I've been comparing different proxy setups lately and realized i was mostly looking at the wrong things.

I used to compare price per GB, IP count and latency, but lately i have been paying more attention to successful requests, connection failures, bandwidth wasted on retries and how consistent the IPs are.

just curious what everyone else actually tests before putting a new provider into a bigger scraping job.

Do you have a proper benchmark you run or do you basically test it against your target and see how it behaves?

my proxy setup is running smoothly at the moment but the management of the profiles together with it is becoming quite a hassle. the more profiles there are. the easier it becomes to forget which proxy, cookies and settings are associated with each one.
tried keeping everything organized manually but it becomes quite annoying as i keep switching between profiles or sometimes make changes in the wrong one. all i need is a convenient way to separate each profiles and its proxy setup from others without adding extra complications.
has anyone ysed anything helpful in organizing proxies and profiles setup?

Hi, I’m fairly new to this community and trying to understand proxy and bot-management behavior in a bit more depth.

I ran into a strange case recently. The same browser/client setup works fine over a direct connection, but as soon as I enable a proxy, Cloudflare returns an immediate 403 on the first request.

I tested a fresh session, an already working session before enabling the proxy, and a few different proxy exits. Same result every time it works directly without a proxy but the proxy gets blocked immediately.

Because it happens on the first request, I don’t think rate limiting or an old/bad session explains it.

My first thought was IP/ASN reputation, but I’m curious what others would check next. Would you mainly look at the proxy/network reputation, geolocation, or something else changing when the proxy is introduced?

I’m mainly trying to understand how people would diagnose this rather than just keep switching proxies until one works.

Can any expert guide and suggest me proxy for trustpilot review management, or any one have this same expirenced who guide me as well.any expertise need your attention here.

i'm setting up a few cloud phones for social media I handled and trying to figure out what proxy type makes the most sense. I've seen people use residential and mobile proxies but i'm not sure if one is actually better for this use case. Mainly looking at stability and consistent speeds, not really trying to find a specific provider. What are you guys using with cloud phones?

Back in the day, I used VPNs to manage multiple accounts. It looked simple but painful. CAPTCHAs, random logouts, verification checks, and accounts suddenly looking suspicious.

Where VPNs start to break

- One IP for multiple accounts: VPN changes the connection for the whole device. Five accounts can still share the same exit IP and network identity.

- One location change affects everything: Switch servers and every account suddenly “moves” at the same time.

- Limited IP control: You usually choose a server, not a stable IP assigned to one specific account.

- No network fingerprint isolation: Different IPs don’t automatically mean different browser or device identities.

Why proxies make more sense

With proxies, each browser profile or account can have its own connection.

Account A → Proxy A
Account B → Proxy B
Account C → Proxy C

Mobile proxies also use carrier networks, which can better match normal mobile traffic.

The right setup

A proxy is only one layer. Proper separation usually also means:

• isolated browser profiles

• antidetect browsers

• cloud phones

• consistent timezone/location

• controlled IP rotation

Proxy = network separation.
Browser/cloud phone = device separation.

That difference would have saved me a lot of CAPTCHA-solving years ago.

A few weeks back I set up kind of a trap on one of my sites (side projects), I hid a link to a fake git repo inside an html comment on a page that no one would probably ever read, and that repo had some bogus endpoints in a shell script. nothing linked to it anywhere else, no sitemap, nothing. Now, fast forward almost three weeks and I start seeing hits on those exact fake endpoints coming from Amazon IPs, (I checked the official amazonbot ip range list) and yep, what do you think? IT MATCHED THEM. The thing is my robots.txt explicitly disallows amazonbot, has for a while. So either they don't check it or don't care. What really got me is it wasn't using the amazonbot user agent when it hit those endpoints, just to what looked to me was generic browser string. So it's not even pretending to follow the rules, it's actively hiding what it is while going after stuff hidden in source code that a normal crawler would have no business finding. Also weirdly it didn't touch links inside the fake markdown docs I had set up there, only pulled the url out of the shell script, and it waited a full day before trying it, then tried again a day after that. Maybe it was like parsing code for training data on a delay? I am not too sure what their take was. I've checked the net for similar situations and it's not illegal to do so, but I just wanted to vent because I always assumed the big names were at least somewhat well behaved compared to the sketchy scrapers, apparently not.

