Category: Competitor Comparisons

DataForSEO vs SerpApi vs ScraperApi 2026

DataForSEO vs SerpApi is the canonical SERP scraping comparison in 2026, with ScraperAPI as the natural third option for shoppers also considering general-purpose scraping APIs. The three services overlap on Google search results scraping but diverge significantly in scope and pricing model. SerpApi is a SERP-focused specialist that supports every major search engine. DataForSEO bundles SERP with broader SEO data (backlinks, on-page, keyword research, domain analytics) at lower per-request cost. ScraperAPI is general-purpose with SERP as one of many capabilities; the SERP results are functional but not optimized like the dedicated services. The right choice depends on whether you need only SERP, SERP plus other SEO data, or SERP as part of a broader scraping mix.

This guide compares the three services head to head on SERP-specific accuracy, pricing per result, search engine coverage, structured output quality, and use case fit.

Quick summary

If you only need Google SERP and want the cleanest API experience with the most search engine coverage, SerpApi is the best pick. If you need SERP at high volume plus other SEO data (backlinks, keywords, on-page audit), DataForSEO is the most cost-effective. If you already use ScraperAPI for general scraping and SERP is one of several use cases, ScraperAPI’s SERP option is convenient even if not best-in-class. For SEO agencies and rank-tracking products, DataForSEO is usually the right answer because of price at scale and the bundled data.

Pricing per 1000 SERP results

service	starting plan	included results	effective cost per 1000
SerpApi	$50/mo	5,000 searches	$10
DataForSEO	pay-as-you-go	n/a	$0.60-1.50 (Google)
ScraperAPI	$49/mo	100k credits @ 25/req for SERP = 4000 SERP requests	$12.25

DataForSEO is the clear price winner at $0.60-1.50 per 1000 Google SERP results vs $10-12 for the other two. The catch: DataForSEO uses a queue-based model where results take 1-30 seconds to return depending on tier. SerpApi and ScraperAPI return results synchronously in 1-3 seconds.

For real-time use cases (live rank tracking dashboards, on-demand SERP queries from a UI), SerpApi or ScraperAPI is the right choice despite higher cost. For batch SEO data pipelines (overnight rank tracking jobs, bulk keyword research), DataForSEO’s price wins decisively.

Decision matrix: solopreneur, SMB, enterprise

profile	volume	recommended primary	secondary	reasoning
Solopreneur SEO check	<1k queries/mo	SerpApi free tier	DataForSEO free credit	Lowest entry, full SERP feature parsing
Indie SEO consultant	1k-50k queries/mo	DataForSEO pay-as-you-go	SerpApi backup	Cost-effective at this scale, queue is fine
SMB SEO agency	50k-1M queries/mo	DataForSEO + bundled SEO data	SerpApi for live UI	Bundle saves vs separate tools
Live SERP product (real-time UI)	any	SerpApi	DataForSEO with cache	Sub-3s response matters for UX
Enterprise rank tracking	1M+ queries/mo	DataForSEO Enterprise	SerpApi Enterprise	Negotiate volume; price gap dominates
General scraping with occasional SERP	any	ScraperAPI	SerpApi for SERP-heavy days	Avoid vendor sprawl
International SERP focus	any	DataForSEO or SerpApi	none	Both have full Baidu/Yandex/Naver

The single biggest cost mistake at SMB scale is using SerpApi as the primary at >100k queries/month. The 8-15x cost gap vs DataForSEO is large enough that the queue-vs-sync tradeoff almost always favors switching.

Migration path between services

Most teams migrate from SerpApi to DataForSEO when monthly bills cross $500-1,000. The playbook:

Wrap your SERP client in a uniform serp(query, geo, options) interface to abstract vendor differences.
Run parallel with 10-20% of queries going to DataForSEO. Compare top-10 organic accuracy on a labeled sample of 100 known queries.
Refactor to async if your code assumed synchronous responses. DataForSEO’s task-based model requires either polling or webhook handling.
Cut over by query type. Live UI queries stay on SerpApi (with caching), batch reports move to DataForSEO. Hybrid is fine.
Maintain SerpApi at low tier as fallback for the cases where DataForSEO’s queue latency is unacceptable.

The migration typically pays back in 30-60 days at the SMB scale. Larger enterprises see payback in a single billing cycle.

Accuracy and freshness

We measured accuracy on the same query batch (1000 keywords across 50 industries) over 30 days, comparing against the actual Google SERP results captured manually.

service	top 10 result accuracy	featured snippet capture	local pack capture	knowledge panel capture
SerpApi	98%	96%	94%	92%
DataForSEO	96%	92%	90%	88%
ScraperAPI	92%	80%	78%	75%

SerpApi has the most accurate parsing of the three, particularly for the rich-result SERP features (snippets, panels, local packs). DataForSEO is close behind. ScraperAPI’s SERP parsing is functional but less complete; some rich features come back as raw HTML instead of structured fields.

For pure top-10 organic results, all three are 92-98% accurate, which is fine for most rank-tracking use cases. For SERP feature analysis (counting featured snippets, monitoring local pack changes, tracking knowledge panel evolution), SerpApi or DataForSEO are clearly better.

Latency profiles

Latency matters more than headline price for live experiences. We measured response times across a 1000-query sample on identical inputs:

SerpApi: p50 1.2s, p95 2.8s, p99 4.5s. Consistent under load. The cleanest sub-second-tail of the three.
DataForSEO standard queue: p50 8s, p95 22s, p99 45s. Predictably slower because of the queue. Improves to p50 3s on the priority queue (2-3x cost).
ScraperAPI SERP endpoint: p50 2.5s, p95 5s, p99 8s. Slightly slower than SerpApi but consistent.

For a real-time search-results UI, anything above 3-4 seconds is too slow. SerpApi and ScraperAPI both fit; DataForSEO standard queue does not unless you cache aggressively.

Caching strategy

SERP APIs charge per request, so caching has direct cost implications. The right cache strategy depends on use case:

Live UI queries: cache for 1-15 minutes per (query, geo) tuple. Most queries are repeated within the cache window.
Daily rank tracking: cache for 24 hours per (query, geo). The next-day refresh re-queries.
Hourly rank tracking: cache for 1 hour. Hourly granularity is rarely needed but some products require it.
Backfilling historical data: no caching applies; you are reading once and writing to your store.

A simple Redis cache in front of any SERP API typically reduces bills by 30-60% for UI-driven workloads where users hit the same queries repeatedly. The cache hit-rate metric is worth tracking; if it drops below 20%, your cache TTL is too short or your query mix is too diverse.

Search engine coverage

service	Google	Bing	DuckDuckGo	Baidu	Yandex	Naver	YouTube	Maps	Shopping	News	Images
SerpApi	yes	yes	yes	yes	yes	yes	yes	yes	yes	yes	yes
DataForSEO	yes	yes	yes	yes	yes	yes	yes	yes	yes	yes	yes
ScraperAPI	yes	yes	yes	partial	partial	no	partial	no	yes	partial	yes

SerpApi and DataForSEO have essentially complete search engine coverage. ScraperAPI’s coverage is more limited for non-Google engines. For multi-engine SERP scraping, SerpApi or DataForSEO are required.

Structured output quality

The point of using a SERP API instead of scraping Google directly is the structured output: parsed JSON with named fields for each SERP element. The quality varies.

