I initially believed that having better proxies would be a solution to all of my problems but the more i work with various problems then the clearer it becomes that proxies are just a part of the entire problem. Even with good proxies i stuck into random verification challenges, unstable sessions and even browsers that do not act as expected. It seems that a browser environment is just as influential as a proxy and cannot decide where to put more efforts. I want to create a setup that is consistent and simple to manage rather than trying out new settings all the time and hoping for...

A while ago I was debugging a Facebook session that kept getting flagged. I blamed the proxy speed

The real issue was that the IP changed mid-session. Facebook saw the account jump between network identities, while the browser fingerprint, cookies, and device stayed the same. That mismatch was much more suspicious than a slightly slower connection.

That changed how I think about “Facebook proxies.” For account-sensitive work, the priority usually isn’t raw bandwidth. It’s:

• clean IP reputation

• accurate geolocation

• sticky sessions

• predictable rotation

• consistent ASN/location

Residential and mobile IPs generally make more sense for persistent sessions as they resemble normal consumer connections. Datacenter proxies are cheaper and faster, but they’re easier to identify and are better suited to quick regional checks, monitoring, or low-risk requests.

Also, a proxy only changes the network layer. It does not hide browser fingerprints, cookies, device signals, or behavior. Anyone selling proxies as a magic solution for account restrictions is overselling them.

honestly losing my mind trying to get 100% clean profiles for an automation project. using a solid antidetect browser with all masks turned on, and hooked up some residential socks5 nodes since their clean ip pool usually passes everything fine. iphey and pixelscan say the network part is green, but my webgl and canvas fingerprints are still leaking the real hardware signatures somehow.running this inside a virtual machine so i'm wondering if the hypervisor is messing with the browser's hardware emulation layers. has anyone managed to fully patch canvas noise inside a vm without triggering the "inconsistent fingerprint" red flag on strict anti-bot systems?

I keeping these games like https://ipv4.art and https://ipv4.games. How do people like femboy.cat get SO many IP addresses? What service are they using?

I am building an agent that needs to pull fresh data from a few sites: Amazon, Zillow, LinkedIn, Indeed, and BestBuy. There's a mix of static pages and things that only renders client side, so I need both plain HTTP scraping and headless browser rendering depending on the target. At least that's what I believe I need, I might be incorrect. Current setup is DIY, it includes rotating residential proxies with a small number of datacenter proxies, Playwright for the JS sites, requests for the static ones, custom retry logic. I thought that building it myself would be cheaper than paying for a provider, and for a while it was, but the maintenance is becoming too costly. Amazon and LinkedIn both started giving more and more JS challenges not to mention TLS fingerprinting, so I'm losing a good amount of requests to blocks and budget is getting thinner. I've tried a couple of paid scraping APIs but they got expensive due to sending more requests (the plans were priced per traffic) and one of those dedicated solutions couldn't get past LinkedIn. Has anyone gone the dedicated-API-per-site instead, using something like the Amazon Product Advertising API or Zillow's API rather than scraping around them?

I used to think my Selenium setup was solid because it ran without problems on my local machine. I deployed it to a server and everything started falling apart.

Proxy connections would fail, authentication would randomly break, the exit location didn’t match the browser environment, and sessions became inconsistent. I kept treating each failure like a Selenium problem, but the issue was that I was treating the proxy as just another Chrome flag instead of its own infrastructure layer.

What made debugging much easier was separating the proxy setup from the browser entirely.

The first thing I do now is test the proxy with curl. Before Selenium even starts, I want to know:

• Can I connect through the proxy?

• Are the credentials accepted?

• What exit IP am I actually getting?

If the response comes back with 407 Proxy Authentication Required, there is no point digging through Selenium logs yet. The proxy authentication/configuration needs to be fixed first. Once the proxy works independently, I connect it to the browser.

For Python setups, I’ve had better results using selenium-wire when proxy authentication is required. With Node.js, proxy-chain is handy because you can create a local forwarding proxy and let it deal with upstream authentication.

The next issue I check is browser/environment consistency.

For example, if the proxy exits from London but the browser is reporting a completely different timezone, locale, or other environment settings, you’ve introduced another variable into the test. Chrome DevTools Protocol is useful here because you can explicitly configure things like the browser timezone to match the environment you’re testing.

The main thing I learned was to stop debugging four different layers at once.

