured data. A response describing a retrieved page is different from one containing selected, normalized product fields.

The table below summarizes Gologin’s web unlocker core features:

Feature	Capability	Relevance
JavaScript rendering	Rendering engine with readiness options	Retrieves dynamically populated content
Residential targeting	Country, state/region, and city across 195+ countries	Supports geographically targeted retrieval
CAPTCHA handling	reCAPTCHA and Turnstile handled within the request	Reduces separate challenge-handling infrastructure
Sticky sessions	Same exit and identity across a sequence	Supports related page requests
Batch submission	Up to 1,000 URLs per call	Simplifies batch retrieval
Asynchronous delivery	Webhook delivery when ready	Supports background collection workflows
Outcome classification	Distinguishes pages, challenges, blocks, and auth walls	Helps identify unusable responses

Gologin Web Unlocker Hands-On Testing: Same Targets, Same Method

To make the comparison fair, we pointed Gologin Web Unlocker at the exact same five targets used in the ScrapingBee tests above, in the same order, using Python and requests.

A few things are different by design, and they matter for reading the results:

One parameter. The public /v1/scrape endpoint takes a single url query parameter plus an apikey header. There is no render_js, premium_proxy, stealth_proxy or country_code to toggle. JavaScript rendering, proxy rotation and anti-bot handling are applied automatically. Options listed on the product page, such as geo-targeting, output formats and sticky sessions, are not exposed in this endpoint’s public docs, so every test below runs on default settings.
Flat pricing. There are no credit multipliers. One successful page costs the same whether it is a static HTML page or a DataDome-protected product page.
Response cache. Repeated requests for the same URL returned byte-identical HTML in under a second, because Web Unlocker caches responses per URL. That would skew latency, so every script appends a unique cb=<timestamp> query string to force a fresh fetch.
More than one run. We ran each scenario at least five times with the cache bypassed and report the success rate and median latency. DataDome got 15 runs and Amazon got 10.

The endpoint used in every test:

GET https://parsing.webunlocker.gologin.com/v1/scrape?url=<encoded_url>
Header: apikey: <YOUR-GOLOGIN-WEB-UNLOCKER-API-KEY>

Test #1: Gologin Web Unlocker API Call on a Static Page

Web Unlocker returned the full static page in 5 of 5 runs, with a median of 3.20s against ScrapingBee’s 0.64s. The target is the same baseline, quotes.toscrape.com, which shows the service’s raw overhead.

import os
import time
import requests

API_KEY = os.getenv("GOLOGIN_WEBUNLOCKER_API_KEY", "<YOUR-GOLOGIN-WEB-UNLOCKER-API-KEY>")
ENDPOINT = "https://parsing.webunlocker.gologin.com/v1/scrape"
# Web Unlocker caches responses per URL; a unique query string forces a fresh fetch
CACHE_BUSTER = f"cb={int(time.time())}"
TARGET_URL = "https://quotes.toscrape.com/"

start = time.perf_counter()
response = requests.get(
    ENDPOINT,
    params={"url": f"{TARGET_URL}?{CACHE_BUSTER}"},
    headers={"apikey": API_KEY},
    timeout=180,
)
elapsed = time.perf_counter() - start

print(f"HTTP status : {response.status_code}")
print(f"HTML length : {len(response.text):,} characters")
print(f"Duration    : {elapsed:.2f}s")

Result: every run returned the full page: 10,969 characters with all 10 quotes. Latency ranged from 0.55s to 5.75s. ScrapingBee is faster here with render_js=false. Web Unlocker renders every page in a browser, and for plain static HTML that step is pure overhead.

Test #2: JavaScript-Rendered Content With Gologin Web Unlocker

Web Unlocker returned all 12 JavaScript-injected products in 5 of 5 runs, with no rendering parameter, at a median of 21.43s. The target is scrapingcourse.com/javascript-rendering. ScrapingBee needed render_js=true plus wait_for here. Web Unlocker has no rendering switch, so a plain requests call serves as the “rendering off” baseline.

import os
import time
import requests
from bs4 import BeautifulSoup

API_KEY = os.getenv("GOLOGIN_WEBUNLOCKER_API_KEY", "<YOUR-GOLOGIN-WEB-UNLOCKER-API-KEY>")
ENDPOINT = "https://parsing.webunlocker.gologin.com/v1/scrape"
# Web Unlocker caches responses per URL; a unique query string forces a fresh fetch
CACHE_BUSTER = f"cb={int(time.time())}"
TARGET_URL = "https://www.scrapingcourse.com/javascript-rendering"