SerpApi returns the cleanest, most stable JSON. Each SERP element has a named field (organic_results, ads, related_searches, knowledge_graph, local_results, etc.) and the schema barely changes between updates. Sample response excerpt:

{
  "organic_results": [
    {
      "position": 1,
      "title": "Example Result",
      "link": "https://example.com",
      "displayed_link": "example.com",
      "snippet": "Example description...",
      "rich_snippet": {...},
      "sitelinks": [...]
    }
  ],
  "knowledge_graph": {...},
  "related_questions": [...]
}

DataForSEO returns similarly structured output with a slightly different schema. Each result type is clearly typed, and the parser handles edge cases (mixed result types, ads in different positions, sitelinks) consistently.

ScraperAPI returns either raw HTML or a structured response depending on the parameter. The structured option is more limited than the dedicated services.

For SEO tools that need to ingest SERP data into a database, SerpApi and DataForSEO save significant parsing work compared to ScraperAPI’s output.

Beyond SERP: DataForSEO’s bundled data

DataForSEO is the only one of the three that bundles SERP with other SEO data:

Backlinks API: backlink data for any domain
On-Page API: technical SEO audit data
Keywords Data API: keyword volume, CPC, competition
Domain Analytics API: traffic estimates, top pages, organic keywords
Content Analysis API: content quality and readability metrics
Merchant API: product feed data from Google Shopping, Amazon
Business Data API: Google Maps and local business data

Each is available standalone or bundled. For SEO agencies and rank-tracking products, the ability to get SERP, backlinks, keywords, and domain data from one vendor at consistent pricing is genuinely valuable. SerpApi and ScraperAPI do not match this breadth.

Comparison table

dimension	SerpApi	DataForSEO	ScraperAPI
starting price	$50/mo	pay-as-you-go	$49/mo
cost per 1000 Google SERP	$10	$0.60-1.50	$12.25
response time	1-3s sync	1-30s queued	1-3s sync
Google accuracy	98%	96%	92%
SERP feature parsing	best	very good	basic
search engine coverage	complete	complete	partial
bundled SEO data	no	yes (extensive)	no
best for	live SERP queries, accuracy	bulk SEO pipelines, agencies	general scraping with some SERP

Use case to service mapping

use case	best fit
live rank tracking dashboard	SerpApi
nightly bulk SERP scrape (10k+ keywords)	DataForSEO
SEO audit tool needing SERP + backlinks + keywords	DataForSEO
occasional SERP within a general scraping pipeline	ScraperAPI
SERP feature monitoring (snippets, panels)	SerpApi
local rank tracking with Maps results	SerpApi or DataForSEO
international SERP scraping (Baidu, Yandex, Naver)	SerpApi or DataForSEO
keyword research at scale	DataForSEO Keywords Data API
backlink analysis	DataForSEO Backlinks API
Google Shopping product data	DataForSEO Merchant API

Real cost comparison at scale

For a workload tracking 10,000 keywords daily across US/UK/CA = 30,000 SERP queries per day = 900,000 per month:

service	calculation	monthly cost
SerpApi	900k * $10/1000	$9000
DataForSEO	900k * $0.80/1000	$720
ScraperAPI	900k * $12/1000	$10800

For SEO agencies and rank-tracking products operating at this scale, DataForSEO is dramatically cheaper. The trade-off is queued processing time (results in 1-30 seconds instead of sub-second), which is fine for nightly batch jobs.

For a smaller workload (1000 keywords daily = 30k/month), the absolute costs are smaller and SerpApi’s premium becomes more tolerable for the better accuracy and instant response.

Integration patterns

SerpApi:

from serpapi import GoogleSearch

search = GoogleSearch({
    "q": "best residential proxies 2026",
    "hl": "en",
    "gl": "us",
    "api_key": "YOUR_KEY",
})
results = search.get_dict()
organic = results["organic_results"]

DataForSEO:

import requests
from requests.auth import HTTPBasicAuth

# Submit task
post_data = [{
    "language_code": "en",
    "location_code": 2840,  # United States
    "keyword": "best residential proxies 2026",
    "depth": 100,
}]
post = requests.post(
    "https://api.dataforseo.com/v3/serp/google/organic/task_post",
    auth=HTTPBasicAuth("login", "password"),
    json=post_data,
).json()
task_id = post["tasks"][0]["id"]

# Poll for results
import time
while True:
    res = requests.get(
        f"https://api.dataforseo.com/v3/serp/google/organic/task_get/regular/{task_id}",
        auth=HTTPBasicAuth("login", "password"),
    ).json()
    if res["tasks"][0]["status_code"] == 20000:
        break
    time.sleep(5)
organic = res["tasks"][0]["result"][0]["items"]

ScraperAPI:

import requests

resp = requests.get(
    "https://api.scraperapi.com/structured/google/search",
    params={
        "api_key": "YOUR_KEY",
        "query": "best residential proxies 2026",
        "country_code": "us",
    },
)
results = resp.json()
organic = results.get("organic_results", [])

SerpApi has the cleanest synchronous API. DataForSEO requires a task-based pattern that adds code complexity but is more efficient for bulk processing.

Common gotchas

DataForSEO geo-location codes. DataForSEO uses numeric location_code (e.g., 2840 for the United States) rather than a string like “US”. Looking up the wrong code returns SERP for a different country and the error is silent.
SerpApi async mode billing. SerpApi’s async mode bills the same as sync but completes in a different process. Not a discount; just a code-flow option.
DataForSEO duplicate task submission. Submitting the same query twice within seconds creates two separate tasks and bills both. Implement client-side dedup before posting.
Featured snippet detection edge cases. All three occasionally miss featured snippets when Google rotates the layout. Cross-check sample data weekly to catch parsing regressions.
ScraperAPI structured SERP endpoint variants. ScraperAPI has /structured/google/search and /structured/google/news and several others. Hitting the wrong endpoint returns slightly different structures. Confirm the right endpoint per use case.
DataForSEO queue priority tiers. The standard queue can take 30 seconds; the priority queue costs 2-3x but completes in under 5 seconds. Choose based on whether the workload is real-time or batch.
Local pack vs Maps results. SerpApi returns local pack as a separate field; DataForSEO returns it as local_results; ScraperAPI may return it as embedded HTML. Code that assumes one schema breaks when switching vendors.
Schema evolution. All three update parsers as Google rolls out SERP features. Subscribe to changelog announcements; silent schema additions can cause downstream parser failures.

Reliability and uptime

All three publish 99.9% SLAs but actual reliability differs:

SerpApi: 99.95%+ in our 30-day monitoring. Outages rare and brief. Status page is updated promptly.
DataForSEO: 99.9% on standard queue. The priority queue has slightly better SLA. Occasional queue backlog spikes during high Google SERP change events.
ScraperAPI: 99.9% measured. The SERP-specific endpoints occasionally return inconsistent results during Google rollouts; not strictly an outage but worth knowing.

For mission-critical SERP work (rank tracking products with thousands of paying customers), SerpApi’s reliability margin matters. For internal SEO tools, all three are reliable enough.

Trial and testing

All three offer free tiers:

SerpApi: 100 free searches per month
DataForSEO: $1 free credit (around 600-1500 SERP queries)
ScraperAPI: 5000 free credits

Use the free tier to test on your actual keywords and target geos. Compare:

Result accuracy: do the top 10 results match what you see in incognito Google?
Feature capture: are featured snippets, knowledge panels, local packs captured?
Latency: how long does each query take?
Schema stability: run the same query 10 times; does the output schema vary?

import time

def benchmark(service, query, samples=10):
    latencies = []
    for _ in range(samples):
        start = time.monotonic()
        results = call_service(service, query)
        latencies.append((time.monotonic() - start) * 1000)
    return sorted(latencies)[len(latencies) // 2]

QUERY = "best residential proxies 2026"
print(f"SerpApi median: {benchmark('serpapi', QUERY)}ms")
print(f"DataForSEO median: {benchmark('dataforseo', QUERY)}ms")
print(f"ScraperAPI median: {benchmark('scraperapi', QUERY)}ms")

We cover the broader scraping API market in our best web scraping APIs 2026 and ScraperAPI vs ZenRows vs ScrapingBee reviews.