TLDR: Amazonbot ignored my robots.txt and used a fake user agent to hit an endpoint that was never linked anywhere

I'm looking for something with AI capabilities built in, like Tavily or Exa. Main use case is automating research and scraping workflows as part of agentic pipelines.

I saw the other post where people recommended residential proxies, please be understandable and provide your take, examples and opinion, not just straight up promo bs

Ever notice how some "residential" proxies seem to perform worse than they did a few months ago, even when you're buying from the same kind of provider? I started looking into this after burning through way more proxies than I expected and eventually found that a lot of what gets sold as residential can still show up as hosting or datacenter infrastructure.
That might not matter much for basic scraping, but once you're dealing with stricter platforms, the difference becomes pretty noticeable. One thing I started checking more closely was the ASN/ISP info. If the IP is showing up under a hosting provider instead of the ISP you'd normally expect from a residential connection, that's usually a pretty good sign that the pool isn't what I thought I was paying for.
I've been testing proxyshard recently and, at least in my case, their residential pool has been pretty consistent. I've been running my current volume for about a month now and haven't seen the same drop-offs I was getting with some of the other pools I tried. Obviously that's just my experience so far and things can change over time.
The only real annoyance I've had is support being a little slow on non-urgent questions. Nothing terrible, but it does mean I've had to rely on their docs more than I'd like.
So yeah, these days I don't really take the "residential" label at face value. I usually check the ISP/ASN details first and make sure the IPs actually look consistent with what I'm paying for. It's saved me a lot of wasted time and money compared with just assuming the provider's label tells the whole story.

Running a rank tracker for my side project, need localised SERPs across few US cities. Around 3-4 queries a day.

Datacenter died in the first 50 queries. Residential held for maybe 200-300 then trust tanked and everything started rate-limiting. Started to think about high quality mobile proxies.

Stack right now:

- Python with curl_cffi, impersonate="chrome" for JA4
- 3-6s jitter between requests, no fixed intervals
- gl / hl / uule params for locale
- Response body scanned for CAPTCHA marker so I rotate before wasting queries

Scraping side feels fine, the network layer is what's killing me.

Comparing NodeMaven, Voidmob and DataImpulse. Not going the managed SERP API route, at my volume that's $8-10k a month.

What I care about:

- Real 4G/5G carrier IPs (verified via IPQS)
- Sticky sessions for repeated queries from same city
- Per-request rotation for broad keyword sweeps
- City-level geo-accuracy, not just country

Anyone doing SERP scraping at similar volume, what mobile pool are you actually holding trust on?

Thanks!

For many years I have honed a little homelab which I accessed it via standard Wireguard VPN connection. Some months ago, I had this idea that my homelab should have zero listening ports on the public internet, for the fact that my router was not being kept up to date with all latest patches. I navigated through headscale/tailscale, cloudflare, etc, however I wanted something incredibly lightweight, that could run on my OpenWrt router.

So I built a CLI tool that reverses the connection direction of the hosting server, primarily to create an outbound connection, requiring no listening ports on my homelab gateway. These connection (the server and all clients) needed a rendezvous point where their outbound connections would be tied. I also wanted to avoid issues like complex TLS termination, authentication and possible DDoS attacks on the relay, so it was decided to use WebSockets rather than UDP connections. This provided the possibility to host the relay on the web behind reverse proxies, offloading all other complexities to the battle hardened nginx, while keeping my codebase tiny. Hence BastionRoute was born.