PRODUCT_SELECTOR = "span.product-name"


def fetch_direct():
    return requests.get(TARGET_URL, timeout=60)


def fetch_unlocker():
    return requests.get(
        ENDPOINT,
        params={"url": f"{TARGET_URL}?{CACHE_BUSTER}"},
        headers={"apikey": API_KEY},
        timeout=180,
    )


counts = {}

for label, fetch in (("plain requests (no rendering)", fetch_direct),
                     ("Gologin Web Unlocker", fetch_unlocker)):
    start = time.perf_counter()
    response = fetch()
    elapsed = time.perf_counter() - start

    print(f"\n{label}")
    print(f"HTTP status : {response.status_code}")
    print(f"HTML length : {len(response.text):,} characters")
    print(f"Duration    : {elapsed:.2f}s")

    response.raise_for_status()

    soup = BeautifulSoup(response.text, "html.parser")
    elements = soup.select(PRODUCT_SELECTOR)
    names = [el.get_text(strip=True) for el in elements if el.get_text(strip=True)]
    counts[label] = len(names)

    print(f"Matching elements, including empty ones : {len(elements)}")
    print(f"Populated product names : {len(names)}")
    print(f"First product : {names[0] if names else '(absent)'}")

print("\nConclusion:")
if counts["plain requests (no rendering)"] == 0 and counts["Gologin Web Unlocker"] > 0:
    print("Product names are injected by JavaScript. Web Unlocker returned the "
          "rendered DOM by default, with no extra rendering parameter.")
else:
    print("Inconclusive: inspect the returned HTML.")

The plain request finds zero product names, while the Web Unlocker response contains all 12. Below is the HTML returned by Web Unlocker, opened locally with scripts stripped, so everything visible came from the API response:

Result: latency ranged from 20.46s to 41.56s. This is the main trade-off of the default-everything approach. You never have to remember render_js or tune wait_for, but you also cannot shorten the wait. It took about 5x longer than ScrapingBee’s 3.95s.

Test #3: Gologin Web Unlocker vs. Cloudflare-Protected Websites

Web Unlocker returned the full SSENSE catalog in 4 of 5 runs, with a median of 25.16s. One run failed with HTTP 504 after 90 seconds. Besides the Web Unlocker call, the script sends a direct request with a browser User-Agent and checks both responses for Cloudflare interstitial markers.

import os
import time
import requests
from bs4 import BeautifulSoup

API_KEY = os.getenv("GOLOGIN_WEBUNLOCKER_API_KEY", "<YOUR-GOLOGIN-WEB-UNLOCKER-API-KEY>")
ENDPOINT = "https://parsing.webunlocker.gologin.com/v1/scrape"
# Web Unlocker caches responses per URL; a unique query string forces a fresh fetch
CACHE_BUSTER = f"cb={int(time.time())}"
TARGET_URL = "https://www.ssense.com/en-us/men/designers/acne-studios"

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
                  "AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0 Safari/537.36"
}
# Markers of a Cloudflare interstitial (not the passive script present on every CF page)
CHALLENGE_MARKERS = ("Just a moment", "cf-chl", "Attention Required")


def report(label, response, elapsed):
    soup = BeautifulSoup(response.text, "html.parser")
    title = soup.title.get_text(strip=True) if soup.title else "(absent)"
    challenged = any(marker in response.text for marker in CHALLENGE_MARKERS)

    print(f"\n{label}")
    print(f"HTTP status : {response.status_code}")
    print(f"HTML length : {len(response.text):,} characters")
    print(f"Duration    : {elapsed:.2f}s")
    print(f"Page title  : {title}")
    print(f"Cloudflare challenge page : {challenged}")


start = time.perf_counter()
direct = requests.get(TARGET_URL, headers=HEADERS, timeout=60)
report("Direct request", direct, time.perf_counter() - start)

start = time.perf_counter()
unlocked = requests.get(
    ENDPOINT,
    params={"url": f"{TARGET_URL}?{CACHE_BUSTER}"},
    headers={"apikey": API_KEY},
    timeout=180,
)
report("Gologin Web Unlocker", unlocked, time.perf_counter() - start)

Result: successful runs returned about 1.4M characters with the title “Latest Acne Studios Collection for Men | SSENSE”. One caveat applies to both tools. On test day, SSENSE did not challenge even our plain direct request, which is also why ScrapingBee succeeded with a basic 1-credit call. So this test confirms that Web Unlocker returns the complete rendered catalog. It is not a hard Cloudflare bypass benchmark. The Web Unlocker response was about 20% larger than the direct one (1.40M vs 1.18M characters) because it includes content loaded after JavaScript runs.

