TL;DR
Zenrows offers 5 products ( Fetch, Extract, Batch, Browser Session, and Residential proxies), one API key, and one credit balance. Fetch is the core product, great for bypassing anti-bot solutions: 100% against product pages behind PerimeterX and Akamai, and 95% (19/20) against DataDome. The catch?
Pages Zenrows bills as successful aren’t always useful, and no product can maintain a session for more than 10 to 15 minutes. At the same time, Zenrows struggles to persist browser identity and IP addresses during sessions.
| Product | Best for | Billing | The catch |
|---|---|---|---|
| Fetch | Accessing pages behind anti-bot protection and scraping e-commerce targets. | Per successful request: 1 credit for a base Fetch request, 5 credits with JavaScript rendering, 10 credits with premium proxies, and 25 credits with both enabled. | Target site determines Fetch configuration: the e-commerce and anti-bot targets strictly required 25 credits (JS + premium proxies) per request. Failed pages returned over successful connections were marked as successful and billed. |
| Extract | Retrieving structured data. | The Zenrows docs state that Extract is billed the same as Fetch, but the review shows 1 additional credit on top of any Fetch configuration. | You must prepare new domains by calling a Zenrows API. Extract against the e-commerce target returned empty review counts and wrong image URLs. |
| Batch | Large-scale web scraping | The same as Fetch | Zenrows marked “failed” requests to the session-based target as successful and billed credits. |
| Browser Sessions | JavaScript rendering and browser simulation via CDP-compatible frameworks. | 25,000 credits per GB + 5 credits per minute | The page size determines a large chunk of credits billed per session. |
| Residential Proxies | Anonymous HTTP requests | 25,000 credits per GB | The Zenrows docs tag Residential proxies as a legacy product. |
Table: Zenrows products at a glance (pricing as of 02 October 2026)
What is ZenRows and who is it for?
Zenrows is a web scraping infrastructure that provides AI agents and applications with access to the live web, including sites behind anti-bot systems and those that load content using JavaScript.
Modern websites often use dynamically rendered pages and block automated traffic, especially from inconsistent fingerprints and data-center IP addresses. To access data on those websites, you’d typically need to buy and manage residential proxies, patch headless Chrome instances, and write your own retry logic.
Zenrows handles all that work through a single API. You make a request to the Zenrows API, and it retrieves the page, launching a headless browser when needed, and routing the request through its proxy network. By default, each request gets a new IP, though the Zenrows docs say you can keep the same IP for only ten minutes; it doesn’t hold cookies.
Testing methodology
I chose 5 web targets that’d enable me to evaluate Zenrows’ performance across increasingly complex web scraping challenges: On each site, I targeted pages with real web-scraping value (product pages, listings, etc.) and checked for useful markers, like a product block that would not appear on a blocked page, not just that Zenrows returned a response.
I classified each target into 5 different categories:
- Static Baseline (scrapethissite.com): Basic paginated HTML page, no JavaScript rendering, no anti-bot protection, just Hockey table data, in plain HTML. I used this target to determine base latency for each product. Ideally, any delay here should be from Zenrows.
- Dynamic SPA (infinite scroll): This demo page displays content using JavaScript and adds more as you scroll.
- E-Commerce Target (Walmart product pages): I chose Walmart because it presents one of the most common web scraping use cases (competitive analysis, price tracking, etc.). These pages use Akamai and PerimeterX, and also display content dynamically, a perfect opportunity to test against real-world scenarios.
- Hard Anti-Bot Target (leboncoin.fr): This target uses DataDome, which checks browser fingerprints and behavior, among other things. I used this as a direct test of Zenrows anti-bot capabilities.
- Session-Based Target (heroku-app-login): The content of this page is behind a valid login and requires cookies and persistent authentication.
Since Zenrows offers multiple configurations that cost more credits as the target’s needs grow (more on pricing in a later section), I started each target with the cheapest possible configuration and moved up only when a tier failed.
The pass criteria for each request are two-fold: an HTTP 200 response, which often means a successful request, and a response body that contains a marker only present on the real page. Checking for a marker matters because some anti-bot solutions, particularly DataDome, return fake responses or blocked pages that also read as successful requests to throw off automated requests. Over an HTTP connection, a successful response carries a 200 status code, regardless of its content. Any body without the selected marker counts as a failure.
