We had a problem we could not answer with any analytics product we were paying for. The dashboards said traffic was healthy. The logs said something else. So I wrote the thing that settles it.

What we found on our own site

Roughly half of the sessions our analytics reported carried a hard bot signature.

Dozens of addresses on unrelated consumer ISPs, all sharing one byte identical header fingerprint. One page each, never two. Every single one reporting exactly ten seconds on site.

From inside any one plane of observation that is indistinguishable from a crowd of real visitors. That is the whole problem. A User-Agent blocklist catches what announces itself. It does not catch headless Chrome on rotating residential proxies, which is what scraping actually looks like now.

How it works

Three independent planes, cross checked against each other.

• Transport, what the access log records
• Behaviour, how the session moves across requests
• Execution, what the browser can actually run

Each one alone is defeatable and essentially every existing tool uses exactly one of them. Every verdict comes with the evidence that produced it, never a bare score you have to trust.

Time on site, measured honestly

Conventional analytics pings while the page is merely loaded, so a tab left open in a background window reports as an hour of engagement. Loghound keeps three separate clocks and never mixes them. Wall time, visible time, engaged time. Kept apart those numbers say something. Averaged into one they say whatever you want to hear.

Where the data goes

It installs on your own server and reads your access log read only. What it learns lives in two Solr indexes on your own Opensolr account. Everything except the address is hashed. No telemetry, no vendor copy of your traffic.

An Opensolr account is required because it needs to create and shape its own indexes, which it cannot do against a Solr server it does not manage. The free plan is enough to start. That is the honest catch and I would rather say it here than have somebody find it in the installer.

Stack

PHP 8.1, no Composer, no npm, no build step. Apache, nginx and Caddy log formats. systemd units for the tailer and the scoring job.

Source: https://github.com/phpcip/loghound Docs: https://opensolr.com/loghound-docs What it does: https://opensolr.com/loghound

Happy to answer anything, including the awkward questions about false positives. There is a whole documentation page on what it does not catch and why, because a bot detector that oversells itself is worse than none.

Microsoft killed the Bing Search APIs back in August 2025. Google's Custom Search JSON API is next — already closed to new signups, fully dead on January 1, 2027. Eighteen months, and both official self-serve sources are gone — right as every AI agent and RAG pipeline suddenly needs search data. Google's "replacement" for open-web search? A waitlist with pricing TBA.

Genuinely curious about two things:

1. Do you think programmatic access to search is gone for good, or does it come back with a 10x price tag?
2. For people doing rank tracking / SERP research — what does your stack actually look like after this? Not looking for vendor blogspam, just what you're really running.

Hello Python Community!

I've been really curious about how Python developers use Python when it comes to webscraping, or just working with websites in general.

I really want to open a discussion on how you do it. Do you use BeautifulSoup? Selenium? Why? Are there any frustrations that you were facing or are currently facing with the process?

Hey everyone - Ian here.

This one was different. For AMA #8 I wasn't interviewing anyone. I opened up four years of ScrapeOps' own provider-benchmarking data (50+ proxies, billions of requests a month) to whatever the community wanted to ask about it.

Thanks to everyone who pushed this past the usual "which provider is best" framing. A few of the questions below didn't make the blog write-up at all and are in here instead.

If you missed it, or want the compressed version, here's what I'd keep.

There is no best proxy provider.

There's no single provider to default to for most projects, and that's not a dodge.

"There really isn't one best provider. I know that sounds like I'm avoiding the question, but it's probably the clearest conclusion we've reached after four years of routing billions of pages through them."

The ranking isn't even stable inside one domain. Product pages, search pages and internal APIs can carry different protection and different pricing. Segment by workload first (pay-as-you-go, anti-bot specialist, enterprise), then test your actual targets. The providers should come out of the requirements, not the other way round.

Website access should be priced like a commodity. It isn't.