Test #4: Gologin Web Unlocker vs. DataDome-Protected Websites

Web Unlocker returned the complete L’Occitane page in only 3 of 15 runs (20%). When it succeeded, it took a median of 10.35s, against ScrapingBee’s 72.26s and 75 credits. This was the hardest target in the ScrapingBee review. ScrapingBee’s first basic attempt was blocked, and success required render_js, stealth_proxy and country_code=us.

Here is what a visitor without a trusted fingerprint gets. A direct request returns an 807-character DataDome stub, and in a real headless Chrome it becomes the device check screen:

The script sends a direct request and a Web Unlocker request. It then checks for the DataDome stub, the product price, and whether the page has a <body>:

import os
import re
import time
import requests
from bs4 import BeautifulSoup

API_KEY = os.getenv("GOLOGIN_WEBUNLOCKER_API_KEY", "<YOUR-GOLOGIN-WEB-UNLOCKER-API-KEY>")
ENDPOINT = "https://parsing.webunlocker.gologin.com/v1/scrape"
# Web Unlocker caches responses per URL; a unique query string forces a fresh fetch
CACHE_BUSTER = f"cb={int(time.time())}"
TARGET_URL = "https://www.loccitane.com/en-us/immortelle-divine-youth-oil-27DH030I22.html"

HEADERS = {
    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) "
                  "AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0 Safari/537.36"
}


def report(label, response, elapsed):
    soup = BeautifulSoup(response.text, "html.parser")
    title = soup.title.get_text(strip=True) if soup.title else "(absent)"
    price = re.search(r'"price"\s*:\s*"?([\d.]+)', response.text)
    blocked = response.status_code == 403 or bool(re.search(r"captcha(-|&#x2d;)delivery", response.text))

    print(f"\n{label}")
    print(f"HTTP status : {response.status_code}")
    print(f"HTML length : {len(response.text):,} characters")
    print(f"Duration    : {elapsed:.2f}s")
    print(f"Page title  : {title}")
    print(f"Price       : {'$' + price.group(1) if price else '(absent)'}")
    print(f"DataDome block page : {blocked}")
    print(f"Full page (<body> present) : {'<body' in response.text}")


# 1. Baseline: a direct request with a browser User-Agent
start = time.perf_counter()
direct = requests.get(TARGET_URL, headers=HEADERS, timeout=60)
report("Direct request", direct, time.perf_counter() - start)

# 2. The same URL through Gologin Web Unlocker, default settings
start = time.perf_counter()
unlocked = requests.get(
    ENDPOINT,
    params={"url": f"{TARGET_URL}?{CACHE_BUSTER}"},
    headers={"apikey": API_KEY},
    timeout=180,
)
report("Gologin Web Unlocker", unlocked, time.perf_counter() - start)

with open("loccitane.html", "w", encoding="utf-8") as f:
    f.write(unlocked.text)
print("\nRendered HTML saved as loccitane.html")

When it works, it works with no extra parameters. Here the full product page came back in 7.94s, with the price present and no DataDome stub:

That was not the typical outcome. Most runs returned HTTP 200 with a page title but no <body>: a 6–7K-character document holding only the <head> section. Web Unlocker got past the DataDome stub, but returned the page before the product content loaded:

In one run, the exit IP landed in a country that L’Occitane blocks at the Cloudflare level. The API returned a Cloudflare “Error 1009 – Access denied” page, again with HTTP 200.

Result: across all 26 uncached requests we sent to this URL, 7 (27%) returned the complete page. Incomplete responses still come back as 200, so a production scraper should validate the content and retry. Check for a known selector or the price, and don’t trust the status code alone.

