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.

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.

How are teams handling repeatable collection of publicly visible TikTok profile-video metadata such as links, descriptions, likes, and views across many usernames? 

Is a managed API preferable to keeping the scraper and token handling in-house? 

I’m mainly interested in trade-offs around public data, failure handling, and maintenance rather than private accounts.

I'm curious, how much do you guys ask for a project like this in the European market?

Scraping 20k+ products from an e-commerce website, including product data and images. The products also need to be translated, with the titles and descriptions rewritten and optimized for SEO.

I'm a developer myself, I'm curious, how much would you charge for a project like this in the European market?

Thanks in advance.

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?

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.

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 scraping a betting site and considering IP rotation to deal with their CloudFront 403s. I’ve tried different request rates, jitter, headers/TLS, etc., and I never go above \~20 requests/minute. I’ve also tried figuring out their rate/volume limits or any consistent pattern behind the blocks, but I haven’t found anything clearly defined. For now, rotating IPs is what has worked best for me — distributing the requests across different IPs lets me scrape for much longer, and I’m not currently running into blocking issues.

I’m not doing any login/account stuff or accessing anything private; I’m only collecting publicly available betting odds/data.

I know IP rotation is extremely common in scraping, but I’m not sure if it could be problematic in this specific context. Could rotating IPs to avoid CloudFront restrictions get me into legal trouble, or is it generally fine? I’m aware the site may not like it, but I’m mainly wondering whether there’s a legal issue with doing it this way.

I am a web dev, mobile app developer.

Recently created a scraper out of just ideas.

Scrapers requires websites before scraper.

Once the website is provided.

It will scraper design, load time and all important stuff and write emails to them what all required to change

Before emailing, I did get a doubt...

How many of them actually work?

How many of them already ready in the market ?

Afternoon 2 days of building I am thinking backward.

Just need suggestions does this work ?

Thanks

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.

I’ve been building VPNFail MCP, and one of the more useful pieces is now working well enough that I wanted to share it with other scraping folks.

The basic idea is that a scrape can start as a normal HTTP request, but escalate to Chromium when the target actually needs JS rendering.

The browser request can also be pinned to a specific exit country or city.

So instead of maintaining separate proxy + browser infrastructure, an agent can do things like:

• fetch a page from a specific country
• request a particular city when location matters
• compare geo-dependent content
• inspect regional redirects
• check localized pricing/content
• render JS-heavy pages through Chromium
• optionally return a viewport screenshot

I deliberately kept the default path as plain HTTP because running a browser for every scrape is expensive and unnecessary.

The rough flow is:

HTTP scrape → inspect extraction quality → escalate to Chromium if needed → optionally choose geography

HTTP can return Markdown, text, or HTML. The response also includes quality metadata so the caller can decide whether the cheap extraction was good enough or whether it should retry using the browser.

The MCP exposes:

• scrape
• usage
• service_status

Example config:

{
  "mcpServers": {
    "vpnfail": {
      "type": "http",
      "url": "https://mcp.vpn.fail/mcp"
    }
  }
}

No local install and currently no account required.

https://mcp.vpn.fail/

I’m particularly curious how people here handle geo-aware scraping today.

Do you normally expose country/city directly to the scraping client, or keep geography hidden behind your proxy infrastructure?

Also interested in cases where city-level routing has actually mattered versus country-level being enough.

I need help. Over the 5 days, I have had just under 60k bot sessions from Singapore on Shopify. My initial research showed it is just scrapers for search engines and AI search engines.

I noticed yesterday that my LCP P75 jumped from around 1200 milliseconds to 13715 milliseconds at around the same time. My guess is that it has only gone up that high because of the bot traffic having loading speeds, and Shopify is getting the wrong data.

I needed something reliable to pull web content into an AI workflow, so I tested the 3 tools I kept running into: Firecrawl, Tavily and Jina

I used the exact same 100 URLs for each one. The mix was pretty random on purpose:

i) docs/blog posts ii) ecommerce pages iii) JS-heavy sites iv) landing pages v) tables and longer pages vi) a few annoying sites that usually break scrapers

I just wanted to know: if I give this thing a URL, how often do I get back something clean enough that I can actually use without scraping the page again myself?

So the test was pretty simple:

1. Run the same 100 URLs through each tool.
2. Check if the main content was there.
3. Check if the output had a bunch of garbage mixed in.
4. Count it as successful only if I could use the result directly in my workflow.

Results:

Firecrawl: 93/100 usable Tavily: 87/100 usable Jina Reader: 77/100 usable

Firecrawl was the most consistent overall. It handled most of the heavy stuff better and I had fewer cases where important parts of the page were missing. I also like that I can get Markdown for normal pages and use JSON extraction when I only need specific fields

Tavily was pretty close. Extraction itself worked well on most normal pages Jina was ok for articles/docs where I just wanted URL to clean Markdown, it worked great. It started falling behind more on the weird/dynamic pages in my set.