For static, dynamic, and e-commerce, I kept the sample size at 5 attempts per target for each product and extended it to 20 attempts for the anti-bot target and any cell (product + target) that had mixed results. My thought process was that if a cell passed 5/5, it would most likely pass 20/20, but a 3/5 result needs a larger sample size. For the anti-bot target, I didn’t expect a few requests to tell the full story because DataDome adapts quickly to incoming traffic.
For the session-based target, I logged in through Zenrows and reused the returned session cookie with a fixed session ID. At 30-second intervals, each product requested the logged-in page, resulting in about 20 requests in 10 minutes. As with the other targets, a pass is two-fold (200 and marker check).
Each run used real, billable API credits, which I bought specifically for this review. I measured credit usage per request using Zenrows’ X-Request-Credits header. For sessions, I used data transfer and session duration and also checked the account’s credit usage during and after the session.
Unpacking Zenrows product line (Demystifying the confusion)
Zenrows’ suite seemed a bit confusing to me at first glance: a few overlapping products, all sharing the same credit balance but 3 different metering units. I had to dig into their docs to understand it better. Here’s what each one is, so you know exactly what you’re buying:
- Fetch (formerly Universal Scraping API): the core product, a single HTTP endpoint that handles JS rendering, proxy rotation, and anti-bot bypass.
- Browser sessions (formerly Scraping Browser): a cloud-hosted remote browser with residential proxies that you control via any CDP-compatible framework (Puppeteer and Playwright).
- Extract: a recently listed product (primitive) that returns pages on domains Zenrows has prepared as structured JSON. Zenrows currently covers a long list of prepared domains, and you can request a domain to be prepared yourself. (More on that later). It’s still in beta and runs on the same engine as Fetch.
- Batch: another product that uses the Fetch engine. It takes a large list of URLs and runs them as jobs, queues each request, and stores the results.
- Residential proxies: a standalone proxy management solution, sold as raw proxies. It’s labeled as legacy in the Zenrows docs. They probably plan on phasing it out as a standalone product; I’m not sure. But at the time of this review, it’s available as a product in the Zenrows dashboard.
Hands-on tests and benchmarks per product
Let’s start with tables combined from my terminal log (completed October 02, 2026). The success rate is generally positive, with most products recording 90% – 100% against the 5 targets. That’s expected because 4 of the 5 products run on the same Fetch engine, so if Fetch is successful, the rest would be as well, albeit with a slight latency trade-off, depending on the product’s main feature.
| Static-based (success rate) | Dynamic SPA (success rate) | eCommerce target (success rate) | Hard anti-bot target (success rate) | Session-based target (success rate within 10 minutes) | |
|---|---|---|---|---|---|
| Fetch | 5/5 (base) (100%) |
5/5 (JS) (100%) |
5/5 (JS + premium) (100%) |
19/20 (JS + premium) (95%) |
17/17 (100%) |
| Extract on Fetch | 5/5 (base) (100%) |
5/5 (JS) (100%) |
5/5 (JS + premium) (100%) |
20/20 (JS + premium) (100%) |
0/20 (0%) |
| Batch | 20/20 (100%) |
20/20 (100%) |
30/30 (100%) |
30/30 (100%) |
Not tested |
| Browser Sessions | Not tested | 5/5 (100%) |
5/5 (100%) |
4/10 (40%) |
9/10 (90%) |
| Residential Proxies | 5/5 (100%) |
Not tested | 0/5 (0%) |
4/20 (20%) |
19/20 (95%) |
Table 1: Success rate
I didn’t test Batch against the session-based target because a Zenrows Batch job is a list of URLs to fetch, and I couldn’t find a way to submit a login form through it. The static page contains only plain HTML, no JavaScript, so there’s no need to test Browser Session, and the reverse is true for Zenrows residential proxies against the dynamic SPA.
| Static-based | Dynamic SPA | eCommerce target | Hard anti-bot target | Session-based target | |
|---|---|---|---|---|---|
| Fetch | 1.5 s | 12.6 s (JS) |
16.2 s (JS + premium) |
8.7 s (JS + premium) |
6.65 s |
| Extract on Fetch | 2.3 s | 23.5 s (JS) |
11.3 s (JS + premium) |
15.6 s (JS + premium) |
|
| Batch (per page)* | 0.3 s | 1.3 s | 3.1 s | 2.1 s | Not tested |
| Browser Sessions (per session) | Not tested | 31.2 s | 45.8 s | 36.6 s | 76.0 s |
| Residential Proxies | 3.8 s | Not tested | 4.8 s | 0.9 s |
Table 2: Latency
Latency for Fetch, Extract, Browser Sessions, and Residential Proxies is the median time per successful request. Batch jobs run in parallel, so I used the total job time divided by the number of pages to estimate latency.