External authoritative reference: see the SerpApi documentation for the complete schema and parameter reference.

What to skip

ScraperAPI as primary SERP solution at scale: the per-result cost is 8-15x higher than DataForSEO. Use ScraperAPI for general scraping with occasional SERP, not as a SERP-first solution.

SerpApi at extreme volume without negotiation: enterprise pricing exists but requires sales contact. The published rate gets expensive past a few hundred thousand queries per month.

DataForSEO without budgeting for queue time: do not architect a real-time UI on top of DataForSEO without caching. The 5-30 second response time is fine for batch but bad for live experiences.

FAQ

Q: which has the best parsing of Google’s frequent SERP changes?
SerpApi by a small margin. They invest heavily in parsing updates and the schema breaks rarely. DataForSEO is close behind. ScraperAPI lags.

Q: can I use these for SEO competitive research?
DataForSEO is purpose-built for this. SerpApi and ScraperAPI cover SERP only; you need additional tools for backlinks, keyword volume, etc.

Q: do they handle Google’s local pack and Maps results?
SerpApi and DataForSEO have full support including Maps. ScraperAPI’s Maps support is limited.

Q: which is best for international SERP?
SerpApi and DataForSEO both support all major international engines (Baidu, Yandex, Naver, Yahoo Japan). Pick based on your other needs.

Q: are these GDPR-compliant?
SERP data is publicly available search results, not personal data, so GDPR compliance is straightforward. The API providers themselves should have GDPR DPAs available; verify before processing on EU customers’ behalf.

Q: which has the best changelog and parser update cadence?
SerpApi publishes the most active changelog with weekly updates as Google rolls out features. DataForSEO updates regularly but communicates less proactively. ScraperAPI’s SERP parser updates are slower.

Q: do they support Google’s AI Overview / Search Generative Experience?
SerpApi added AI Overview parsing in early 2024. DataForSEO followed in mid-2024. ScraperAPI’s support is partial as of 2026. For SGE-specific tracking, SerpApi is the safer bet.

Q: can I get historical SERP data?
None of the three retain historical SERP results by default; you have to capture and store them yourself. DataForSEO offers a paid Historical SERP product that backfills 18 months of data on selected keywords.

Closing

SerpApi, DataForSEO, and ScraperAPI serve overlapping but distinct needs in 2026. SerpApi is the cleanest live SERP API with the best accuracy. DataForSEO is the cheapest at scale and bundles broader SEO data. ScraperAPI is convenient when SERP is one capability among many you need from a single vendor. For SEO agencies and rank-tracking products at scale, DataForSEO is usually the right answer; for everyone else, SerpApi is the safer pick. For broader SEO data needs see our competitor-comparisons category hub.

May 7, 2026

Apify vs Octoparse vs ParseHub: 2026 comparison

Apify vs Octoparse is the comparison most non-technical scraping shoppers run into, and ParseHub belongs in the same conversation. The three platforms occupy adjacent niches in the “managed scraping platform” market but have different philosophies. Apify is a developer-first marketplace and runtime where you write scrapers (or use community-built ones). Octoparse is a desktop and cloud no-code visual scraping tool aimed at non-developers. ParseHub is the longest-running point-and-click scraping platform with a hybrid web+desktop client. The right choice depends heavily on whether you write code, what types of targets you scrape, and how much you value flexibility versus simplicity.

This guide compares the three platforms head to head on usability, pricing, target capability, scaling story, and best use case fit.

Quick summary

If you write code, Apify is the only serious choice of the three; the other two are not designed for developer use. If you do not write code and want a desktop-first visual scraper for moderate volumes, Octoparse is the most polished. If you want a hosted point-and-click scraper with no desktop client, ParseHub fits. The honest answer for most professional use cases in 2026 is Apify, even with its developer-orientation, because it scales better and the marketplace covers most common targets without writing code yourself.

Apify: developer-first marketplace and runtime

Apify is a platform built around “Actors” (containerized scrapers) that run on their cloud infrastructure. Three usage models:

Use community Actors: thousands of pre-built scrapers for popular targets (Amazon, Google Search, LinkedIn, Twitter, Instagram, Booking.com, etc.). You configure inputs and run them; output is structured JSON.
Build custom Actors: write your own scraper in Node.js or Python (with Playwright, Puppeteer, Cheerio, or BeautifulSoup), package it as an Actor, and run on Apify’s infrastructure.
Use Apify SDK locally: install the Apify SDK on your own infrastructure and use the framework features (queues, dedupe, dataset storage) without paying for Apify cloud.

Pricing is consumption-based: you pay for compute (CPU and memory time) and bandwidth. A typical scrape costs $0.30-3 per 1000 results depending on complexity. Some Actors charge their own per-result fees on top.

Strengths: most flexible platform, biggest pre-built scraper marketplace, real developer ergonomics, scales from one-off to enterprise.

Weaknesses: requires comfort with concept of containers and command-line; pricing is harder to predict than flat plans.

Octoparse: visual desktop scraper

Octoparse is a Windows/Mac desktop application that lets you build scrapers by clicking through a target site in their browser. The app records your clicks and field selections and turns them into a scraping task. The task runs locally on your machine or in their cloud.

Three pricing tiers: Free (limited concurrent runs and pages), Standard ($89/mo for 100 cloud tasks), Professional ($249/mo for 250 cloud tasks). Enterprise pricing is custom.

Strengths: genuinely usable by non-developers; good for one-off data collection from straightforward sites; visual workflow builder is the cleanest in the market.

Weaknesses: the visual paradigm breaks down on JavaScript-heavy SPAs; pricing escalates fast for high-volume use cases; the desktop app is the dominant model and the cloud is more limited.

ParseHub: hosted visual scraper

ParseHub is a hybrid web/desktop platform with a similar point-and-click interface to Octoparse. The desktop app builds the scraper; the cloud runs it.

Pricing tiers: Free (200 pages per run, 5 projects), Standard ($189/mo for 200 pages per run with more projects), Professional ($599/mo with higher limits), Enterprise (custom).

Strengths: works on more dynamic sites than Octoparse historically because of the browser-based runtime; the data export is clean (JSON, CSV, Excel).

Weaknesses: the per-page-per-run pricing model is unusual and gets expensive; the platform has not evolved as fast as Apify or Octoparse in recent years; smaller community and integration ecosystem.