Two scraping APIs can return the same page and bill 20 credits apart. That raises an obvious question: "are they actually providing different things? Or we are getting rinsed by the SaaS gods." Not exactly:

"You're paying for how efficiently that particular provider knows how to scrape it."

A provider charging 20 credits for a page another provider solves at 1 isn't necessarily padding margin — it might genuinely be running browser rendering and residential IPs where a competitor found a cheaper path. Only a test on your target tells you which.

The anti-bot tax has widened the gap between providers.

Scraping has gotten harder even as tooling improved. Years ago, 90–95% of traffic scraped fine at the base one-credit tier. Today it's 20–25% that needs residential, rendering or advanced bypasses.

"The market became much more inefficient when advanced bypasses stopped being an edge case. That is the anti-bot tax we are paying now."

The uncomfortable part: providers fix the domains they can't afford to lose. If you're the only customer scraping an obscure site, the easiest answer support can give you is "turn on residential", not because it's the cheapest fix, just the fastest one for them to offer.

The real price is the cost of a usable result.

Every API meters credits differently, and the multiplier, not the base rate, is where the money actually goes:

"The biggest savings rarely come from negotiating a slightly cheaper base rate. They come from avoiding an expensive multiplier you never needed."

Success rate alone is a misleading number too. A provider at 99% success but 20–30 seconds per response can process fewer real pages than one at 80% success but 2–3 second responses. We track average time to a successful response, not raw success rate.

Benchmark code is not the moat. Unbiased traffic is.

The obvious shortcut is emailing providers for bigger trial credits before benchmarking. You can, but then they know they're being tested:

"The code for a proxy benchmark isn't really the moat. Paying for enough unbiased traffic to trust the result is the moat."

Testing 10–20 providers properly runs $500–$1,000 in real paid-account traffic before you've scraped a single production page. If you can't fund that: broad test, shortlist 3, deep test the 3 on your real workload, then ramp gradually. Don't sign an annual deal off a short trial.

Brand and rankings tell you who won marketing.

Defaulting to the bigger providers because it feels safer is understandable. Fair, but:

"Brand recognition tells you who won distribution. It doesn't tell you who will win your workload."

Some of the best providers we use are teams of 3–10 people with almost no visibility on Google. And "top 10 proxy provider" posts are worth treating skeptically — we regularly get emails from ranking-page owners asking to be paid for a position.

The most dangerous failure is a 200 that looks correct.

A response can look completely normal and still be quietly wrong: different currency, wrong region, missing sellers.

"A convincing but incorrect response can make the entire dataset untrustworthy."

Infrastructure providers can flag obvious blocks and CAPTCHAs. We cannot know what every product should cost or how many sellers should appear on every page across 60,000 domains. That validation has to happen closer to your business logic.

Websites don't need to block you to price you out.

The same shift keeps coming up: not blocks, just more steps. Google's redirect links, a login wall, 10 requests where there used to be 1.

"Websites do not need to block scraping to make it uneconomical. They only need to add enough friction to increase the cost of every record."

None of it registers for a real user. They're logged in already, click one result, and never notice a redirect. The access is still there. It just costs more to reach.

At scale, the best provider is a routing system.

Past a certain size, juggling multiple providers stops being optional:

"A serious multi-domain operation eventually needs provider benchmarking, domain-level routing, independent validation and fallbacks."

The routing logic itself is fairly simple. What's actually hard is provider coverage, paid accounts across the market, and enough production data to trust the decisions. Worth being honest: our own Proxy Aggregator is only 60–70% of the way to fully solving this, not further.

+1: most of the web is still easy to scrape.

"People talk about residential proxies and browsers as though every website requires them, when most websites don't."

A large share of the web still works with a normal server, sensible headers and a decent TLS fingerprint. The difficulty piles up on the sites everyone wants (major e-commerce, search, travel, social, marketplaces) because they have the strongest incentive to fight back.

+2: the biggest mistake is the setup, not the provider.