In the Testing Methodology section, I described a different approach for the session-based target, but I didn’t explain why. Here’s why:
I initially logged in locally from my browser to retrieve the session cookie and then used that cookie in the requests, but Fetch, Extract, and Batch failed every time. Only Browser Sessions (obviously, because it uses its own browser identity) and Residential proxies with a different HTTP client passed.
I traced the problem to inconsistent browser identity: my requests contained headers, including a set user-agent, but Zenrows always presented a different user-agent, as seen in the log below:
Cookie in targets.yaml: rack.session=BAh7Ckk...ccee7e
[echo] status 200
[echo] Cookie the target received: rack.session=BAh7Ckk...ccee7e
[echo] Matches what we sent: True
[echo] User-Agent received matches ours: False
sent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/141.0.0.0 Safari/537.36
received: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/140.0.0.0 Safari/537.36
[echo] Accept-Language received matches ours: TrueSince the session-based target ties user agents to its cookies, the request failed automatically. I couldn’t find any Zenrows resources on locking in a user-agent, so I had to do something different for Fetch, Batch, and Extract.
Fetch
Using Zenrows Fetch, I started with 5 requests per target on separate pages within the same domain. It passed 100% of the time against static and dynamic targets (JS), with a median latency of 1.5 seconds at 1 credit per request and 12.6 seconds at 5 credits per request, respectively.
Fetch (JS + premium) also passed 100% of the time against the e-commerce target and 95% against the anti-bot target, recording a median latency of 16.2 seconds and 8.7 seconds at 25 credits per request, respectively. Against the session-based target, Fetch also passed 100% of the time. However, in round 13 (7.5 minutes after login), the IP changed from 134.19.239.205 to 105.235.131.52, as seen in the log below.
--- round 11
{"product": "fetch", "step": "secure", "round": 11, "minutes_since_login": 6.2, "latency_s": 2.14, "ip": "134.19.239.205:62709"}
--- round 12
{"product": "fetch", "step": "secure", "round": 12, "minutes_since_login": 6.7, "latency_s": 22.98, "ip": "134.19.239.205:62738"}
--- round 13
{"product": "fetch", "step": "secure", "round": 13, "minutes_since_login": 7.5, "latency_s": 5.01, "ip": "105.235.131.52:64964"}I also recorded multiple instances of this change. This result contradicts Zenrows docs, which state that its session_id parameter keeps the same IP for 10 minutes.
Below is the summary of the entire run directly from my terminal:
## fetch
| Target | Config | Success | Avg latency (s) | Median latency (s) | Avg credits/request | Credits per 1K successful |
|---|---|---|---|---|---|---|
| antibot | js_premium | 19/20 (95%) | 10.34 | 8.71 | 25.0 | 25,000 |
| ecom | js_premium | 5/5 (100%) | 15.65 | 16.17 | 25.0 | 25,000 |
| spa | js | 5/5 (100%) | 15.76 | 12.59 | 5.0 | 5,000 |
| static | base | 5/5 (100%) | 3.79 | 1.49 | 1.0 | 1,000 |Session-based target:
## fetch
| Run | Window | Success | Avg latency (s) | Median latency (s) | Avg credits/request | Credits per 1K successful |
|---|---|---|---|---|---|---|
| run 1 | before 10 min | 17/17 (100%) | 6.65 | 2.84 | 1.0 | 1,000 |
| run 1 | after 10 min | 0/9 (0%) | - | - | 1.0 | - |
| run 1 | all | 17/26 (65%) | 6.65 | 2.84 | 1.0 | 1,529 |Extract
Extract runs on Fetch, so it had approximately the same success rate as Fetch. However, Extract focuses more on the quality of the output. While the marker (current product price) was present for the e-commerce target (Walmart) in every successful request, the review count came back empty, and all image entries pointed to a video thumbnail, not the intended product photos.
Each Extract usage incurred 1 additional credit on top of a Fetch request cost: 2, 6, 26, and 26 credits per request to static, dynamic, ecommerce, and anti-bot targets, respectively. Zenrows refused every Extract request to the session-based target, so no credits were charged for that run.