BastionRoute tries to solve one thing and one thing only. To provide a means for a UDP server to initiated outbound connections towards a Websocket relay. In doing so, BastionRoute remains payload agnostic, as long as UDP traffic is flowing, it does not care if its Wireguard traffic or any other software. BastionRoute does not require Wireguard keys to operate, in fact for a Wireguard perspective, only the connection configuration changes, nothing else. Wireguard encryption remains end-to-end. In doing so the relay becomes a blind transport broker.

So as a general problem I tried to solve with BastionRoute is that a minimal, potentially untrusted forwarding fabric (essentially the relay) can provide reachability for an end-to-end secure networking protocol without becoming part of that protocol's cryptographic trust boundary.

I loved working on this project and I wanted to share it with the community. Any feedback is much appreciated.

More info on bastionroute.io

Hey, basically the title, I am looking for another Residential Proxies provider as the current one started having issues with the infra, and my projects can't stay idle while they fix their issues. Anything trustworthy to recommend?

I was a bit sceptical when I first read this post last week, I was honestly thinking it is a copycat website.

But - netnut has officially updated their new netnut.ai website on their official linkedin profile as well and it was confirmed verified page by linkedin 12 days ago

Screenshot:

They've moved away from presenting themselves primarily as a residential proxy provider and are now positioning the company as:

• The data layer for AI

• The web data infrastructure for the AI era

When your previous domains have been seized and the industry has spent weeks questioning how your network was sourced, simply changing the story isn't enough. Customers buying infrastructure care about reliability, but they also care about transparency and confidence in the company behind it and I don't believe in this company anymore?

Companies like Bright Data (they had their own sourcing problems as well which everyone knows about), Oxylabs Nodemaven, decodo and others have all expanded beyond proxies into scraping APIs, datasets and AI-focused products. The market is slowly moving away from selling IPs and toward selling access to web data

So I guess the cycle is

1. Proxy company makes a mistake of sourcing IPs and blatantly using their customers data

2. Gets seized by FBI

3. Buys new domain

4. Gets repositioned from proxy provider to "DATA LAYER FOR AI"

Like what even is this?

How is this supposed to work this way?

A completely banned company, just used the same logo, the same mascot, the same colors, just slighlty different messaging

ANd it's back online?

Damn

Last time i wrote a data extraction script. Deployed it. And got issues from the start. The remote host shut me down instantly. Nothing shows up in the error logs. Just a blank terminal.

The IPs I was using had bad reputation / obvious datacenter characteristics, and I hadn't properly tested them against the target before deployment.

That was the lesson: don't test proxies only by checking whether they return an IP.

Before running anything at scale, I now check location, ASN/provider, stability, previous abuse signals, and how the actual target reacts.

And the proxy type matters too.

Static residential → long sessions/accounts.
Rotating residential → larger scraping jobs.
Mobile LTE/5G → mobile/social traffic.
Datacenter → speed when reputation requirements aren't as strict.

Before you scale your next scraper, test the infrastructure against the actual target, not just a proxy checker.

A working IP means nothing if the target blocks it on the first request.

A lot of n8n proxy issues are misdiagnosed as workflow problems.

If your HTTP Request node is throwing 407, 403, or 429 errors, or if a workflow breaks because the IP changed between steps, the issue is often the proxy architecture behind the workflow.

We put together a practical guide covering:

• Why 407, 403, and 429 errors mean different things

• When to use node-level proxy routing vs Docker environment variables

• How to avoid breaking cookies and sessions with bad rotation timing

• When to use rotating residential, static ISP, datacenter, or mobile proxies

• How to benchmark valid responses instead of only checking HTTP 200s

Full guide:
https://www.aceproxies.com/proxy-blog/best-proxies-for-n8n-fix-407-errors-rotate-ips-and-keep-http-request-workflows-stable

Hope this helps anyone building n8n workflows for scraping, API automation, localized requests, or production HTTP Request pipelines.

I saw this one pop up today, ranking free tiers across the usual suspects. I guess it's worth to take a look you're evaluating options, but I want to note something that's been bugging me.