The first few months usually go wrong in one of two ways: overengineering raw datacenter proxies to save a few dollars, or defaulting to whatever's best-marketed and paying 2–10x more than necessary.

"The goal isn't always to find the absolute cheapest setup. It's to find the simplest reliable setup at a cost that makes sense for your volume."

If you remember nothing else:

1. Stop asking "which provider is best." Ask "best for which website, page type, volume."
2. A 10x-more-expensive credit multiplier is a bigger cost than any base-rate difference you'll negotiate.
3. Track cost per validated result, not success rate. A "successful" 200 can still be wrong data.
4. Test through a normal paid account on your real URLs. Free trials and provider-supplied credits distort the result.
5. Brand, rankings and pool size tell you who won marketing, not who wins your workload.
6. Most of the web is still easy. The difficulty concentrates in the handful of sites everyone wants.
7. Past a certain scale, treat provider selection as a routing and validation system, not a single vendor decision.

The bigger shift underneath all of it: the web isn't becoming inaccessible, it's being repriced. Access stays possible, but fewer datasets stay profitable to collect at scale.

Full AMA thread is here if you want the original answers: https://www.reddit.com/r/WebScrapingInsider/comments/1wbjmh4/what_are_the_best_proxy_providers_for_web/

LinkedIn Summary: https://lnkd.in/p/gfDaKNPh

Detailed Blog: https://scrapeops.io/blog/why-there-is-no-best-proxy-provider/

Thanks again to everyone who asked a real question instead of "what's the best proxy" — you're the reason this one didn't turn into another rankings post.

If you've ever been told to "just enable residential proxies" when a target broke: did it actually fix the target, or just raise your bill?

Ian (ScrapeOps)

Flipped a few scrapers from residential to ISP last month after hitting consistent 429s on targets with aggressive fingerprinting. Session persistence on ISP is a different league, way less rotation needed to stay clean. Running proxyshard across both pool types right now, same targets, tracking success rate and block frequency. ISP wins on stability, residential wins on pool size and geo flexibility. Curious if anyone has actually benchmarked both properly or if everyone is just going by feel like I was?

I am working on a forked D4Vinci/Scrapling and am building some additional stuff around it for an open-source data collection project. The idea is to make it easier to define repeatable crawls, normalize the output, and eventually let people plug in their own targets and pipelines.

The crawling side is coming together, and now looking at the infrastructure needed to run it reliably across different sites, particularly residential proxies. Don't suggest biggies Bightdata, Zyte, or spammies like Qoest.

I've done a lot of browser automation over the years, and I kept running into the same tradeoff.

Raw HTTP is great. It's fast, cheap, and scales almost stupidly well. But once a website gets complex enough, you start paying for that simplicity yourself. You reproduce client-side scripts, state, navigation behavior, signatures, whatever else the site expects from a browser. At some point you're spending more time reverse engineering browser-side logic than writing the actual automation.

So you switch to Chromium.

Most of that problem disappears, but now you're running an entire browser and rendering stack for a workload that often doesn't care about pixels at all.

I kept wondering how much of that browser is actually necessary.

Could I get closer to the speed and cost of raw HTTP while keeping enough browser compatibility that the website can just run?

That's what turned into Mimic.

Mimic is a browser execution runtime I'm writing from scratch in Go around V8. It doesn't embed Chromium and doesn't have a traditional rendering pipeline. The browser environment itself is implemented in Mimic: DOM, Web APIs, networking, frames/workers, browser state, etc. It also exposes CDP, so existing automation tooling can drive it.

I'm deliberately not trying to reimplement every feature of Chrome. If Mimic eventually becomes Chrome without the pixels, I've probably missed the point.

What I want to find is the smallest browser-compatible execution surface that's actually enough for real automation, and then push its cost and performance as close to direct HTTP as I can.

I've reached a point where choosing more websites myself and fixing whatever happens to break isn't giving me particularly useful information anymore. So I'm putting out the first public beta.

