Hey everyone,

I'm Ian Kerins, CEO & Co-Founder of ScrapeOps.io.

After four great AMAs with the r/WebScrapingInsider community, we're excited to bring you our fifth guest.

This Thursday, August 20, at 10:30 AM GMT+3, we'll be joined by Stan Sadokov from NodeMaven for a discussion around one of the questions almost every serious scraping operation eventually has to deal with:

How do you actually choose the right residential proxy provider?

NodeMaven is a proxy infrastructure provider focused on residential, mobile, and ISP IPs, with an emphasis on IP quality rather than simply advertising the biggest pool.

Instead of relying purely on massive raw IP counts, NodeMaven uses real-time quality filtering to identify and remove flagged or low-reputation IPs before they cause problems for customers.

WebScrapingInsider AMA #5 with NodeMaven

During the AMA, we can dig into topics like:

• Residential proxy quality
• How proxy providers measure IP reputation
• Choosing between residential, mobile and ISP proxies
• IP rotation vs. sticky sessions
• Proxy success rates and why they vary by target
• Detecting and removing bad IPs
• Proxy performance for large-scale web scraping
• How proxy pricing actually works
• What to look for when evaluating a proxy provider
• Where residential proxy infrastructure is heading

We've had some great discussions in the community so far:

Our first AMA covered proxy infrastructure, Cloudflare bypass, browser automation, and scaling scrapers.

Our second with WebClaw explored AI agents, hidden APIs, open source scraping, and LLMs.

Our third with CloakBrowser went deep on stealth Chromium, fingerprinting, anti-bot detection, and browser automation.

Our latest with Browser Use brought insights on browser agents, AI-powered scraping, proxies, evaluations, and browser infrastructure.

We're excited to keep the conversation going.

If you're building web scrapers, data pipelines, browser automation, account management systems, or proxy infrastructure, or you're simply trying to figure out why one proxy provider works better than another, this should be a good one.

Drop your questions below, and Stan Sadokov from NodeMaven and I will start answering them during the AMA.

Looking forward to seeing everyone there!

Ian

I'm looking for a web scraping API for a SaaS that will primarily scrape Amazon, along with some Walmart data and a few smaller e-commerce websites. We'll probably add more retailers over time, so I need something that works reliably across a broad range of domains.

I have been comparing providers like Bright Data, Zyte, ScrapingBee, ScraperAPI, and Scrape.do, but the pricing is difficult to compare.

Its not just that the prices shown on their pricing pages are different. The effective cost also seems to vary significantly depending on the website being scraped. Some APIs use more credits for particular domains, premium proxies, JavaScript rendering, etc.

Ideally, I want the cheapest API that still performs reliably across all our target websites. I don't want to save money on Amazon only to discover that the same provider is expensive or unreliable on Walmart and the smaller sites.

Is there one web scraping API that offers consistently good price-to-performance across different e-commerce domains, or do you generally need to use multiple providers?

I’m designing a system where many users can create their own independent search agents.
Each agent has its own search criteria and runs periodically to retrieve fresh results.
The challenge is that every user can have multiple agents, and each agent can perform multiple different searches. As the number of users grows, the number of individual searches grows very quickly.
I need the searches to remain independent and reasonably real-time, while also preventing the system from becoming overloaded as the number of users and agents increases.
How would you architect the scheduling and execution layer for this kind of system so that it can scale to a very large number of users and recurring searches?
I’m particularly interested in how you would approach scheduling, concurrency, backpressure, and prioritization without letting a traditional queue grow indefinitely.

Trying to get a read on where people actually draw the build-vs-buy line in 2026, because it feels like it's moved.

for context, the tradeoff as I see it: rolling your own gives you control and lower per-request cost, but you eat all the maintenance when targets change, anti-bot shifts, proxies get flagged. paying for a scraping API or managed service hands off the headache but the cost scales with volume and you're stuck with whatever they support.

what I'm curious about from people running this at real scale:

where's your actual cutover? like, is it volume, is it how hostile the target is, is it just not having the headcount to maintain scrapers?

for the stuff you still build yourself, what makes it worth keeping in-house?

and for anyone who went the other way and moved off managed APIs back to building, what pushed you back?

Mostly wondering if the "just build it" default has shifted now that the managed options got better, or if at scale it still always comes back to owning your own stack.

I'm building a workflow that pulls public data from several social platforms. How do you differentiate a focused API against a general scraping platform for this?

I want to avoid a recurring monthly subscription, a separate scraper for every platform while keeping the costs predictable when usage changes.

When you need public data from TikTok, Instagram, YouTube, or other social platforms, how do you decide between platform-specific APIs and a general web-scraping tool ?

Right now I’m thinking about integration , reliability, platform coverage, customization, and whether I might need to scrape regular websites later.

Figured it'd be useful to share some basics for anyone just getting into proxies. Starting with the simplest one, finding your IP.

Public IP is easy. Search "what is my IP" on Google and it shows up right at the top.

Local IP depends on the device.

• Windows: run `ipconfig` in Command Prompt
• macOS: System Settings > Network > your active connection
• iPhone: Settings > Wi-Fi > tap the info icon next to your network
• Android: Wi-Fi settings > gear icon next to the connected network

The quick difference between the two is that your public IP is your network as the internet sees it; the local IP is one device inside it. You'll run into both when setting up a printer, remote access, or troubleshooting a connection.