Full summary from terminal:
## extract
| Target | Config | Success | Avg latency (s) | Median latency (s) | Avg credits/request | Credits per 1K successful |
|---|---|---|---|---|---|---|
| antibot | js_premium | 20/20 (100%) | 19.01 | 15.57 | 26.0 | 26,000 |
| ecom | js_premium | 5/5 (100%) | 12.77 | 11.26 | 26.0 | 26,000 |
| spa | js | 5/5 (100%) | 22.08 | 23.53 | 6.0 | 6,000 |
| static | base | 5/5 (100%) | 6.75 | 2.32 | 2.0 | 2,000 |Batch
Batch also runs on Fetch, so it succeeded at approximately the same rate as Fetch across all targets, but faster. Batch recorded 94 seconds for 30 requests to the e-commerce target, compared to about 470 seconds for Fetch requests, one at a time. Although Fetch can also run multiple requests in parallel, I didn’t test that since Batch already does.
Batch jobs charge credits per URL at exactly the same rate as Fetch. For analysis purposes, I intentionally included attempts to the session-based target in this run to check whether Zenrows charged for failed requests, and it did. I recorded 20 failed requests to the session-based target, but the activity log in my Zenrows dashboard marked all 20 as successful and charged credits for them.
Full summary from terminal:
## batch
| Target | Config | Success | Job time per page (s) | Credits per URL | Credits per 1K successful |
|---|---|---|---|---|---|
| antibot | js_premium | 30/30 (100%) | 2.06 | 25.0 | 25,000 |
| ecom | js_premium | 30/30 (100%) | 3.14 | 25.0 | 25,000 |
| session | base | 0/20 (0%)| 1.17 | 1.0 | 1,000 |
| spa | js | 20/20 (100%) | 1.33 | 5.0 | 5,000 |
| static | base | 24/24 (100%)| 0.28 | 1.0 | 1,000 |Browser Sessions
Using Browser Sessions, I made 5 requests to dynamic and e-commerce targets, and 10 requests to anti-bot and session-based targets. It passed 100% of the time against dynamic and e-commerce targets, 40% against the anti-bot target, and 90% against the session-based target.
Browser Sessions charges credits differently from the previous 3. It charges 25,000 credits per GB + 5 credits per minute. Failed sessions are billed too; you pay for data and time whether the request succeeds or fails.
Full summary from terminal:
## browser sessions
| Target | Config | Success | Avg latency (s) | Median latency (s) | Avg credits/request | Credits per 1K successful |
|---|---|---|---|---|---|---|
| antibot | browser | 4/10 (40%) | 37.28 | 36.64 | 150.2 | 375,475 |
| ecom | browser | 5/5 (100%) | 49.74 | 45.75 | 123.2 | 123,160 |
| session | browser | 9/10 (90%) | 78.10 | 75.98 | 33.4 | 37,156 |
| spa | browser | 5/5 (100%) | 32.62 | 31.22 | 47.0 | 47,000 |In this log, I noticed much more variable credit costs than the other products. So, I dug a bit deeper and found that data is the driving factor when using Browser Sessions. From my dashboard screenshot below, you can see how much data accounts for most of the credit cost.
Residential Proxies
For this test, I used the requests library and Zenrows Residential Proxies, making 5 requests to static and e-commerce targets and 20 requests to anti-bot and session-based targets. I didn’t test against the dynamic target because a basic HTTP client can’t render JavaScript.
Zenrows Residential Proxies passed 100% of the time against the static target, 0% against the e-commerce target, 20% against the anti-bot target, and 95% against the session-based target. I expected the very low success rate against the anti-bot target because you need more than residential proxies to bypass DataDome.
Using residential proxies, standalone, is billed at 25,000 credits per GB. Here’s the full summary from my terminal:
## residential proxies
| Target | Success | Avg latency (s) | Median latency (s) | Avg KB/request | MB per 1K successful |
|---|---|---|---|---|---|
| antibot | 4/20 (20%) | 5.03 | 4.80 | 145.5 | 711 |
| ecom | 0/20 (0%) | - | - | 14.8 | - |
| static | 20/20 (100%) | 4.58 | 3.83 | 49.8 | 49 |Pricing Deep Dive: standardized metrics and hidden costs
Zenrows bills every plan in credits, and each product draws from the same credit balance, with no defined limit per product. What varies is how each product uses your credit balance, and the actual cost($) per credit based on your plan.
- Fetch: billed at 1 credit per successful request, with a 5x multiplier for JavaScript rendering and 25x for JavaScript rendering + premium proxies.
- Extract: Zenrows says Extract is billed at the same rate as Fetch, but the Zenrows billing header reported 1 additional credit on top of every Extract request.
- Batch: billed the same as Fetch.