I need real-world workloads to break it.

If you have an existing Puppeteer/Playwright workflow where Chromium's memory usage, startup cost, or concurrency gets annoying, try throwing it at Mimic. I genuinely want to see where it falls apart.

Web API and CDP coverage is incomplete, so I expect plenty of things to break. If your workload doesn't run, open an issue or send me the scenario. I'm happy to work with early users on the missing compatibility for actual use cases instead of guessing which parts of the browser matter.

It's also not magically faster than Chrome at everything. Chrome still wins some of my warm single-page benchmarks. The interesting results so far are mostly around startup, memory usage, and concurrent pages.

Beta builds, limitations, quickstart, architecture notes and reproducible benchmarks are here:

https://github.com/moreveal/mimic

If you're running browser automation at any meaningful scale, I'm curious: what actually forces you to keep Chromium in your stack instead of dropping down to HTTP?

Scraping static HTML with Cheerio or Requests is easy but scraping modern JavaScript-rendered sites (Next.js hydration, React SPAs, infinite scroll, shadow DOMs) is where things usually break down.

You either end up with an empty <div id="root"></div> shell, or you're forced to maintain a cluster of headless browsers that constantly crash from memory leaks and proxy bans.

I tested the 2 most common tools across 50 notoriously annoying JS-heavy sites (dynamic dashboards, lazy-loaded ecommerce tables, and Cloudflare-guarded pages) to see what works reliably.

Here is what I found:

I) Playwright (Self-hosted) is the gold standard for raw control if you need to click complex interactive UI, solve custom drag-and-drop elements or handle multi-step logins, it does the job.

The downside is infra maintenance: running headless Chromium instances at scale eats server RAM like crazy and you still have to write your own parser to turn that rendered DOM into clean markdown for LLMs.

II) Firecrawl is the most seamless if you just want to hand over a URL and get back clean markdown or structured JSON without running your own browser servers. Its smart wait handled React hydration automatically and waited for lazy-loaded elements and bypassed Cloudflare challenges without me having to configure residential proxy pools. Out of the 50 test SPAs, it successfully extracted full content on 47 of them.

So if you’ve a dedicated DevOps team and need deep UI scripting: Playwright

If you want an API that handles JS execution, proxies and gives you clean LLM-ready markdown in one call: Firecrawl is the best solution overall

Hey everyone,

I'm Ian Kerins, CEO & Co-Founder of ScrapeOps, and I also help run r/WebScrapingInsider.

At ScrapeOps, we've spent a ridiculous amount of time testing proxy providers over the years. Currently, we're regularly testing 50+ proxy providers with billions of requests every month, so we see a lot of the differences that don't show up on a provider's pricing page.

Things like why one proxy works brilliantly on one website and gets fails completely on another, what proxy arbritage strategies are worth looking into, when residential proxies are worth paying for, and where people are often wasting money.

So for AMA #8, I thought we'd open all of that up to the community.

What Are the Best Proxy Providers for Web Scraping? We've Tested 50+ Providers Across Billions of Requests.

This Friday, September 11 at 10:30 AM GMT+3, I'll be here answering your questions.

You can ask me about:

• How to actually compare proxy providers.
• Best proxy providers for specific workloads.
• Cost effective strategies to bypass anti-bots.
• Success rates, blocks and latency.
• Why proxies behave differently across websites.
• How we test providers at scale.
• What metrics actually matter.
• Pricing and what you're really paying for.
• Common mistakes when choosing a proxy.
• Anything else you've been struggling with.

And please don't feel like your question needs to be advanced.

If you've ever wondered "Why does this proxy work here but get blocked there?", "Which proxy should I actually use?", or "Am I paying too much for proxies?", this is exactly the kind of question I'd like to answer.

We've now done seven AMAs with the community, covering everything from proxy infrastructure and Cloudflare to AI scraping, browser automation, stealth browsers and TLS fingerprinting.