Test #5: Scraping Amazon Products With Gologin Web Unlocker

Web Unlocker returned the Amazon product page in 10 of 10 runs, with a median of 8.09s. The price was present in 9 of 10 runs. ScrapingBee has a dedicated Amazon API that returns JSON for an ASIN. Web Unlocker has no Amazon endpoint. It returns the rendered page, so we requested the same ASIN (B0DPDRNSXV) and parsed the key fields with BeautifulSoup:

import json
import os
import time
import requests
from bs4 import BeautifulSoup

API_KEY = os.getenv("GOLOGIN_WEBUNLOCKER_API_KEY", "<YOUR-GOLOGIN-WEB-UNLOCKER-API-KEY>")
ENDPOINT = "https://parsing.webunlocker.gologin.com/v1/scrape"
# Web Unlocker caches responses per URL; a unique query string forces a fresh fetch
CACHE_BUSTER = f"cb={int(time.time())}"
ASIN = "B0DPDRNSXV"
TARGET_URL = f"https://www.amazon.com/dp/{ASIN}"


def text(soup, selector):
    el = soup.select_one(selector)
    return " ".join(el.get_text().split()) if el else None


start = time.perf_counter()
response = requests.get(
    ENDPOINT,
    params={"url": f"{TARGET_URL}?{CACHE_BUSTER}"},
    headers={"apikey": API_KEY},
    timeout=180,
)
elapsed = time.perf_counter() - start

print(f"HTTP status : {response.status_code}")
print(f"HTML length : {len(response.text):,} characters")
print(f"Duration    : {elapsed:.2f}s")

soup = BeautifulSoup(response.text, "html.parser")
product = {
    "asin": ASIN,
    "title": text(soup, "#productTitle"),
    "price": text(soup, ".priceToPay"),
    "rating": text(soup, "#acrPopover .a-icon-alt"),
    "reviews": text(soup, "#acrCustomerReviewText"),
    "availability": text(soup, "#availability"),
    "deliver_to": text(soup, "#glow-ingress-line2"),
}
print(json.dumps(product, indent=2, ensure_ascii=False))

Result: whether the price shows depends on geography. The public endpoint we tested has no country parameter, so the exit IP is chosen for you. With a US exit, Amazon showed $598.00 and “In Stock”. With other exits it replaced the buy box with “This item cannot be shipped to your selected delivery location”. We saw Italy in the benchmark, and Zimbabwe, Mongolia and Vietnam in earlier ad-hoc runs. One ad-hoc request also hit Amazon’s “Continue shopping” interstitial with HTTP 200. For reliable US prices, check deliver_to and retry when it isn’t a US location.

ScrapingBee’s Amazon API took 3.38s and 5 credits and returned ready-made JSON. Web Unlocker is slower and leaves the parsing to you. In return you get the entire page, including variants, other sellers and add-ons, not just what a predefined schema covers.

The Core Difference Between Scraping APIs and Unlocker APIs: Page Retrieval vs. Structured Extraction

When comparing scraping APIs (ScrapingBee’s offering) and web unlocker APIs (Gologin’s offering) to determine which technical solution fits your needs, you first need to understand, at a high level, how these services work. First note that a web-data workflow usually involves two distinct tasks:

Page retrieval: Means reaching the target, handling access obstacles, executing JavaScript where necessary, and obtaining the content.
Data extraction: Means identifying the information you need within that content and returning it as defined fields.

Data extraction can be structured and unstructured. For example, retrieving a product page might give you its rendered HTML, including navigation, descriptions, reviews, related products, and pricing. That extraction is unstructured.

On the other hand, extracting a complete product record and reporting it in an organized JSON file means identifying which text represents the product name, which price is current, which currency applies, and whether the selected variant is available.

In this context, Gologin Web Unlocker provides unstructured content, while ScrapingBeee is more focused on structured content (while it can also provide unstructured, but it is not its main focus). Below is a table that summarizes common extraction scenarios to help you better understand:

Question	Page retrieval/web unlocking (unstructured extraction)	Structured extraction
What problem does it solve?	Accessing and loading the target page	Identifying and organizing particular information
Typical output	Rendered HTML, Markdown, or text	Named fields in structured JSON
What does success mean?	The intended page content is unlocked and retrieved	Required fields are present, correct, and are presented in a structured form
What work may remain?	Parsing, field selection, normalization, validation	Validation, normalization, and integration

These are functional distinctions, not rigid product categories. In other words, a service marketed as a “scraping API” may provide either or both.

That distinction should guide you when comparing different scraping services so that you can interpret results and compare features and prices.