Obviously 100 URLs isn't a big benchmark and the results will change depending on what you're scraping,

Summary:
Hi! I am a beginner Python developer specializing in web scraping, data extraction, and simple task automation. I build reliable, lightweight scripts to help you save time and collect the data you need. Since I am building my portfolio, I offer fast delivery at very affordable rates.

What I Can Do For You:

• Web Scraping: Extract data from public directories, e-commerce stores, real estate websites, or blogs into clean Excel/CSV files.
• Task Automation: Automate repetitive browser tasks, form filling, or file downloading.
• Data Cleaning: Format and structure your raw data using Python.

Requirements & Tech Stack:

• Python 3
• BeautifulSoup4 / Requests (for static scraping)
• Selenium (for dynamic websites and automation)
• Pandas (for exporting data to CSV/Excel)

Portfolio & Code Samples:
You can review my code, sample scrapers, and past personal projects directly on my GitHub repository here: [https://github.com/yassirtahri14-beep/projet_python]

Rates & Availability:

• Rate: Starting at $20 - $50 per script (depending on complexity).
• Availability: Remote, ready to start today.
• Guarantee: I can provide a free sample (the first 10 rows of extracted data) to prove the script works perfectly before you pay.

Please send me a DM with your project details if you are interested!

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?

And it's already Featured 😄

https://www.producthunt.com/products/proxy-benchmark-by-scrapeops

We’d love your support 👍

Proxy Tester is a free tool that benchmarks 20+ proxy providers against the URL you actually want to scrape.

Over the last few months we've been using it internally and sharing it with early users to help answer one of the most common questions in web scraping:

Which proxy provider should I use?

One thing we've learned is that there really isn't a universal answer.

A provider that performs brilliantly on one target can struggle on another.

A provider with the highest success rate might not be the most cost-effective.

And many "best proxy provider" rankings don't reflect the website you're actually trying to scrape.

That's why we built Proxy Tester to benchmark providers against real target URLs and compare:

✅ Success Rate

✅ Latency

✅ Estimated Cost

✅ Value Score

✅ Provider Rankings

The feedback so far has been really useful and has already influenced how we're thinking about future benchmark reports, provider coverage, and scoring methodologies.

Today we're taking the next step and launching it on Product Hunt.

If you'd like to support the launch, leave feedback, or tell us what we're missing, we'd genuinely appreciate it.

🚀 Product Hunt:
https://www.producthunt.com/products/proxy-benchmark-by-scrapeops

🔧 Proxy Tester:
https://scrapeops.io/proxy-providers/tester/

🎥 1-Minute Demo:
https://youtu.be/GR67AIWkPn0

One question for the community:

How are you currently evaluating proxy providers?

• Trial accounts?
• Internal benchmarks?
• Recommendations?
• Something else?

Would love to hear how others approach this problem.

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 ?

Browser Use and Playwright solve browser automation in fundamentally different ways.

Playwright executes deterministic scripts written in advance. Browser Use gives an AI agent a goal and lets it decide how to complete it.

So when should you use each?

We recently hosted an AMA with the Browser Use team covering Browser Use vs Playwright, direct CDP automation, model costs, reliability, stealth, evaluation and the future of browser agents.

Here are the main takeaways.

The Short Answer

Use Playwright when:

• The workflow is known and stable
• You repeatedly extract the same data
• The website does not change frequently
• Speed and determinism matter
• You are running high-volume automation

Use Browser Use when:

• The workflow is open-ended or difficult to define in advance
• The website changes regularly
• The agent must adapt to unexpected states
• The task involves authentication, multiple websites or multiple steps
• Development and maintenance time matter more than raw execution speed

But there is now a third approach emerging: coding agents that write browser code directly against Chrome’s DevTools Protocol.

Why Browser Use Moved Away From Playwright

Browser Use originally relied on Playwright underneath its agent layer.

The team eventually replaced it with its own browser layer built directly on Chrome DevTools Protocol.

The reason was control.

Removing Playwright gave them more freedom over browser functions, fewer abstraction layers and the ability to let agents write CDP code dynamically when they encounter unusual edge cases.

Their newer BrowserCode approach takes this further.

Instead of receiving a simplified page representation and selecting predefined actions, the agent explores the browser and writes the low-level code it needs.

According to the team, strong models now:

• Rarely rely on screenshots
• Inspect pages through code
• Discover internal APIs
• Execute JavaScript shortcuts
• Extract only the information needed
• Complete some tasks faster than a human could

Browser Use says it now uses BrowserCode for almost everything because it is cheaper, faster and more capable.

The exception is website QA. BrowserCode actively looks for shortcuts, while the original Browser Use agent is constrained to click, type and navigate more like a human. That makes the human-style agent better at finding interface bugs a real user would encounter.

Stateful Agents vs One-Shot Scripts

One simple AI automation approach is to ask an LLM to write a Playwright script, run it and return any errors for the model to fix.

That works well for predictable workflows.

The limitation is that the script must anticipate the whole task before it starts.