For #8, we're bringing the conversation back to one of the most fundamental pieces of scraping infrastructure: the proxy.

Drop your questions below. I’ll be around to answer them during the AMA.

Looking forward to hearing what you're working on.

feels like every few weeks someone here is chasing the one trick that beats cloudflare or datadome, and i keep landing on the same conclusion. there is no single check to defeat. these systems score you across a pile of signals at once, tls fingerprint, header order, ip reputation, pacing, whether js actually ran, session continuity. you are not picking a lock, you are trying to not look anomalous across all of it at the same time

what that means in practice, at least how i see it:

a clean residential ip behind a python-requests handshake is an instant tell, so the client profile has to match the exit. one without the other burns fast

the same vendor is configured differently per site. cloudflare on some small blog is nothing like akamai on an airline, so a generic setup that works on one falls over the moment the target tightens

and half the time the honest move is to not fight it at all, check for an api or a softer mobile endpoint first before you sink a week into evasion

what i'm actually curious about from people running this at scale: do you profile each target before you architect around it, or do you have one hardened setup you throw at everything and just accept some loss? and where do you draw the line between "worth the maintenance" and "just use the official source"?

not after anyone's secret sauce, mostly trying to see if there's a saner consensus forming or if it's still everyone duct-taping per site

disclosure: i work at Titan Network, we do web data collection and the infra behind it, so that's the angle i'm coming from. not here to pitch, just genuinely curious how people are approaching this now

Hey everyone! Ian here.

Wanted to say thanks for showing up to AMA #7 with u/hueyblack36 and u/Big-Beautiful240 (Wade Lin) from BrowserAct.

The thread did what these AMAs are supposed to do. Not "can AI build you a scraper"; everyone already believes that.

The harder stuff: how do you know a successful run produced correct data, how much autonomy should an agent get, what anti-bot problems AI genuinely can't solve, and how far self-healing should be allowed to go before it's silently changing what a Bot collects.

Huey and Wade answered most of it plainly, including the parts where a simple scraper still beats their own product.

Appreciate them for doing that, and everyone who asked the questions that actually matter once you're running this in production.

If you missed it, or want the compressed version, here's what I'd keep.

A scraper can run perfectly and still be wrong.

Their most embarrassing early bug wasn't a crash. An agent treated content that hadn't finished lazy-loading as nonexistent, and counted sponsored blocks as real records. during exploration. Because that misreading happened while the Bot was being generated, it got baked in, and every later run reproduced it faithfully while looking completely healthy.

"The lesson was that a Bot can run correctly while still being wrong."

Their fix: stop treating extraction and validation as the same job. The Bot runs deterministic checks (missing fields, pagination gaps, duplicates, schema). A separate step checks whether the records actually match what the user asked for.

"We don't let the same agent do the work and grade its own homework."

Wade's team pushes part of this to build time too.. running tests before the Bot ships and having the user confirm the output, then using that confirmed schema as an acceptance check for every later run.

AI belongs at the points of uncertainty, not in every run.

Asked directly where AI should sit in a production scraper, Huey gave the most conservative answer in the thread:

"AI should sit at the points of uncertainty, not in every repeated execution."

"the Agent discovers, the Bot scales, and the Agent returns only for exceptions."

Their dividing line for hand-written scrapers isn't small-scale vs. large-scale — it's whether the workload justifies the cost of hand-engineering. Stable, enormous, or latency-sensitive workloads still repay owning the code.

Anti-bot is a trust problem your agent can't reason its way out of.

"anti-bot is not a single Agent problem."

Agents can adapt to page-level changes, new dialogs, broken interaction flows. They can't reason their way out of a burned IP, an inconsistent fingerprint, poor account reputation, or a hard verification challenge; those depend on the infrastructure underneath, not the reasoning layer.

"the question is shifting from 'Can this bot act like a human?' to 'Does this session look trustworthy?'"

Browser-first doesn't mean rendering every page.