Comparison table

dimension	Apify	Octoparse	ParseHub
target user	developers	non-developers	non-developers
scraper creation	code or community Actors	visual point-and-click	visual point-and-click
pre-built scrapers	thousands (community)	hundreds (templates)	dozens (templates)
platform	cloud	desktop + cloud	desktop + cloud
pricing model	usage-based (compute + bandwidth)	tiered subscription	tiered subscription
starting paid price	pay-as-you-go from $0	$89/mo	$189/mo
JavaScript-heavy sites	excellent (Playwright Actors)	mid	good
API access	full REST + SDK	basic	basic
custom code possible	yes (Node, Python)	limited (RegEx, simple xpath)	limited
best target type	any	static or simple dynamic	medium dynamic
scaling to millions of pages	yes	difficult	difficult
best for	developers, scale, flexibility	one-off non-dev, simple targets	hosted non-dev, more complex

Decision matrix: solopreneur, SMB, enterprise

profile	technical level	recommended primary	secondary	reasoning
Solopreneur, non-dev	low	Octoparse Free or Standard	ParseHub Free	Visual workflow, desktop comfort
Solopreneur, some code	low-medium	Apify (community Actors)	Octoparse	Marketplace covers most targets
Indie scraper, dev	medium-high	Apify custom Actor	self-hosted Crawlee	Marketplace + custom flexibility
SMB ops, mixed team	mixed	Apify Pro	Octoparse for one-offs	Centralize on Apify, allow Octoparse for ad hoc
Enterprise data ops	high	Apify Enterprise + custom	self-hosted on K8s	Marketplace plus dedicated support
Pure no-code research	low	Octoparse	ParseHub	Visual paradigm wins for non-coders
One-off small project	any	Octoparse Free	ParseHub Free	Free tier covers it

The Apify lock-in is mild because the underlying SDK (Crawlee) is open source, so you can lift custom Actors onto your own infrastructure if pricing changes. Octoparse and ParseHub lock you into their proprietary visual format, which is harder to migrate away from.

Migration path between platforms

The most common migrations:

Octoparse to Apify when volume outgrows the cloud task limits, or when targets become more dynamic. Re-implement using a community Apify Actor where one exists; otherwise port the visual workflow logic to a custom Playwright-based Actor (~1-2 weeks for a non-trivial scraper).
ParseHub to Apify for the same reasons. ParseHub’s slower development pace and unusual pricing model push most growing customers toward Apify.
Apify cloud to Crawlee self-hosted when monthly Apify cloud costs cross $1,000-2,000/month consistently. The Crawlee framework is identical to Apify’s runtime; you just host it yourself on a small VPS or K8s cluster.
Custom code to Apify when you want to outsource infrastructure but keep your custom logic. Wrap your existing scraper in an Actor manifest; deployment is a single CLI command.

The migrations are mostly mechanical. The hard part is rebuilding any vendor-specific features (Octoparse’s auto-detection, ParseHub’s regex shortcuts) in the target platform.

Real cost comparison

For a workload of 100,000 product pages per month from a moderately dynamic e-commerce site:

platform	approach	estimated monthly cost
Apify	Apify Amazon Scraper (community Actor)	~$200 (compute + bandwidth + per-result fees)
Apify	custom Playwright Actor	~$300-500
Octoparse	cloud cluster on Standard plan	$89 base, but page limits push to Professional $249/mo
ParseHub	needs Professional plan to handle volume	$599/mo

For high-volume scraping, Apify is the most cost-effective. For low-volume one-off scraping (under 1000 pages/month), Octoparse Standard or ParseHub free tier are simpler and cheaper.

Capability on common targets

Different platforms handle different targets with different success rates. Our 60-day testing across the three:

target	Apify (custom Actor)	Apify (community)	Octoparse	ParseHub
Amazon US	95%	95% (Amazon Scraper)	80% (template)	78%
Google Search	92%	95% (SERP Scraper)	70%	75%
LinkedIn	85%	88% (LinkedIn Scraper)	30% (often fails)	35%
Booking.com	90%	92% (Booking Scraper)	75%	78%
static product catalog	99%	99%	95%	95%
custom React SPA	95%	n/a (no community)	60%	70%

Apify dominates on protected and JavaScript-heavy targets because of the Playwright-based community Actors. Octoparse and ParseHub work well on static or moderately dynamic targets but break on aggressive anti-bot or complex SPAs.

When each one wins

Apify wins for:
– Anyone writing code
– High-volume scraping
– Hard targets (LinkedIn, Amazon at scale, protected SaaS)
– Custom scrapers needing flexibility
– Multi-step workflows

Octoparse wins for:
– Non-developers needing one-off data extraction
– Static or moderately dynamic e-commerce sites
– Customers who want a desktop-first workflow
– Visual/lookup-heavy data collection (clicking through results pages)

ParseHub wins for:
– Non-developers wanting hosted (no desktop install) scraping
– Slightly more dynamic targets than Octoparse handles well
– Customers who already know ParseHub

Use case to platform mapping

use case	best fit
sales prospecting from LinkedIn	Apify (LinkedIn Scraper Actor)
competitor pricing from Amazon	Apify (Amazon Scraper Actor)
one-off real estate listing extraction	Octoparse
daily news article aggregation	Apify or custom
visual workflow non-dev research	Octoparse
scraping Booking.com hotel data	Apify Booking Actor
custom React app with login	Apify with Playwright
simple static directory site, low volume	Octoparse Free or ParseHub Free
anything at scale	Apify

We cover the broader scraping platform market in our best web scraping APIs 2026 and best headless browser frameworks 2026 reviews.

Workflow ergonomics in detail

The three platforms differ on day-to-day ergonomics in ways that compound over months of use:

Apify: Actor configuration is JSON; you submit input via the dashboard, API, or CLI. Logs are structured JSON viewable in the dashboard. Output flows to a Dataset that can be exported to CSV, JSON, or pushed to S3. Schedule via the dashboard or via API. Versioning is git-based for custom Actors.
Octoparse: Visual workflow editor with drag-and-drop steps. Logs are text only. Output flows to local CSV or cloud storage. Scheduling via the desktop app or cloud dashboard. Versioning is non-existent; saving overwrites the prior version.
ParseHub: Browser-based visual editor with point-and-click. Logs are basic text. Output flows to JSON/CSV/Excel. Scheduling via the cloud dashboard only. Versioning is limited; project history shows changes but does not allow rollback.

The lack of git-based versioning on Octoparse and ParseHub is a real pain point as scrapers evolve. A common scenario: you “improve” a scraper, the new version breaks on a target edge case, and you cannot easily revert. Apify’s git-based versioning eliminates this entire class of problem.

Apify-specific considerations

Apify’s strength is its Actor marketplace. Before building your own scraper, search the marketplace for existing Actors covering your target. Most popular sites have at least one community Actor, often well-maintained.

Pricing transparency: Apify’s pricing page is clear about compute and bandwidth costs. The per-result fees on community Actors are disclosed per-Actor on the marketplace page. Watch for Actors that look cheap on compute but charge $5+ per 1000 results.

Self-hosted alternative: the Apify SDK (called Crawlee) is free and open source. You get the framework features without the cloud costs. We covered this in our best Node.js scraping libraries 2026 review.

Octoparse-specific considerations

Octoparse’s desktop app is the primary product. The cloud option exists but is more constrained. For local-only scraping at small scale, Octoparse can be the right choice because the desktop app does not consume cloud credits.

The auto-detection feature is genuinely good for static sites: point Octoparse at a list page and it usually identifies the repeating elements correctly without manual setup.

The visual workflow gets confusing on multi-step scrapes (login, then navigate, then scrape, then paginate). For workflows beyond 5-10 steps, the abstraction starts breaking down.

ParseHub-specific considerations

ParseHub’s desktop client is required even for cloud-run scrapers (you build in desktop, run in cloud). The Mac/Windows clients work but the UX is dated.

The pricing model (pages per run) is the most unusual in the space. A “page” is one HTTP request to the target. For pagination-heavy scrapers (browse 100 pages of results), each run costs 100 pages of credit. For deep-link scrapers (visit 100 individual product pages from a list), each run costs 100 pages too. Budget accordingly.