When Gologin Is a Better Choice

The examples below illustrate situations in which Gologin’s Web Unlocker is a better technical fit than ScrapingBee’s structured APIs:

Scenario	Why Gologin Web Unlocker is better than ScrapingBee
You need localized Google Search results	Gologin’s interface supports country, state or region, and city-level targeting. This is useful when the results depend on a specific location. For example, comparing what users in New York see with what users in another city see.
You need the complete rendered page rather than predefined fields	Gologin returns rendered HTML, Markdown, text, or JSON. This is useful when you want to preserve the whole results page and decide later which information to extract.
You want Markdown for an AI or research workflow	Gologin returns the target page as Markdown, making it suitable as input for an AI agent, summarization pipeline, or research tool. This is more convenient than requesting structured fields when the workflow needs the page’s broader context, such as a Reddit discussion or a Google AI Overview.
You need to keep control of extraction and output formats	Gologin separates page unlocking from downstream processing. A freelancer or team can retrieve the page, then use its own parser, validation rules, database schema, or AI model. This is useful when different projects require different fields from the same target or when the required schema changes frequently.
You need to extract content from Zillow, LinkedIn, or from Reddit threads	The case is pretty simple: Gologin directly supports extracting content from those sources natively, without integrations or spaghetti code.

In other words, the strongest case for Gologin is when you need reliable, localized access to the supported target pages and want the resulting HTML, Markdown, or text as a flexible input for its own extraction or AI workflow.

Also, note that Gologin Web Unlocker’s pricing offer is not based on credits. Below is a breakdown of the offers per 1k requests, at the time of writing this article:

Plan	Monthly price per 1k requests	Best for
Growth	0.98$	Low-volume requests
Professional	0.75$	Teams looking to scale their operations
Business	0.60$	Large teams with extensive operational needs
Premium	0.45$	Advanced support and features for critical operations

The rationale to bear in mind here is that these prices include:

JavaScript rendering.
Proxy management.
CAPTCHA solving capabilities.
Automatic retries on failed requests.

In other words, Gologin’s Web Unlocker APIs provide a predictable monthly expenses as you do not need to focus on estimating credit costs.

ScrapingBee vs. Gologin Web Unlocker: Head-to-Head Results

ScrapingBee was faster on every target except DataDome. Gologin Web Unlocker needed no parameters anywhere and was far cheaper on the protected page. ScrapingBee figures come from the single-run tests above. Gologin figures are medians of uncached benchmark runs on default settings.

Test results

Test	ScrapingBee result	ScrapingBee time	ScrapingBee credits / params	Gologin success rate	Gologin median time	Gologin params
Static page	Success	0.64s	1 credit, `render_js=false`	5/5 (100%)	3.20s	`url` only
JavaScript rendering	Success	3.95s	5 credits, `render_js` + `wait_for`	5/5 (100%)	21.43s	`url` only
Cloudflare (SSENSE)	Success	1.74s	1 credit	4/5 (80%), one 504 timeout	25.16s	`url` only
DataDome (L’Occitane)	Success after a blocked first try	72.26s	75 credits, `render_js` + `stealth_proxy` + `country_code`	3/15 (20%) complete pages	10.35s	`url` only
Amazon product	Success, structured JSON	3.38s	5 credits, Amazon API	10/10 page, 9/10 with US price	8.09s	`url` + own parser

Cost per 1,000 successful requests

Plans compared: ScrapingBee Freelance ($49 for 250K credits) and Business ($249 for 3M credits), and Gologin Growth ($49 for 50K pages) and Business ($299 for 500K pages). Prices from ScrapingBee pricing and Gologin Web Unlocker.

Scenario	ScrapingBee Freelance ($49)	Gologin Growth ($49)	ScrapingBee Business ($249)	Gologin Business ($299)
Static / basic	$0.20	$0.98	$0.08	$0.60
JavaScript rendering	$0.98	$0.98	$0.42	$0.60
Amazon	$0.98	$0.98	$0.42	$0.60
DataDome (stealth)	$14.70	$0.98*	$6.23	$0.60*

* Gologin states that blocked and challenged responses are not billed. Our incomplete DataDome pages came back as HTTP 200, and the API returns no usage data per request, so we could not confirm whether they were billed. If they were, a 20% success rate gives about $4.90 (Growth) or $3.00 (Business) per 1,000 complete pages. That is still below ScrapingBee’s stealth-proxy price.