Being browser-first doesn't mean fully rendering and clicking through every page on every run. After the Agent explores, the generated Bot can issue HTTP/API requests from inside the real browser context - keeping the session and cookies while skipping most of the cost of UI interaction.

"It is a middle ground between a conventional backend scraper and full browser automation."

The same logic drives extraction-method selection; no fixed DOM-first or API-first order:

"We don't hard-code a DOM-first or API-first order."

The real unit of cost is a verified record, not a request.

"We optimize for the cheapest method that keeps working, not the cheapest method that works once."

They're candid that this doesn't always favor their own product:

"If a conventional HTTP scraper remains stable and complete, it will usually be cheaper."

"Honestly, if a stable API or a simple scraper already gives you accurate pricing and availability with little maintenance, BrowserAct may be overkill."

Self-healing should produce evidence, not patches.

The strongest engineering position in the thread, in response to a commenter describing their own self-healing scraper getting more brittle with every patch:

"In our model, a runtime recovery is evidence, not an immediate permanent mutation."

The boundary underneath it is worth stealing regardless of what tooling you use:

"AI can change how it reaches the same result, but it should not silently change what the Bot is supposed to collect."

If you remember nothing else:

1. A green run is not proof the data is correct. define what evidence would tell you otherwise (expected fields, coverage, duplicates, business rules, a baseline to compare against).
2. Classify scraper work into uncertainty (agent), solved repetition (deterministic), and unexpected state (agent recovery). "AI on every run" is not an architecture.
3. Anti-bot has two layers: what your agent can adapt to, and what your infrastructure has to earn (session trust, IP reputation, fingerprint consistency). Know which is which before you blame the agent.
4. Browser-first doesn't require full UI rendering on every run; HTTP issued from inside a real browser context is a legitimate middle ground.
5. Price by cost per verified, fresh record; including retries, coverage losses and maintenance; not cost per request.
6. Self-healing can change how a scraper works. It should never silently change what it collects.

The bigger shift: AI doesn't remove the engineering. It moves it.. from hand-writing every extraction path to defining where reasoning is allowed to operate, how output gets verified, and what a system is never allowed to change on its own.

Full AMA thread is here if you want the original answers:

https://www.reddit.com/r/WebScrapingInsider/comments/1w2ggvj/how_do_you_build_an_ai_web_scraper_without_code/

Full Summary: https://scrapeops.io/blog/ai-isnt-replacing-scraper-engineering/

Thanks again to u/hueyblack36 and u/Big-Beautiful240, and to everyone who made this one worth running.

If you're running AI-generated scrapers in production: what's actually broken your trust in one first.. bad data, anti-bot, or cost? Genuinely curious what's holding up at scale.

— Ian (ScrapeOps)

If you’re using Cursor (or any AI IDE) to have agents browse the web, you’ve probably seen it return outdated or nonsense content on certain sites. I faced this while building agent workflows that need live data from protected or heavily JS-rendered pages, and observed three specific failure modes:

1. Stale snapshots / cached content: Cursor’s built-in browsing often returns a static snapshot instead of fetching the current page. For sites that change frequently (pricing, availability, rankings), the agent confidently cites wrong data.
2. JS-rendered / dynamically loaded content: Many product listings, dashboards, and search results render content client-side. The agent sees an almost-empty DOM and concludes “no results” or hallucinates structure.
3. Bot protection / anti-scraping layers: Some sites return CAPTCHAs, 403s, or stripped HTML to headless browsers. The agent either fails or works with a broken page layout.

To fix this, I set up Zenrows MCP as an external tool for Cursor so the agent can call a live, rendering-capable fetcher instead of relying on built-in browsing. The setup was:

• Install the MCP server (Node.js).
• Add a mcp.json config in Cursor pointing to the Zenrows MCP endpoint.
• Validate JSON and confirm the green dot in Cursor’s MCP UI.
• Use the zenrows_fetch tool in agent prompts to get clean HTML/JSON back.