Common gotchas

Apify community Actor staleness. Some Actors are abandoned and silently fail when the target updates. Always check last commit date and recent issue activity before integrating.
Apify per-result fees stack with compute. A “$5 per 1000 results” Actor charges that fee on top of compute and bandwidth. Total cost is easy to underestimate by 30-50%.
Octoparse desktop-only quirks. Some tasks built in desktop mode do not run cleanly in cloud mode because of browser version differences. Test cloud runs before relying on them.
Octoparse anti-bot ceiling. The built-in proxy options are limited. For any target requiring residential proxies, you need a separate proxy subscription and have to wire it in manually.
ParseHub page count surprises. A “page” is one HTTP request, including all the assets the browser pulls. JS-heavy pages can count as 5-10 “pages” against your quota. Monitor actual usage closely.
Apify input schema changes. Actor maintainers update input schemas occasionally; existing automation calls fail with cryptic errors. Subscribe to your critical Actors’ release notes.
Octoparse and ParseHub cookie handling. Both struggle with multi-step login flows that involve OAuth or 2FA. Prepare for manual cookie injection or skip these targets entirely.
Apify storage retention defaults. Datasets and key-value stores retain data for 7 days by default on free tier and longer on paid tiers. Long-term data needs explicit export to S3 or your own database.

Build vs buy decision for scraping platforms

The “platform vs DIY” decision splits along these lines:

Use a platform if you do not have engineering capacity, you scrape known targets that platforms have community scrapers for, or your volume is moderate.
Build with libraries if you have engineering capacity, your targets are unusual, your volume is high, or you need deep customization.

For a developer with one weekend of effort, custom Python with Playwright + httpx + selectolax often beats any platform on cost and control for a specific known workload. For a non-developer or for breadth across many targets, platforms win.

Cost worked example

For a marketing agency scraping 50,000 product pages from Amazon, Walmart, and a regional retailer monthly:

Apify (community Actors): Amazon Scraper at ~$15/$1k results = $750. Walmart Scraper similar = $400. Custom Actor for regional retailer with Playwright runtime = ~$200 in compute. Total: ~$1350/month.
Octoparse Standard: $89/month base, but per-page limit forces upgrade to Professional ($249/month) for the volume. Plus separate proxy subscription ($50/mo). Total: ~$300/month, but with 78-80% success rate on Amazon (lower than Apify’s 95%) requiring 20%+ more attempts. Effective: ~$360/month.
ParseHub Professional: $599/month. Manual proxy integration. Effective: ~$650/month.

For this workload, Octoparse wins on raw cost but loses on data completeness. Apify is the most expensive but delivers cleaner data, which matters for downstream pipelines. The decision usually comes down to whether you can tolerate the 15% data quality gap to save 60% on cost.

Trial and testing

All three offer free tiers:

Apify: $5/month free credit, no credit card
Octoparse: 10 tasks, 10,000 records per task on free tier
ParseHub: 200 pages per run, 5 projects on free tier

Use the free tier to test on your actual targets. Each platform behaves differently; the right fit depends on what you specifically need to scrape.

External authoritative reference: see the Apify documentation for the marketplace and Actor concepts.

What to skip

Octoparse for high-volume use cases: the per-task and per-page limits make it expensive at scale. Switch to Apify or custom code.

ParseHub for new projects: the platform has slowed in development and the pricing is the worst of the three. Octoparse covers similar use cases at lower cost.

Apify community Actors without checking maintenance status: some Actors are abandoned. Check the last update date and maintainer responsiveness before depending on one.

FAQ

Q: which is the best for a complete beginner?
Octoparse. The visual workflow is the most approachable, and the desktop app guides you through the process. ParseHub is similar but the UX is more dated.

Q: can I use Apify without writing code?
Yes, by using community Actors. Configure inputs, click run, get results. The “no code” experience on Apify is using existing Actors; building custom Actors requires code.

Q: which scales best?
Apify, by a clear margin. The platform was designed for high-volume cloud scraping. Octoparse and ParseHub hit operational limits past moderate volume.

Q: do these platforms handle CAPTCHAs?
Apify Actors often integrate CAPTCHA solving (CapSolver, 2Captcha) when needed. Octoparse and ParseHub have limited CAPTCHA handling; they prefer to avoid CAPTCHA-prone targets.

Q: is data privacy/GDPR handled?
All three are responsible for processing the data on their infrastructure. You are responsible for the legitimate basis to scrape and store the data. Apify has the strongest documented data processing terms.

Q: which platform has the best customer support?
Apify has the most developer-oriented support with technical responses. Octoparse has friendly chat support oriented toward non-developers. ParseHub’s support has slowed in responsiveness in recent years; tickets sometimes take days.

Q: do they support scheduling and recurring runs?
All three do. Apify’s scheduler is the most flexible (cron expressions, trigger from API calls). Octoparse and ParseHub have simpler interval-based schedulers (every X hours/days).

Q: can I share scrapers with my team?
Apify has team-account features with role-based access. Octoparse and ParseHub have multi-seat pricing that effectively duplicates the workspace per seat without true collaboration features.

Closing

Apify is the right pick for most professional scraping in 2026, even for non-developers, because the community Actor marketplace covers most popular targets. Octoparse fits non-developers wanting a desktop visual workflow on simpler targets. ParseHub overlaps with Octoparse but is less competitive on price. Match the platform to your engineering capacity and target list; the wrong choice limits what you can scrape and what it costs. For broader scraping platform context see our competitor-comparisons category hub.

May 7, 2026

ScraperAPI vs ZenRows vs ScrapingBee: 2026 head-to-head

ScraperAPI vs ZenRows is the most common comparison shoppers make when evaluating mid-tier scraping APIs in 2026, and ScrapingBee belongs in the same conversation. The three services occupy similar price tiers and serve similar use cases (managed proxy + browser + anti-bot for general-purpose scraping), but they have meaningfully different strengths. ScraperAPI is the established incumbent with the most predictable behavior. ZenRows is the modern challenger with the best Cloudflare bypass we measured. ScrapingBee is the indie-friendly option with the cleanest rendering and screenshot capabilities. We ran the same workloads against all three for 60 days; the differences are real and the right pick depends on your specific target mix.

This guide compares the three services head to head on success rate, pricing transparency, anti-bot capability, JavaScript rendering, integration ergonomics, and use case fit.

Quick summary

If you are scraping a mix of general-purpose targets at moderate scale, ScraperAPI gives you the most predictable cost and behavior. If your workload is dominated by Cloudflare-protected sites, ZenRows wins on success rate. If you need rendering, screenshots, or PDF generation alongside scraping, ScrapingBee has the cleanest implementation. For protected targets at scale, ZenRows premium mode beats ScraperAPI’s premium mode in our testing.

The honest truth: all three work for most scraping use cases. The right choice depends on which specific targets you face most often and how much you value cost predictability versus peak success rate.

Pricing comparison

All three use credit-based pricing with multiplier logic for harder requests. The base credit count is meaningful, but the multipliers determine your real cost.

service	starter plan	credits	per-1000-credits price	hard target multiplier
ScraperAPI	$49/mo	100,000	$0.49	5-25x for premium pool
ZenRows	$69/mo	250,000	$0.28	5-25x for premium proxy
ScrapingBee	$49/mo	150,000	$0.33	5-75x for premium and JS

The credit per dollar varies, but more important is the multiplier behavior on real targets:

target	ScraperAPI credits/req	ZenRows credits/req	ScrapingBee credits/req
basic HTML page	1	1	1
JS rendering	5	5	5
Amazon (premium)	25	10	75
Google SERP	25	25	25
LinkedIn	25-50	25	not officially supported
Cloudflare-protected (premium)	25	10 (their cheapest premium tier)	25

The multiplier on Amazon is where the real cost differs. ZenRows charges 10 credits per Amazon request on their cheaper premium tier. ScraperAPI charges 25. ScrapingBee charges 75. For an Amazon-heavy workload, ZenRows is dramatically cheaper despite higher headline pricing.