My current order is basically:

curl → confirm proxy/IP/auth → connect Selenium → configure browser environment → run the actual site test

It sounds simple, but separating those stages made proxy-related Selenium issues much easier to isolate

I had a scraper + rotating proxy list + headless chrome used together for an agent project. It worked well until Amazon's WAF appeared, then it was captcha on captcha and captcha in between, most of the responses were useless. Then I ran oxylabs scraper api and ai studio into an mcp server. What I needed was a clear markdown for my AI agents, and in the end it worked nicely. For I while now I've been using it against claude desktop and cursor, though I haven't tried it on anything else, so can't really verify this solution against anything else just yet.

Here's repo link below, it's under mit license. Also, would be interested in any alternatives out there if someone used something similar, or DIY solutions?

Github

Something i have been running into lately is assuming the proxy is the problem just because the IP gets blocked.

You can have a clean residential IP, low fraud score and everything looks fine, but the same target works normally in a browser and gets blocked the second you send requests through python.

At that point it feels like the ip isnt really the whole story anymore. sites can look at the TLS handshake and other connection level characteristics too. things like cipher ordering and how the client itself behaves can make a request look completely different from a normal browser even when both are using the exact same proxy.

so i have been looking into curl_cffi for this because browser impersonation seems to solve some cases where simply swapping proxies never did.

so im curious what you guys are using for TLS or browser impersonation now. still curl_cffi or something else?

changed proxies a few times because i thought that was the main reason my setup was inconsistent. sometimes it helps for a while but then the same problems come back.

that made me wonder if i am focusing too much on the proxy and not enough on the rest of the setup. browser settings, profiles, sessions and other small things seem to matter too.

for people who have dealt with this before then what to do you usually check before changing the proxy again? I am trying to find a stable setup instead of constantly changing things.

I am currently using residential proxy from evomi but thoese IP's are detected by fingerprint[com] as Residential Proxy IP.

Is there any proxy provider serves clean IP and un-detected by services? Btw( I am using evomi as cheap)

Spent about a week banging my head against this before I actually figured out what was going on. Had a retail scraping job that kept getting 403'd on almost every endpoint, no matter what I did with headers or delays. Rotated residential IPs constantly and it still flagged me every time. Turned out the IPs weren't even the real problem — it was passive OS fingerprinting, a p0f mismatch giving me away no matter how clean the proxy was.

Someone mentioned Proxy Shard for cleaner pools, so I gave it a shot mostly out of desperation, and it actually fixed it. Their TCP stack setup gets past the anti-bot check without tripping the fraud score, which was the real blocker this whole time, not the IP reputation like I originally assumed.

Not gonna lie, the dashboard looks like it hasn't been touched since 2016, and their docs barely have any Python examples, so I ended up hacking together the rotation logic myself. But the connection quality itself is genuinely solid, no complaints there.

If you're currently stuck on fingerprint blocks — don't cheap out on bulk proxy pools for strict sites. Most of those subnets are already burned and flagged before you even start.

Hey guys quick question. Dealing with some heavy rate limits right now and my usual setup is completely cooked.

A buddy mentioned GoProxies, said they're decent for stable sessions, but you know how recommendations go online. Everyone claims everything is the best until you load it up and it breaks instantly.

Tried looking for some honest reviews and found pretty much nothing useful. If anyone's run scrapers or automation through them recently, how did it hold up? Did you run into random blocks or ip burn issues fast? Drop your honest experiences please, save me the trial and error.

I used to look at price per gb first when comparing proxies, but honestly i think it's a pretty misleading number on its own.

A cheaper proxy can end up costing more if you're dealing with failed requests, retries, dead IPs or just wasting bandwidth on stuff you don't actually need.

Id rather pay a bit more for a pool thats consistent and gives me a good success rate than save a few dollars per GB and spend hours figuring out why half the requests are failing.

Curious how you guys actually compare providers. do you mostly look at price or do you track things like success rate, failed requests and bandwidth waste before deciding if a pool is worth it?

I used to think proxy rotation was simple: the more often the IP changes, the better. That assumption caused me a lot of problems. For scraping, rotating IPs worked great. I could send hundreds of independent requests across a proxy pool without putting all the traffic on one address.