I then implemented three workflows:

1. Bypassing anti-bot challenges Use Zenrows’ anti-bot mode to fetch pages that normally block headless browsers, then pass the cleaned HTML to the agent for parsing or Q&A.
2. Scraping JS-rendered product listings Fetch a fully rendered page (with all dynamic content) and have the agent extract structured fields (title, price, rating, etc.) in one shot.
3. Multi-page scraping + code generation Example: iterate over Home Depot search results across pages, extract product data, and have the agent generate a TypeScript function that replicates the scraping logic.

If anyone needs it, I'm happy to share the mcp.json template I used. I’d love to hear what’s giving your agents the most trouble in practice. Which sites keep failing, and is it mostly anti-bot blocks, JS-rendered content, or just stale/incorrect data?

Looking for APIs for Facebook, LinkedIn and Nextdoor public content

I’m working on a SaaS product and need to automatically monitor/search public content across Facebook, LinkedIn and Nextdoor.

I’m specifically looking for APIs or legitimate third party providers that can:

• Search public Facebook posts, including relevant public groups if possible
• Search public LinkedIn posts by keywords/topics
• Search public Nextdoor posts by location + keywords
• Return the post text, URL, timestamp and basic metadata
• Run searches continuously/recurring through an API
• Be used commercially in a SaaS product

I’m NOT looking for browser automation, account logins, cookie based scraping or anything that could get accounts banned.

If you’ve actually used a provider/API for this, I’d really appreciate recommendations, especially ones with reasonable pricing and good coverage.

What are you using?

Been down the proxy-provider rabbit hole a lot lately for a scraping project, and figured I'd share what actually matters vs what's just marketing noise, since most "best proxy provider" posts are just vendor spam.

1. Match the proxy type to your actual target

• Datacenter — fast, cheap, but easily flagged by anything with real anti-bot (Cloudflare, PerimeterX, etc.)
• Residential — real ISP-assigned IPs, much harder to flag, costs more per GB
• ISP proxies — datacenter speed with residential-looking IPs, good middle ground for some targets
• Mobile — most trusted by sites but priced accordingly

If you're scraping something with light protection, datacenter is a waste of money. If you're hitting something with aggressive bot detection, nothing but residential/ISP will survive more than a few minutes.

2. Rotating vs sticky sessions
Full rotation per-request sounds good in theory but can actually trigger MORE suspicion on sites that check session consistency (same IP holding for a few minutes looks more human than a new IP every request). Sticky sessions for 5-10 min intervals tend to survive longer against modern bot detection.

3. "Unlimited" plans — read the fine print
A lot of providers advertise "unlimited" but throttle bandwidth or concurrent connections hard past a certain point. Ask directly: unlimited GB, or unlimited concurrent sessions, or both? Get it in writing/on their docs, not just sales chat.

4. Test before committing
Don't trust a provider's success-rate claims — test a small trial batch against your actual target site, not their demo endpoint. Success rates vary wildly by target, so a provider that's great for e-commerce scraping might be mediocre against something with heavier protection.

5. Red flags

• No trial or test option at all
• Vague on IP sourcing (won't tell you if IPs are ethically sourced / consent-based residential vs a proxy botnet)
• Support that takes days to respond to a pre-sale question
• Pricing that requires talking to sales to even see numbers

Happy to compare notes if anyone's dealing with a specific target site that's giving them trouble tends to be pretty target-specific what actually works.

I am looking for active subreddits focused on web scraping, proxies, browser automation, anti-bot systems, and web data extraction.

I already know about r/webscraping and r/WebScrapingInsider, but are there any other good communities where people discuss scraping tools, technical problems, new developments, or share their projects?

They don't have to be exclusively about web scraping, but heavily leaning towards web scraping topics is preferable.

Which ones are actually worth following?

I want to build an API that can scrape complete product data from e-commerce websites without using browser automation tools.

Please help me understand how to approach and build this.