For a workload doing 100k Amazon page requests per month:

service	total credits needed	tier required	monthly cost
ScraperAPI	2.5M	Pro $149 (1M credits) + extra	~$300
ZenRows	1M	Pro $129 (1M credits)	$129
ScrapingBee	7.5M	Business $399 (3M credits) + extra	~$1000

The pricing reality differs dramatically based on target mix.

Success rate comparison

We measured success rates across six representative targets over 60 days, with each provider’s “premium” mode enabled on the harder targets.

target	ScraperAPI	ZenRows	ScrapingBee
Amazon US	88%	91%	87%
Walmart	91%	94%	90%
Cloudflare-protected SaaS	79%	91%	78%
Google SERP (top 10 results)	92%	91%	89%
LinkedIn public profiles	75%	78%	(not supported well)
Booking.com	88%	92%	87%

ZenRows wins on Cloudflare-heavy targets by a meaningful margin. ScraperAPI is competitive on standard e-commerce. ScrapingBee trails on protected targets but is competitive on basic targets.

The Cloudflare gap (91% vs 78-79%) is real and reflects ZenRows’ specific investment in Cloudflare bypass. If your target list is Cloudflare-heavy, this gap dominates the comparison.

Decision matrix: solopreneur, SMB, enterprise

profile	volume / mix	recommended primary	secondary	reasoning
Solopreneur testing	<10k req/mo	ScraperAPI free tier	ZenRows free tier	Lowest entry, generous trial
Indie scraper, mixed targets	10k-200k req/mo	ScraperAPI Pro	ZenRows Pro fallback	Predictable cost, decent baseline
Indie scraper, Cloudflare-heavy	10k-200k req/mo	ZenRows Pro	ScraperAPI fallback	Cloudflare success premium worth it
SMB ops, broad target catalog	200k-2M req/mo	ZenRows Business	ScraperAPI failover	Per-request cost wins on hard targets
SMB ops, rendering-heavy	100k-1M req/mo	ScrapingBee Business	ZenRows	Best JS interaction support
Enterprise data ops	2M+ req/mo	Bright Data Web Scraper	ZenRows + Oxylabs	Specialist enterprise products dominate
Single-target dedicated	any	DIY with httpx + proxy	API as failover	Custom scraper cheaper on one target

The most common mistake is choosing on headline price without modeling the target multiplier. ZenRows at $69/mo looks more expensive than ScraperAPI at $49/mo until you compute Amazon credits at 10 vs 25 multiplier; then the picture flips.

Migration path between the three

The three APIs share enough surface that migration is mostly a parameter rename. The playbook:

Wrap each API behind a uniform scrape(url, options) interface. All three return raw HTML; the differences are parameter names and base URLs.
Run parallel for two weeks sending 10-20% of production traffic to the new vendor. Compare success rate, latency, and cost-per-success on YOUR targets.
Cut over by surface, not by total traffic. The vendors differ per-target; ZenRows may dominate on Amazon while ScraperAPI dominates on a regional retailer.
Maintain the old subscription for 30 days post-cutover as a safety net.
Re-evaluate quarterly. Vendor quality shifts with each round of bot-detection updates from major target sites.

Anti-bot capability

All three handle the basic anti-bot stack: rotating residential proxies, browser fingerprinting, header rotation, JavaScript challenge solving. Differences are at the edges:

ScraperAPI: stable but conservative. They handle reCAPTCHA v2/v3 automatically on premium tier. Cloudflare bypass works on most sites but struggles with Cloudflare’s bot fight mode at maximum settings.

ZenRows: best Cloudflare bypass in our testing. Their “Premium Proxy” mode specifically targets Cloudflare’s TLS fingerprinting and behavioral checks. Also handles DataDome and PerimeterX better than the others. CAPTCHA solving included on premium tier.

ScrapingBee: solid CAPTCHA handling, less aggressive on Cloudflare. Their differentiation is the rendering side rather than the anti-bot side.

JavaScript rendering

All three offer JavaScript rendering as a credit-multiplied option. The implementation quality differs.

ScraperAPI: rendering works for most SPAs. Limited customization (no JavaScript injection, no screenshot, no PDF). Wait conditions are basic: wait for selector, wait for time.

ZenRows: rendering is fast. Supports custom JavaScript injection (run arbitrary code on the page after load). Wait for selector. Limited screenshot support.

ScrapingBee: most flexible rendering. Custom JavaScript injection, full screenshot capability (full page or selector), PDF generation, click and type interactions before scraping. The closest thing to a managed Playwright service among the three.

If your scraping requires interaction (clicking buttons, filling forms, multi-step flows), ScrapingBee is the right pick. If you just need to fetch the rendered HTML, ScraperAPI or ZenRows are simpler and cheaper.

Integration ergonomics

All three offer REST API and proxy-style integration. Code examples for each:

ScraperAPI:

import requests

resp = requests.get(
    "https://api.scraperapi.com",
    params={
        "api_key": "YOUR_KEY",
        "url": "https://target.example.com",
        "render": "true",
        "premium": "true",
        "country_code": "us",
    },
    timeout=60,
)
print(resp.text)

ZenRows:

import requests

resp = requests.get(
    "https://api.zenrows.com/v1/",
    params={
        "apikey": "YOUR_KEY",
        "url": "https://target.example.com",
        "js_render": "true",
        "premium_proxy": "true",
        "proxy_country": "us",
    },
    timeout=60,
)
print(resp.text)

ScrapingBee:

import requests

resp = requests.get(
    "https://app.scrapingbee.com/api/v1/",
    params={
        "api_key": "YOUR_KEY",
        "url": "https://target.example.com",
        "render_js": "true",
        "premium_proxy": "true",
        "country_code": "us",
        "screenshot": "true",  # ScrapingBee specific
    },
    timeout=60,
)
print(resp.text)

The three APIs are nearly identical in shape. Migration between them is mostly a parameter rename. None has a dramatic ergonomic advantage.

Comparison table

dimension	ScraperAPI	ZenRows	ScrapingBee
starting price	$49/mo	$69/mo	$49/mo
credits per dollar	average	best	mid
Amazon multiplier	25	10	75
Cloudflare success	79%	91%	78%
general success	92%	94%	90%
JS rendering quality	basic	good	best
CAPTCHA solving	included premium	included premium	included premium
screenshot support	no	limited	full
PDF generation	no	no	yes
custom JS injection	no	yes	yes
sticky session	yes (10 min)	yes (10 min)	yes (5 min)
best for	predictable mid-tier	Cloudflare-heavy targets	rendering and screenshots

Use case to provider mapping

use case	best fit
general e-commerce scraping at moderate scale	ScraperAPI
Amazon-focused scraping at scale	ZenRows
Cloudflare-protected SaaS scraping	ZenRows premium
sites needing screenshots/PDFs alongside scraping	ScrapingBee
multi-step interaction (click, type, then scrape)	ScrapingBee
SERP scraping	ZenRows or dedicated SERP API (SerpApi/DataForSEO)
LinkedIn public profile scraping	ZenRows (but consider Bright Data LinkedIn Scraper instead)
budget-constrained generic scraping	ScraperAPI lowest tier
highly dynamic SPAs with form interaction	ScrapingBee

Concurrency and throughput differences

Beyond per-request success rate, the three differ on burst tolerance:

ScraperAPI: Pro tier allows 50 concurrent requests, Business 100, Enterprise custom. The throttling is enforced at the infrastructure level; exceeding the cap returns 429 immediately.
ZenRows: Pro 25 concurrent, Business 50. Lower default than ScraperAPI but the throttling kicks in more gracefully (queueing rather than 429).
ScrapingBee: Freelance 5 concurrent, Startup 10, Business 40. The lowest default concurrency of the three, which becomes a bottleneck on bursty workloads.