Features

Feature	ScrapingBee	Gologin Web Unlocker
API surface	Many parameters: `render_js`, `wait_for`, `premium_proxy`, `stealth_proxy`, `country_code`, `screenshot`, extraction rules, JS scenarios	One parameter (`url`); rendering and anti-bot are automatic
Pricing model	Credits with multipliers (1x, 5x, 10вАУ25x, 75x)	Flat price per successful page
Geo-targeting via API	Yes (`country_code`)	Advertised (country, state, city), but not exposed in the public `/v1/scrape` endpoint we tested; the exit country was random
Structured data	Extraction rules, AI extraction, dedicated Amazon / Google / Walmart APIs	Rendered HTML from the endpoint we tested; Markdown, text and JSON are advertised, and the CLI exposes markdown/text output and schema `extract`
Screenshots	Yes	No
Response cache	No	Yes, per URL (add a unique query string for fresh data)
Concurrency	50 (Freelance) to 400 (Business+) requests	Our test key returned HTTP 429 on parallel requests
Failure signaling	Error status codes; failed requests not billed	Incomplete or geo-blocked pages can return HTTP 200, so validate content
Free trial	1,000 credits	7-day free trial

Verdict: Which One Should You Use?

Pick ScrapingBee for speed, geo control and ready-made JSON. Pick Gologin Web Unlocker for one simple endpoint and flat pricing on protected sites.

ScrapingBee is the more mature, more configurable tool. It was faster on every target except DataDome, it lets you pin a country, and its dedicated APIs return structured JSON with no parsing code. The cost is complexity and credits. You have to know which parameters a site needs, and one DataDome page cost 75 credits.

Gologin Web Unlocker takes the opposite approach: send a URL, get rendered HTML. That held up on static, JavaScript-heavy, Cloudflare-fronted and Amazon pages with no tuning at all. On DataDome, when it succeeded, it was about 7x faster and up to about 15x cheaper than ScrapingBee’s stealth-proxy request.

It is also a younger product, and four things showed in testing:

20вАУ25 second latency on rendered pages
no geo-targeting parameter in the public endpoint
a per-URL cache you have to work around
incomplete pages that still return HTTP 200

Build content validation and retries into your pipeline. On our DataDome target, only about one request in four or five returned the complete page.

Choose ScrapingBee if you need low latency, country-specific results, screenshots, or ready-made JSON for Amazon, Google and similar sites, and your targets are mostly lightly protected.
Choose Gologin Web Unlocker if you want one endpoint for many different sites, you parse HTML yourself or feed it to an LLM, and protected pages drive your budget. There, flat per-page pricing beats credit multipliers.

Frequently Asked Questions

What is the main difference between ScrapingBee and Gologin Web Unlocker?

ScrapingBee supports page retrieval and structured extraction. It can return HTML, extract selected fields, or provide structured data through dedicated APIs. Gologin Web Unlocker focuses primarily on accessing and rendering supported pages, then returning their content as HTML, Markdown, text, or JSON metadata.

When should I choose Gologin Web Unlocker instead of ScrapingBee?

Choose Gologin when you need reliable access to supported targets such as Google Search, Amazon, LinkedIn, Zillow, or Reddit. When you need localized results, rendered page content, or Markdown for an AI and research workflow. It is also useful when you already have your own parser or want to control the extraction and validation process instead of using a fixed endpoint schema.

Does Gologin Web Unlocker return structured data?

Gologin can return JSON with page metadata, but this should not be confused with source-specific structured extraction. Its core output is the retrieved page in rendered HTML, Markdown, or text. Your application can then extract the fields it needs, using custom code, an AI model, or another downstream processing tool.

Is Gologin Web Unlocker suitable for freelancers and small teams?

Yes. Freelancers can use it to access supported pages without building and maintaining their own browser-rendering, residential-proxy, CAPTCHA-handling, and location-targeting infrastructure. Teams can use the same page-retrieval layer for AI pipelines, monitoring systems, research tools, and custom extraction workflows.

Can I try Gologin Web Unlocker before committing to it?

Yes. You can try Gologin Web Unlocker for free and test how it handles your supported targets, output format, and location requirements. This is the most practical way to determine whether it fits your workflow better than a structured scraping API.

Conclusion

ScrapingBee and Gologin Web Unlocker address different parts of the web-data workflow. ScrapingBee is a good choice when you want structured data through extraction rules or dedicated source-specific APIs. Gologin Web Unlocker focuses on reliably accessing supported pages and returning their rendered content as HTML, Markdown, text, or metadata-rich JSON, so you remain in control of parsing and downstream processing.

Try Gologin Web Unlocker for free by starting with the 7-day free trial and see how quickly you can turn difficult, dynamic web pages into usable data.