Hope that helps!

Preferably one that has never been used.

But don't want to pay 100s of dollars.

I think Evomi has one but not sure it compares to other offers out there.

Who has one and is happy with it?

Right now i am mkaing a vinted tool scraping displaying and more, thats not the point but how do i learn the stuff like maby proxies, idk what type of scraping if its for multiple people from a server and the other stuff i need pls help me !

I've read some articles on the legality of web scraping but most are pretty out of date at this stage. So I'm interested to hear if there are any changes on the question is web scraping legal or not.

Appreciate any input and insights. Preferably from experts not just keyboard warriors.

The tricky thing with screen scraping is that most people reach for it when they don't need it.

The difference

Web scraping reads a page's HTML or an API. The data arrives already structured, tags and values you can grab directly. Screen scraping reads what's rendered on screen, so the data arrives as pixels and you need OCR to turn it back into text.

https://preview.redd.it/ovo6pbl07zih1.png?width=903&format=png&auto=webp&s=9fe453509e0405de1b4051afc1392b7d5a4458fe

Why the extra step matters

More stages means more places for things to break, and OCR errors are sneaky. In a quick test with Tesseract, clean text read at 100% accuracy, but a slightly low-res capture hit 96% and still flipped a price from 1299.00 to 2299.00. A blurry one turned a 4.6 rating into 46. The headline accuracy looks fine while single digits quietly corrupt your dataset.

When to actually use it

If the data exists in the HTML or an API, parse that. Screen scraping is for when it genuinely only exists visually, like prices rendered as images, embedded charts, scanned PDFs, or legacy terminal systems with no API at all.

If you do go the OCR route

Capture tight regions instead of full pages. If the number you need sits in one panel, screenshot that panel. Less noise means cleaner OCR output, and small layout changes elsewhere won't break your job.

https://preview.redd.it/m7zo8tj17zih1.png?width=970&format=png&auto=webp&s=08c196ae71b80971a0fc1368691f3b9ec11c6337

Validate output against expected formats, especially numbers. Don't trust raw OCR text for anything that ends up in a dataset.

For JS-heavy pages, render in a headless browser first, then capture. A plain HTTP request to a single-page app returns an empty shell.

One last thing

Budget for maintenance. A code-level parser breaks when the markup changes, a screen scraper breaks when the layout changes, and layouts change more often.

Curious if anyone here has run OCR pipelines at scale, and how you handle validation for numeric fields.

I have been checking on managed scraping APIs for a small project, but there's obviously a gap between wanting a reliable anti-bot bypass and getting handed a full enterprise data platform with a procurement process attached.

The project is a job board and some e-commerce, all behind Cloudflare. Maybe a few hundred thousand pages a month. What I care about is how fast I can get the first successful request out, what a request costs after retries and blocks, and whether I can scale up without a sales call or KYC.

Bright Data is the obvious heavyweight, and I'm not arguing with that. If I needed SERP at scale or SOC 2, I'd pay them and move on. But for job boards behind Cloudflare, it feels like a little too much.

So I've been poking at the self-service tiers of a few tools, like Scrapfly, ScraperAPI, and Zenrows. They all skip the sales-call part, but the billing models are more different than I expected, especially around whether failed requests count against you.

Anyone running a small workload somewhat like this? If yes, what are you using, and how has that been working out for you?

Hello everyone I am working on an utility tool (tcg scanner like collectr and price charting) and I need to fetch daily prices of 8000 cards on eBay with with different variations such as Raw , PSA 8 9 10 .. which makes it 40000 request , did anyone work on something like this or is there any services that gets data from eBay with this amount ?

I run freehire, an open-source job aggregator that ingests postings from dozens of ATS platforms (Greenhouse, Lever, Ashby, Workday, etc.), normalizes them into one schema, and runs each through a facet pipeline.

Just exported the whole catalogue to Hugging Face: freehire-jobs — 6,041,471 postings.

Each row is a raw posting (title, company, description, URL, source, posted_at) plus derived facets:

- Dictionary-only (deterministic, no LLM): skills, seniority, category, work_mode, posting_language, employment_type, education_level, english_level, experience_years_min

- Dictionary-first, LLM-filled: countries, regions, cities

- LLM-only: salary_min/max, currency, period — pay is stated in every format imaginable, no dictionary handles that

Company rows carry their own facets too (industries, HQ country, size, YC batch/stage where applicable).

Format: 20 gzip JSONL shards, split by row range for easy streaming/resuming.

Backstory: I originally wanted to train a cheap classifier for seniority/category tagging instead of paying for an LLM call per job. Turned out the dictionary+LLM pipeline already in prod beats a from-scratch classifier by enough that finishing the classifier wasn't worth it — so I'm sharing the data instead.

Since May and the old reddit almost gone, what are currently the best libs on reddit?
What is your favorite?
I want to scrape all the posts of my top favorite 100 (small) subreddits and sort them by AI to delete AI slop + self promotion and really only get the interesting posts for me to read.

What do you suggest?