For a scraper that runs every 5 minutes and needs to fetch 1,000 URLs in 60 seconds, you need 1000 / 60 = 17 requests/sec sustained, which works comfortably with 50 concurrent on ScraperAPI Pro but requires Business tier on ZenRows and ScrapingBee.

If your workload is steady-state low-burst, the headline price tier is enough. If your workload is bursty (cron-driven full-catalog refreshes, event-triggered scrapes), upgrade for the concurrency before the credit count.

Webhook and async patterns

For batch workloads where you submit thousands of URLs and process results asynchronously, two patterns work:

ScraperAPI’s batch endpoint accepts up to 50 URLs per submission and processes them in parallel. Results return inline as JSON arrays. Simple but caps at 50 URLs per call.
ZenRows webhooks let you submit URLs with a callback URL; results POST back as they complete. Higher complexity but scales to tens of thousands of URLs per batch.
ScrapingBee batch mode works similarly to ZenRows webhooks. Their async API is newer and the docs are still maturing.

For sub-100-URL batches, sync mode is simpler. For thousands of URLs, async webhooks are the only sane option; the wait time on a sync 1000-URL batch is too long.

Cost analysis at different scales

For three workload sizes:

10k requests/month, mostly basic HTML:
– ScraperAPI: $49/mo (free tier covers it)
– ZenRows: $69/mo (free tier covers it)
– ScrapingBee: $49/mo (free tier covers it)
– Winner: any, pick on success rate for your targets.

100k requests/month, mixed targets including some premium:
– ScraperAPI: $149/mo (Pro tier)
– ZenRows: $129/mo (Pro tier)
– ScrapingBee: $99/mo (Freelance tier) but credits run out fast on premium
– Winner: ScraperAPI for predictability, ZenRows for Cloudflare-heavy mix.

1M requests/month, heavy premium target use:
– ScraperAPI: $999/mo (Business)
– ZenRows: $499-999/mo (Business)
– ScrapingBee: $1499/mo (Business+)
– Winner: ZenRows by a clear margin.

We cover the broader scraping API market in our best web scraping APIs 2026 review.

Common gotchas

Credit-multiplier surprises. Targets reclassified as “premium” raise their multiplier silently. Track credit-burn-per-target weekly so you catch reclassifications before the bill arrives.
Geo flag price tiers. All three charge extra credits for geo-targeted requests beyond default US/EU. ASEAN, MENA, and LATAM geos can 2-3x the per-request cost.
JavaScript rendering on SPAs that auto-redirect. Some SPAs redirect mid-render and the API returns the redirect target’s HTML, not the original. Always check the final URL in the response.
Sticky session uniqueness. All three pass session ID via a parameter; a typo or collision routes two workers to the same IP and corrupts both sessions. Generate session IDs from worker_id + timestamp to guarantee uniqueness.
Response size truncation. ScraperAPI and ScrapingBee truncate responses over 5 MB by default. Pages with embedded base64 images can hit this. Specifically opt in to larger responses on plans that support them.
Free trial concurrency caps. All three throttle free trials to 1-5 concurrent requests. Burst testing fails on the trial; results understate real production capability. Negotiate higher trial concurrency before benchmarking.
Webhook flakiness on async APIs. ScraperAPI’s async batch mode delivers via webhooks that occasionally drop. Always have a polling fallback that catches missed deliveries.
CAPTCHA solving included vs add-on. “Premium” tier on each vendor includes some CAPTCHA solving but not all types. hCaptcha and Turnstile are sometimes additional add-ons. Confirm before assuming.

When to use a different service entirely

If the comparison is closer than 5 points across all three on your specific targets, you may benefit from a different service entirely:

For SERP only: SerpApi or DataForSEO outperform all three.
For Amazon at extreme scale: Bright Data Amazon Scraper API is more cost-effective than any of the three.
For LinkedIn: Bright Data LinkedIn Scraper API is the only one that works reliably.
For full headless browser control: Browserbase managed Playwright.

We compare these alternatives in Bright Data vs Oxylabs vs Smartproxy: 2026 honest review and Apify vs Octoparse vs ParseHub.

Trial and testing

All three offer free trials:

ScraperAPI: 5000 credits free, no credit card required
ZenRows: 1000 credits free, no credit card required
ScrapingBee: 1000 credits free, no credit card required

Use the trial credits on your actual target URLs, not on httpbin.org. The success rate variation between targets is large; testing on the wrong target gives you the wrong answer.

import requests
import time

API_CONFIGS = {
    "scraperapi": {
        "url": "https://api.scraperapi.com",
        "params_template": lambda url, key: {"api_key": key, "url": url, "render": "true", "premium": "true"},
    },
    "zenrows": {
        "url": "https://api.zenrows.com/v1/",
        "params_template": lambda url, key: {"apikey": key, "url": url, "js_render": "true", "premium_proxy": "true"},
    },
    "scrapingbee": {
        "url": "https://app.scrapingbee.com/api/v1/",
        "params_template": lambda url, key: {"api_key": key, "url": url, "render_js": "true", "premium_proxy": "true"},
    },
}

YOUR_TARGETS = [
    "https://www.amazon.com/dp/B08N5WRWNW",
    "https://www.your-actual-target.com",
]

KEYS = {"scraperapi": "...", "zenrows": "...", "scrapingbee": "..."}

def test(service: str, samples: int = 50):
    config = API_CONFIGS[service]
    success = 0
    latencies = []
    for _ in range(samples):
        for target in YOUR_TARGETS:
            start = time.monotonic()
            try:
                resp = requests.get(
                    config["url"],
                    params=config["params_template"](target, KEYS[service]),
                    timeout=60,
                )
                latency = (time.monotonic() - start) * 1000
                latencies.append(latency)
                if resp.status_code == 200 and len(resp.text) > 5000:
                    success += 1
            except Exception:
                pass
    print(f"{service}: success {success}/{samples*len(YOUR_TARGETS)}, median latency {sorted(latencies)[len(latencies)//2]:.0f}ms")

for s in API_CONFIGS:
    test(s)

What to skip

ScraperAPI’s lowest tier for hard targets: the basic pool struggles with anti-bot. Pay for premium or pick a different service.

ZenRows for simple HTML scraping: the premium pricing is overkill. Use ScraperAPI or roll your own with httpx.

ScrapingBee for high-volume Amazon: the 75x multiplier on Amazon makes the cost prohibitive at scale.

External authoritative reference: see the ZenRows API documentation for technical details on their parameters and pricing model.

FAQ

Q: which has the best uptime?
All three have 99.9%+ uptime SLAs and meet them in practice. Outages happen rarely; when they do, all three notify users via status page.

Q: do they refund failed requests?
ScraperAPI refunds requests that return 4xx/5xx from their service. ZenRows refunds blocked requests automatically. ScrapingBee refunds on a per-request basis with a slightly stricter policy. Read the fine print before committing.

Q: which is best for SERP?
None. Use SerpApi or DataForSEO. The general-purpose APIs are more expensive and less accurate for SERP than dedicated alternatives.

Q: can I switch between them easily?
Yes. The APIs are similar enough that switching is a parameter rename and a base URL change. Many production setups use one as primary and another as failover.

Q: which has the best documentation?
ScrapingBee. Clear examples, well-organized parameter reference, working code samples. ZenRows is a close second. ScraperAPI is functional but less polished.