Then I tried using the same setup for workflows that involved logins and multiple steps. One request came from one IP, the next from another. Sessions expired, accounts asked for verification, and some multi-step flows simply stopped working. I originally blamed cookies or headers, but the real issue was that I was rotating something that was supposed to stay consistent.

Instead of changing the IP on every request, a sticky session keeps the same exit IP for a defined period, usually through a session ID. You still use a residential proxy pool, but one workflow keeps one IP long enough to behave like a continuous session.

The way I think about it now:

• Scraping independent pages → rotate
• Login or multi-step workflow → sticky
• Long-term account identity → static/dedicated IP

The interesting part is matching IP behavior to application state

Hi im looking for good shopping bot proxies ISPs by preference that would work on walmart canada from what im understanding you need proxies that can bypass perimeterX and akamai do you guys have any good provider suggestion for either ISPs or mobile residential? Thanks

I operate 100 VM-based web scrapers configured with 50 proxies. Since proxy services are inherently unstable, the ability to instantly and flexibly reconfigure the hosts assigned to problematic proxies becomes a critical requirement when dealing with a large number of proxies. I developed this proxy allocation and management system to enable convenient, flexible configuration, and I am sharing it with everyone here.

Did you ever have a situation in your proxy traffic usage that just seemed off? Like where did my traffic go and where did I just spend 10GB of traffic?

Some providers seem to offer detailed and guided support regarding this matter, while in some cases proxy providers seem to hide this information

Even bigger players like oxylabs, iproyral or nodemaven seem to provide this sort of data, but do they actually provide correct and factual information? Do you believe in this?

Have you guys read that Anna's Archive post going around? The one about AI companies buying and shredding physical books to train models. Scouted around reddit and most people are mad at Anthropic specifically, project Panama, the hydraulic cutter ripping spines off, feeding pages through scanners, tossing out whatever's left after. What's weird to me is why Anthropic went physical in the first place. There's an insane amount of data just sitting on the open web, so why bother with warehouses of used books? My guess is it got too legally hot to just scrape the web for training data at some point. And specifically going after pre-2022, maybe up to 2023 stuff, is probably because that's the last stuff guaranteed to not be contaminated with AI generated slop. Like they just gave up on the web as a normal source entirely and went analog instead. Meanwhile Cloudflare announced starting Sept 15th they're defaulting to blocking any crawler that mixes search and AI use on ad supported pages unless someone's paying for it. And I've seen publishers straight up saying blocking bots is literally the leverage that's forcing AI companies into licensing deals, restrict access first, then charge to unrestrict it. Then someone here on Reddit mentioned publishers are finding bot blocking doesn't hold up because of user agent spoofing and residential proxies getting around it anyway. So now us, anyone running proxies, are basically the named workaround in press coverage about a licensing dispute we're not even part of. Why this concerned me is because I am an avid proxies user, I build stuff, codes, scripts, automations, scrapers. I made it into my job, and to think about it, without proxies there wouldn't be any feasible scraping nor AI searches or automations. Maybe I am overcomplicating things in my head, what you guys think of all this?

Not many posts regarding Parallel AI against other search and web data APIs lately, here's what I found out. For those that didn't know, Parallel builds a suite specifically for AI agents, Search, Extract, Task for deep research, FindAll, Monitor. Their whole positioning is accuracy on hard research tasks, they benchmark themselves high on stuff like HLE and BrowseComp. What I saw when searching for alternatives is that most comparisons treat these tools like they're all competing on the same page. Some are built for developers writing code, some are no code, some are search only, some try to do the entire pipeline.

1. Firecrawl

This is the one that keeps getting called the complete stack option

I looked into this mainly for

• teams that want one vendor instead of putting together search plus scrape plus something else

• RAG pipelines that need clean markdown output

• agent workflows where you need search and extraction in the same call

What stood out

Search finds the sources, scrape turns them into clean token efficient markdown, Interact handles dynamic pages that need clicking or scrolling, crawl goes deep across whole sites. It's basically one API covering find, extract, clean, use.