- Browser Sessions: 25,000 credits per GB + 5 credits per minute of session time.
- Residential Proxies: 25,000 credits per GB.
Here’s a table showing the actual cost ($) per credit, based on your plan.
| Plan | Price/month | Credits | $ per 1,000 basic pages | $ per 1,000 protected pages (= $ per GB) |
|---|---|---|---|---|
| Free | $0 | 5,000 | – | – |
| Build | $19 | 45,000 | $0.42 | $10.56 |
| Launch | $69 | 250,000 | $0.28 | $6.90 |
| Growth | $199 | 1,200,000 | $0.17 | $4.15 |
| Scale | $549 | 5,000,000 | $0.11 | $2.75 |
I tested on the Build plan, so every credit spent cost me $0.00042 per successful request. Here’s a table showing my actual cost per 1000 successful requests, so you can get an idea of how much I paid for failed requests.
| Static-based | Dynamic SPA | eCommerce target | Anti-bot target | Session-based target | |
|---|---|---|---|---|---|
| Fetch | $0.42 | $2.11 | $10.56 | $10.56 | $0.42* |
| Extract | $0.84 | $2.53 | $10.98 | $10.98 | – |
| Batch | $0.42 | $2.11 | $10.56 | $10.56 | – |
| Browser Sessions | – | $19.84 | $52.00 | $158.53 | $15.69 |
| Residential Proxies | $0.49 | $7.11 | $0.07 |
To get a better understanding of per GB billing, I estimated the cost based on the size of each page received and cross-referenced it against actual dashboard values. My estimate was 1393 credits, while Zenrows account usage snapshot recorded 1398, a 0.5% difference as shown in the screenshot below:
That shows that a session costs roughly as much as what each page weighs, and 1GB isn’t directly equal to 1000 protected pages (Zenrows’ rule of thumb)
Per hour only applies to Browser Sessions, which cost about 300 credits an hour. Based on my plan’s billing structure, running back-to-back requests on the same page would cost about $0.67 an hour on the session-based target, $2.29 on the dynamic target, $4.09 on the e-commerce target, and $6.23 on the anti-bot target.
What’s included vs. what costs extra
The Zenrows pricing docs state that Extract draws credit at the same rate as the underlying Fetch request, but I found that Zenrows own billing header records 1 additional credit more than Fetch on every configuration (base, JS, and JS + premium).
Zenrows also says you only pay for successful requests, but “successful” for Zenrows is different from my own pass criteria. I checked for markers only present on the target’s actual page, while Zenrows counts every successful connection.
Zenrows recorded all 20 “unsuccessful” Batch requests to the session-based target as successful and billed credits. My failed Browser Sessions were billed according to the bandwidth and time; that’s why the table above shows $158.53 per 1000 successful requests to the anti-bot target despite costing $0.06 per session.
Pros & cons (based on test data)
Pros:
- Zenrows’ one API key across all products makes it easy to integrate, and Fetch, Extract, and Batch use the same request format.
- Strong JavaScript rendering. The dynamic page passed 5/5 across all products.
- Zenrows documentation is easy to understand, and it also returns account information on every response, so you can see what you’re paying.
Cons:
- According to my benchmark’s billing report, successful for Zenrows doesn’t always mean useful. Failed browser sessions and logged-out pages that showed a successful HTTP connection were billed.
- No persistent browser identity. Fetch login expired after 10 minutes, plus IP didn’t stay consistent throughout the duration. Zenrows also replaced my defined user-agent.
- Extract had some inconsistencies: Zenrows refused its requests to the session-based target, the extra 1 credit on top of Fetch’s bill, and the e-commerce target output missing review count and the corresponding images.
- Credits are billed differently per product. Fetch: per successful request; Browser Sessions are a combination of bandwidth and session time; and residential proxies are billed per bandwidth.
Comparison table: Zenrows vs. Gologin
| Feature / Metric | ZenRows (Scraping Suite) | Gologin (Web Unlocker & Cloud Browser) |
|---|---|---|
| Product Architecture | One API, multiple metering units | Unified API & Browser Infrastructure |
| Billing Model | Complex credits with 5x–25x multipliers | Transparent fixed cost per 1k requests / flat plan |
| Proxy Inclusion | Standalone, Billed at 25,000 per GB. | Built-in residential proxies (195+ countries) |
| Session Persistence | Limited / stateless HTTP | Full persistent browser contexts & profiles |
| AI Output Format | JSON (extra charge for AI extraction) | Clean Markdown, HTML, or Metadata JSON |
| Anti-Bot Bypass | Server-side stealth API | Fingerprint-level stealth (Orbita Chromium + MCP) |
What survives when the browser closes
My session-based target rejected a session cookie when it came back with a different user agent. It’s a bit more complex than that in real-world use cases because many websites track a lot more signals than your user agent, so if your use case requires you to stay signed in, the question isn’t whether you can log in but if the target site sees the exact same user returning.