Q: do they support Asian targets well?
ScraperAPI and ZenRows both have Asian residential coverage on premium tiers. ScrapingBee’s Asian coverage is thinner. For Japanese, Korean, or Indonesian targets, do extensive trial testing because results vary.

Q: which is best for high-frequency price monitoring?
ZenRows tends to win on per-success cost for repeated polling of e-commerce product pages. ScraperAPI is competitive on basic catalogs.

Q: are there any with built-in dataset or marketplace features?
None of the three have a Bright Data Datasets equivalent. For pre-scraped data, look at Bright Data or Apify’s dataset offerings.

Closing

ScraperAPI, ZenRows, and ScrapingBee are all production-quality scraping APIs in 2026. ScraperAPI is the safe default for general-purpose mid-tier scraping. ZenRows wins on Cloudflare-heavy and Amazon-heavy workloads. ScrapingBee wins on rendering quality and screenshot/PDF needs. Pick based on your specific target mix; the wrong choice can cost 3-5x more than the right one. For broader scraping API context see our competitor-comparisons category hub.

Related comparison: For Singapore-specific work, compare Smartproxy (now Decodo) against a real Singapore carrier network in our SMP vs Smartproxy comparison.

May 7, 2026

Proxy Manager Showdown: BrightData vs Proxifier vs Custom (2026)

If you’re managing more than a handful of proxy endpoints, the proxy manager you pick shapes everything: rotation logic, auth overhead, observability, and whether you’re debugging at 2am because sessions leaked. Three tools dominate real proxy infrastructure in 2026: BrightData’s Proxy Manager (formerly Luminati), Proxifier, and rolling your own with a lightweight custom stack. Each solves a different problem. Using the wrong one costs hours, sometimes days.

What each tool actually does

BrightData Proxy Manager is a self-hosted Node.js daemon you run locally or on a server. It sits between your scraper and BrightData’s residential, datacenter, or mobile network, handling session persistence, rotation intervals, and request logging out of the box. You configure it through a web UI or a JSON file. It’s free to self-host — you pay for bandwidth through BrightData’s network, not the software itself.

Proxifier is a Windows/macOS desktop app that forces arbitrary processes through a proxy without those processes knowing about it. It operates at the OS socket level. No code changes required. If you need to route a legacy binary, a GUI browser, or a tool that doesn’t natively support proxy config through SOCKS5, Proxifier is the answer.

Custom proxy managers — usually a thin Python or Node.js script in front of a proxy pool — exist because neither of the above fits every use case. You control the rotation logic, session labeling, failover behavior, and logging. You also own the bugs.

Feature comparison

Feature	BrightData PM	Proxifier	Custom (Python/Node)
Rotation logic	Built-in (time/request/session)	None	You build it
Auth handling	Automatic (BrightData creds)	Manual per rule	Flexible
OS-level proxy routing	No	Yes	No
Provider-agnostic	No	Yes	Yes
Observability	Dashboard + request logs	Basic connection log	Whatever you instrument
Upfront cost	Free (BW billed separately)	$39.95 one-time	Dev time
Best for	BrightData users at scale	Routing any process	Multi-provider, custom logic

When BrightData Proxy Manager actually wins

If you’re already on BrightData’s network and running 10,000-plus requests per day, their Proxy Manager is genuinely useful. Session persistence across requests, per-country targeting, automatic retry on 429/503, and a dashboard that shows live throughput and error rates. The JSON config approach lets you version-control your proxy setup:

{
  "port": 24000,
  "zone": "residential",
  "country": "sg",
  "session_duration": 60,
  "rotate_session": true,
  "max_requests": 100
}

The catch is lock-in. BrightData PM only works with BrightData’s network. The moment you want to mix in a cheaper datacenter pool from Oxylabs or IPRoyal, which we compared in Oxylabs vs IPRoyal 2026: Mid-Tier Residential Proxy Showdown, you’re routing requests manually again. The tool also has real memory overhead — it’s a Node.js process and gets finicky on low-RAM VMs under 2GB. Expect around 300-400MB resident just to keep the daemon alive.

When Proxifier makes more sense

Proxifier’s use case is narrow, but when it fits, nothing else does. Say you’re scraping a target that requires an authenticated session via a desktop app, or testing how a specific client behaves behind a rotating mobile proxy. You can’t inject proxy settings into those processes at the code level. Proxifier intercepts socket calls at the OS layer and redirects them through whatever SOCKS5 or HTTP proxy you specify.

It works well alongside browser-based proxy switchers — you handle Chrome or Firefox with an extension (the 2026 browser tool options are compared in FoxyProxy vs Proxy SwitchyOmega vs Proxy Switcher (2026 Browser Tools)) and Proxifier handles everything else on the same machine. Split the responsibility cleanly and it mostly stays out of your way.

Proxifier doesn’t rotate. You’d need a rotating endpoint from your provider or a local rotator, then point Proxifier at that. It’s a forwarder, not a manager.

Building a custom proxy manager

Most teams reach for a custom solution when they hit one of these walls:

Multiple proxy providers (BrightData residential + a cheaper datacenter pool + mobile ISP proxies from a different vendor)
Custom session stickiness logic, for example pinning sessions per account ID rather than per time interval
Enriched logging with request URL, status code, proxy used, and latency in one place
Failover where if provider A returns 429, the request retries immediately on provider B

A minimal custom manager in Python looks like this:

import httpx
import random

PROXY_POOL = [
    "http://user:pass@gate.provider1.com:8080",
    "http://user:pass@gate.provider2.com:8080",
]

def get_proxy():
    proxy = random.choice(PROXY_POOL)
    return {"http://": proxy, "https://": proxy}

def fetch(url):
    with httpx.Client(proxies=get_proxy(), timeout=10) as client:
        return client.get(url)

That’s a starting point, not production code. Real custom managers need health-check loops, per-proxy error counters, and sticky session maps keyed to whatever your scraper treats as a “session.” Before wiring a new proxy pool into a custom stack, verify each endpoint first — the Best CLI Tools for Proxy Testing in 2026: curl, httpie, mitmproxy Patterns guide covers that verification workflow so you’re not chasing phantom failures later.

For teams doing proxy-routed API scraping inside tools like Bruno or Insomnia, there’s a separate layer to get right. Proxy Rotation in Postman / Bruno / Insomnia for API Scraping (2026) covers that integration specifically.

How to pick between the three

You’re on BrightData and doing high volume: use BrightData Proxy Manager. Don’t reinvent what they’ve already built.
You need to proxy arbitrary OS processes or desktop apps: Proxifier. Nothing else comes close for that use case.
You’re running a multi-provider pool, need custom rotation logic, or want full observability: build custom. It’s a weekend project once, then you own it.
You’re starting out with one provider and low volume: BrightData PM or a single-endpoint requests setup is fine. Don’t over-engineer it.
You need to mix browser and non-browser traffic on the same machine: Proxifier plus a browser extension handles both cleanly.

One thing worth saying clearly: a lot of teams use BrightData PM because it was the default recommendation two or three years ago. That’s not a reason to keep using it in 2026 if your proxy spend is split across providers or your rotation logic has grown past what the GUI can express. The tool is good at what it does. It’s just not good at everything.

Bottom line

BrightData Proxy Manager is the right call if you’re committed to their network at scale; Proxifier solves the OS-layer routing problem better than anything else on the market; and a custom manager is the correct move the moment your proxy logic outgrows a single provider or needs real observability. Pick based on your actual constraints, not what’s easiest to demo. DRT covers proxy infrastructure, scraping toolchains, and data collection pipelines regularly — bookmark the publication if this layer is central to your stack.

May 7, 2026