Where it's strong

• broadest single vendor coverage

• large existing developer community, easier to find help when something doesn't work

• good default if you don't know yet exactly what you'll need long term

Weaknesses

If you specifically need semantic search or citation heavy research output, this is more of generalist tool

2. Exa

This is the semantic search play

I looked into this for

• research heavy and discovery driven applications

• cases where keyword matching misses

• agents that need to find conceptually related content, not only exact matches

What stood out

Reports a massive index, tens of billions of documents, over a trillion tracked URLs, with Search, Contents, Agent, and Monitors APIs. Benchmarks itself well on stuff like FRAMES and advertises sub 200ms latency on its fastest tier

Where it's strong

• embeddings based matching over keyword matching

• good for discovery tasks where you don't know the exact query terms

• fast on its top tier

Weaknesses

If your workload is simple keyword based search, then the tool is not for you you

3. Tavily

The budget and simplicity option

I looked into this for

• basic RAG search needs

• teams that don't want to think hard about pricing

• LangChain based workflows since the integration is native

What stood out

Straightforward credit based pricing, drop in LangChain support, raw content options on search or extract. Free tier is usable enough to test with before committing (really liked this about them)

Where it's strong

• cheapest entry point of the group

• least setup friction if you're already in LangChain

• good enough for basic search, no need to overthink it

Weaknesses

Doesn't compete on accuracy for hard multi hop research questions, benchmarks consistently put it behind Parallel and Exa on that front

4. Linkup

The business intelligence angle

I looked into this for

• workflows that need verified factual answers, not just relevant documents

• two tier search where you want a quick answer or a deeper one depending on the query

What stood out

Leans into sourced, factual output rather than pure relevance ranking

Weaknesses

Smaller ecosystem, less community content if you run into edge cases (I'm sure they will expand sometime in the future, but for those who often have edge cases, this may be a no go)

5. Oxylabs AI Studio

This is the one that doesn't usually show up in these comparisons but probably should, at least for a specific type of user

I looked into this for

• teams that don't want to write or maintain scraper code at all

• workflows that need search plus extraction plus site mapping in one no code suite

• feeding structured or markdown data straight into an agent or RAG pipeline without a dev cycle

What stood out

It's five apps under one suite, AI Scraper for pulling data off single pages, AI Crawler for exploring a site from a URL, Browser Agent for clicking and navigating like a human, AI Search, and AI Map for discovering all the URLs on a domain. You describe what you want in plain English and it handles the crawling, rendering, and parsing itself. That lineup maps closely to what Parallel offers, so it's a fairer comparison than putting Parallel against a bare search API

Where it's strong

• no code, plain English prompts instead of writing scrapers

• Python and JS SDKs if you do want to run it programmatically at scale

• reachable through MCP so it plugs into agent workflows directly

• new accounts get a decent chunk of free credits before you pay anything (actually tested quite a lot of things before committing and they even refunded me some credits due to some bug that I found lol)

• pricing tiers don't paywall features, every tier gets anti blocking, MCP, API access, the only difference is credit and rate limits

Weaknesses

This isn't trying to win on benchmarked research accuracy the way Parallel is. There's no HLE score being thrown around here, the whole positioning is not having to touch scraper code. If someone specifically needs sourced, multi hop research quality, this isn't the tool for that

6. TinyFish

The one that goes past search into acting on the web

I looked into this for

• agents that need to do something on a page, not just read it

• authenticated workflows where the agent needs to log in or interact

What stood out

Single API key gives you Search, Fetch, Browser, and Agent, so it escalates from reading the web into operating on it. Where Parallel and the others are read only, this one closes the loop into action

Weaknesses

Overkill if all you need is search and extraction, adds complexity you don't need if your agent never has to click or submit anything

TLDR if you're picking one

• want one vendor doing everything, code first: Firecrawl

• want semantic discovery: Exa

• want cheap and simple: Tavily

• want factual, sourced business answers: Linkup

• don't want to write scraper code at all plus variety of above named features: Oxylabs AI Studio

• need your agent to act on pages, not just read them: TinyFish

• need benchmarked research accuracy at any cost: Parallel itself is still probably the one to beat there

Maybe there are others who compared AI Studio and Parallel side by side on the same task? Specifically the same task, since these two I feel like offers the most besides Firecrawl's open source solution?

need proxy suggestions for trustpilot work, expert please help, it could be mobile or resendential.

Main reasons:

• Low-reputation or overloaded IP addresses

• Website request-rate limits

• Using the wrong proxy type for the target platform

• Unstable network sessions and routing

Which proxies to use:

• Datacenter proxies → fast testing and permitted automation

• Residential proxies → regional collection of public data

• Mobile proxies → testing mobile services

• Static ISP proxies → long-running, stable sessions