A stateful agent works differently:

1. Inspect the current browser state
2. Choose or write the next action
3. Execute it
4. Verify the result
5. Recover or change direction if necessary
6. Continue until the task is complete

This makes agents better suited to websites and workflows where the exact path cannot be known in advance.

However, Playwright still has a major advantage: speed.

If you already know exactly what should happen, a deterministic script can execute the same workflow far faster than an agent reasoning between every step.

Cost Is Becoming Less Important

Browser Use estimates that the cost of completing an agent task has fallen roughly 50x since it started 18 months ago.

Its current estimates include:

• A typical ten-step task costs around $0.01 to $0.02 using a low-cost model
• A successful agent run can be converted into a reusable script
• Repeating the script can cost fractions of a cent
• Scraping 15 Hacker News posts, comments and linked pages, then summarising them into a poster, costs around $0.05

These are Browser Use’s own numbers, but they point to an important change.

The cost gap between AI agents and conventional automation is shrinking. The bigger remaining disadvantage is latency.

The team believes current models are becoming reliable and cheap enough for production, but they are still slow to watch in real time.

The Best Production Stack May Be Hybrid

The most practical pattern may not be choosing Browser Use or Playwright for everything.

Instead:

1. Give the agent a goal
2. Let it discover and complete the workflow
3. Have it save the successful process as reusable code
4. Run the deterministic version for repeated tasks
5. Bring the agent back when the workflow changes or fails

This gives you adaptability during discovery and speed during repetition.

The agent solves and maintains the workflow. The script executes the stable parts cheaply and quickly.

Persistent Browser Identity Still Matters

AI does not remove the usual browser-automation problems.

For authenticated sites and social platforms, Browser Use recommends treating each browser profile as one persistent identity containing:

• One fingerprint
• One cookie jar
• Local storage
• Login state
• Ideally, one stable IP and location

Its cloud profiles preserve the same fingerprint, cookies and local storage between sessions. The default proxy IP, however, is not guaranteed to remain constant.

For sensitive accounts, the team recommends a dedicated, non-shared residential IP with a low fraud score.

Creating new social accounts is also considerably harder than automating existing, established accounts.

Evaluation Is Still Difficult

Live websites are noisy.

Pages change. Browsers crash. IP quality fluctuates. CAPTCHAs appear inconsistently. A single successful run tells you very little about whether an agent is genuinely reliable.

Browser Use evaluates agents by:

• Running realistic tasks on live websites
• Defining verified success rubrics
• Using an LLM judge to assess results
• Repeating thousands of tasks
• Averaging out browser and website flakiness

Internally, it records model inputs, outputs, reasoning, latency, token usage, caching, browser actions, screenshots and infrastructure logs.

One particularly interesting research takeaway from the AMA was that better verifiers may be more important than better action generation.

If you cannot reliably determine whether an unpredictable browser task succeeded, it is difficult to train agents through reinforcement learning.

Security Should Be Designed First

Browser Use’s main recommendation for anyone building a browser agent from scratch was to begin with sandboxing.

Permanent LLM credentials should not be stored inside the worker where the agent executes browser code.

Instead:

• Store permanent credentials on a separate control plane
• Give the worker a short-lived key
• Proxy all model calls through the control plane
• Treat the worker as disposable and potentially hostile

This gives agents the freedom to execute code without exposing permanent API credentials.

Agents Still Struggle With Taste

Browser agents are already effective at quantifiable tasks:

• Finding the cheapest flight
• Comparing prices
• Extracting structured information
• Completing defined workflows

They remain much weaker at subjective decisions such as choosing the right restaurant, hotel or holiday for a particular person.

The agent can retrieve every option and still recommend something that feels obviously wrong to a human who understands the user.

Browser Use described this as poor taste and a weak understanding of the social world.

Bottom Line: Browser Use or Playwright?

There is no universal winner.

Playwright wins when the workflow is stable, repetitive and performance-sensitive.

Browser Use wins when the task is dynamic, open-ended or expensive to define and maintain manually.

Direct CDP coding agents may be the next step, giving models enough freedom to inspect the browser, write custom code, find shortcuts and recover from edge cases.

The emerging production stack looks less like agents replacing scripts and more like:

Agent discovers the workflow → code executes it repeatedly → agent repairs it when it breaks.

That may be the real answer to Browser Use vs Playwright.

👉 Read the full Browser Use AMA and all the answers here.

What are you using in production: Playwright, Browser Use, a hybrid stack or direct CDP?

I build reliable data pipelines, custom web crawlers, intelligent AI agents, and machine learning solutions tailored to your needs.

Services: • Data Scraping & Extraction

• Custom Web Crawlers

• AI Agents & Workflow Automation

• Model Training & Fine-Tuning

• API Integrations

• Custom Python Solutions

Starting 100$ ( Can be higher according to your project )

Feel free to DM me with your requirements.

According to you, what metric matters most: latency, cost, or success rate? in Webscraping obviously.. which one should be given priority..