Zenrows struggled to keep the same browser identity and IP even within its reported 10 minutes per session ID. I recorded some instances of IP rotation during multiple 10-minute sessions. Staying signed in through Zenrows would require signing in every few minutes, which isn’t always guaranteed because the site sees a new device with every new login.
Gologin’s Cloud Browser solves this problem: the profile identity is the product – and the browser is the disposable part. Same cookies, same device fingerprint, same IP address, run after run. On run 100, the site sees the same user it saw on run 1.
Cloud Browser connects over CDP, so your existing Playwright or Puppeteer script needs only a connection URL:
https://cloudbrowser.gologin.com/connect?token=
token&profile=
{profileId}Here’s an example that runs a saved profile, opens a page using that profile, logs the page title, and stops the profile when it’s done.
import asyncio
import os
import requests
from playwright.async_api import async_playwright
TOKEN = os.environ["GL_API_TOKEN"]
PROFILE_ID = os.environ["GL_PROFILE_ID"]
CDP_URL = f"wss://cloudbrowser.gologin.com/connect?token={TOKEN}&profile={PROFILE_ID}"
STOP_URL = f"https://api.gologin.com/browser/{PROFILE_ID}/web"
def check_start():
r = requests.get(CDP_URL.replace("wss://", "https://"), timeout=60)
if not r.ok:
reason = r.headers.get("X-Error-Reason") or r.status_code
raise RuntimeError(f"Cloud Browser didn't start: {reason}")
async def main(url):
check_start()
try:
async with async_playwright() as p:
browser = await p.chromium.connect_over_cdp(CDP_URL)
context = browser.contexts[0] if browser.contexts else await browser.new_context()
page = await context.new_page()
await page.goto(url, wait_until="domcontentloaded")
print(await page.title())
await browser.close()
finally:
requests.delete(STOP_URL, headers={"Authorization": f"Bearer {TOKEN}"}, timeout=60)
asyncio.run(main("https://www.leboncoin.fr/ad/voitures/3267212200"))Here’s the output:
Audi Q3 Sportback 45 TFSIe 245 ch S tronic 6 S line - VoituresFrequently Asked Questions
Is ZenRows completely free to use?
Zenrows offers a free plan, but it’s capped at 5000 credits per month.
How do ZenRows credit multipliers work for JavaScript rendering?
JavaScript rendering raises the cost of a Fetch request from 1 credit to 5 credits. That’s a 5x multiplier.
What is the difference between ZenRows Scraping API and ZenRows Proxies?
Zenrows Scraping API, now Fetch, is a managed endpoint: you send it your target URL, and it returns the page, rotating proxies, and rendering JavaScript where needed.
Can ZenRows bypass Cloudflare Turnstile and DataDome in 2026?
Yes. Zenrows Fetch with JavaScript rendering and premium proxies enabled can bypass anti-bot solutions.
Why choose Gologin Web Unlocker over ZenRows?
Web Unlocker is more cost-effective: a single flat price per 1,000 requests regardless of how well protected the site is, while Zenrows multiplies its base rate by up to 25x for protected sites.
Conclusion
Zenrows offers a capable scraping API, especially where it’s hardest: 19/20 against e-commerce product pages behind PerimeterX and Akamai, and 5/5 against DataDome protected pages. A single API key and one credit balance for all products make it easy to set up or integrate, at least on paper.
The costs are a lot more difficult to plan for, particularly when running Browser Sessions, which are billed per GB + per session minute. GB makes up the most of any bill you pay for running Browser Sessions, so the size of your target pages dictates your bill. Based on my review, 1000 requests to the anti-bot target cost $158, despite using only $0.06 in session minutes per cycle. Keep in mind that these sessions last only 10 – 15 minutes, depending on the Zenrows product or configuration.
If your use case requires staying signed in across multiple sessions, consider the Gologin Cloud Browser: Each profile keeps its own cookies, fingerprint, and IP address from one run to the next, so sites see the same user at every sign-in. It also offers flat billing rates that make it easy to estimate your project cost before you make any commitment.