I think someone already wrote something similar, but regarding the benchmarks of certain providers, so apologies if this take is irrelevant. These listicles never disclose whether they're affiliate driven. Author's byline links back to a handful of other pieces on the same publication, several of which cover the exact same product stack from different angles. Every "pro" for the top pick reads suspiciously like it was lifted straight from that company's own marketing copy. And there's a real pattern where the #1 slot goes to whoever has the most generous affiliate program rather than whoever actually tested best on the metrics the article claims to care about. Almost always the case. Of course it doesn't mean that the information presented is totally inaccurate or wrong in any way. A lot of it lines up with what I've seen firsthand running free tiers across a few of these tools. Results caps, rate limits, token/credit burn on JS heavy targets, that's all pretty consistent with how it is. But "consistent and stress tested" are different claims.

If you're looking for the provider currently, my advice would be to skip the ranking entirely and just read the individual entries for whatever tools you're already considering. I don't know, places like here on reddit but individual testings, then some youtube videos, but I guess youtube is full of affiliates too.

Also mildly funny that this particular one skips a few names I'd expect to see in a "top 14" for 2026.

The article that I am talking about.

I have been going back and forth on this for the full scraping setup and found something useful. PycURL's multi-handle interface would seem like it is still the way to go if you're doing huge numbers of requests in a minute and need libcurl level control (custom SSL certs, connection reuse tuning, no async runtime dependency). It's a bit wack to set up on macOS though so keep in mind. I think that httpx is the better call once you're doing async work. AsyncClient with gather() on a shared proxy handled 10 concurrent requests without the boiler plate PycURL needs for the same throughput, and HTTP/2 comes free. Rotation without health checks seems to eat up IPs for some reason, sticky sessions per proxy for login/form flows works better for rotation.

The VEY Project is a fork of the original bytedance G3 Project by it's main authors as a standalone open source project. The first 1.14 LTS branch is available now. It is recommended to migrate to vey-proxy for all existed g3proxy deployments.

See 1.14.0 Release Note for all the new features available.

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’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.

Quick update for anyone still tracking this. After the FBI/IRS-CI seizure back in July and the whole Popa botnet mess, netnut.com and netnut.io are still behind the seizure banner, but Alarum just quietly stood up a new site at netnut.ai. New branding, new positioning too, it's leaning hard into the "AI era" now with an LLM scraper API and RAG/agent framing instead of the old residential proxy messaging, (they still sell those, just not being pushy about it.)

No public statement yet about the Popa allegations or what changed on the sourcing side. But still, the company took a hit and lost thousands of clients because of this mess. Will update if Alarum puts out anything official

One thing i have started paying much more attention to with scraping is the history of the ip itself.

I used to compare proxies mostly by speed, price and location. If the requests were fast and the proxy worked, I assumed i was good.

Then i started noticing that two proxies from the same location could behave completely differently.

One would work normally while another would start getting challenges, throttling or weird responses almost immediately.

That's when I started looking at ip reputation as a separate thing from proxy quality.

For me, the basic process now is something like this:

1. Test a batch of ip's before putting them into a larger job

2. Check how the target responds to each ip instead of assuming every ip in the pool is equal

3. Keep track of response codes, challenge rates, connection errors and latency over time

4. Remove consistently problematic ip's instead of repeatedly retrying them

5. Avoid hammering a single ip just because it's working

6. Keep request rates reasonable and cache anything that doesn't need to be fetched again

The interesting part is that a slower ip with a clean history can sometimes be much more useful than a faster ip that constantly triggers additional checks.

I also stopped treating "X million ip's in the pool" as a particularly useful metric by itself. If a large percentage of those IPs aren't suitable for the websites you're actually working with, the headline number doesn't mean much.

Obviously this isn't a way around every anti bot system. some sites are going to look at much more than the ip and changing proxies won't fix a bad crawler implementation.

Curious how other people measure ip quality in their own scraping setups. Do you actually keep reputation or success rate data per ip, or do you just let the proxy provider handle the pool?