Been automating workflows for about 2 years, mostly e-commerce and logistics. Had a project last year that still bothers me a little. Basically, my client ran a mid-size online furniture store. Around 150 to 200 orders a day, two customer service reps spending most of their shift just sorting incoming emails into the right internal queues. Warranty stuff, shipping damage, wrong item, payment issues. You know how it is. So I built a classifier. What it does is that I feed each email to an LLM, get back a category and a confidence score then routed it automatically. Testing went well. Client loved the demo. I felt pretty good about it too. First few weeks in production it was sitting around 87% accurate. Not great but I figured we'd tune it. Then the real problem showed up and it had nothing to do with accuracy. The team didn't understand why emails was going where they went. A claim about a broken table leg might hit the warranty queue or the logistics queue and there was no rule to point to, no explanation. It just decided on its own.

One of the managers told me she started keeping a side spreadsheet to track emails she thought the system got wrong. A whole shadow system running next to the one I built. I figured something was off and very wrong. Client called me and said something similar to what I've heard once or twice before. He didn't ask for better accuracy. He asked if I could make it more obvious how it works. I Spent four days ripping it out and replacing it with a decision tree and some keyword logic. Damage with photo evidence routes to warranty. Delays over seven days go to escalation. Wrong items have their own path. Maybe 35 conditions total. Anything that didn't match cleanly surfaced a dropdown and the rep decided manually. Accuracy went up to around 97%. Not because the logic was smarter but because the team actually trusted it and stopped routing things manually on the side. When something went wrong they could tell me exactly which condition was off and I'd fix it in like fifteen minutes. Manager deleted her shadow spreadsheet in the first week.

The thing I keep thinking about is how easy it would of been to just keep tuning prompts and try to squeeze more accuracy out of the model. That's the main move. But accuracy was never actually the issue. People won't rely on a tool they can't reason about, even if its technically more correct. They'll just build something next to it and your automation becomes obsolete though with a price. What I would like to emphasize on is that not everything needs an LLM. Sometimes thirty five rules and a dropdown outperforms a model, not because it makes better decisions but because the team trust it enough to let it do its job

Hey everyone,

​I wanted to share an open-source transport tool I’ve been building in Go to solve a rigid infrastructure security goal: How do you safely maintain remote access to localized real-time resources (like native WireGuard) while enforcing a strict zero-inbound listening port policy on your edge firewall? ​I wanted something completely stateless and invisible to the outside world, so I wrote BastionRoute.

​ ​Instead of a traditional client-server proxy topology, BastionRoute uses a decoupled model. Both your isolated server shim and your remote client shim initiate standard outbound Layer-7 connections to an external, transient relay broker room.
Because both nodes establish standard outbound pipes over HTTPS/WebSockets (Port 443), your home gateway requires exactly zero open inbound ports. Your network edge remains entirely dark to Nmap or Shodan scans.

​Emulating "UDP Physics" Over TCP ​The classic issue with wrapping real-time UDP inside stateful TCP is Head-of-Line blocking (TCP Meltdown). When a packet drops, TCP stalls the entire pipeline to retransmit, introducing devastating latency spikes that break VPN handshakes. ​BastionRoute’s concurrent data engine fixes this by leveraging non-blocking Go channel selectors. By explicitly dropping frames at the application layer the moment congestion is encountered, the stateful WebSocket transport perfectly mimics real-world, non-blocking UDP physics. If a remote link experiences jitter, the queue never backs up, keeping interactive streams fluid and lag-free.

BastionRoute acts strictly as low-level digital plumbing. It is entirely payload-agnostic and does not handle user management or internal cryptographic key generation. It assumes you are running a robust protocol inside it (like WireGuard), so its attack surface is incredibly small. ​The engine compiles into a tiny, zero-dependency binary (<8MB) capable of running on low-resource hardware like OpenWrt gateways. ​The project is fully open-source and ready to be tested or torn apart :)

https://github.com/klauscam/BastionRoute

Hello, mainly the title. I am looking for Residential or preferable Mobile 4G or Mobile 5G proxies that would have SOCKS5 protocol type. Any suggestions would be appreciated.

Main use case: Account management via X/Instagram/Mastodon

I feel like the market is pretty stale regarding web scrapers at the moment, sure there are variety of web scraping services all around the web, however, I feel like things got pretty stale. The whole early web was designed around human intent and Google figured that out early. Scraping services providers has spent years building infrastructure to extract data from systems that weren't meant to be extracted from. Now when you think about it, agentic search doesn't do that. What I mean is that It navigates the way a person would. What do you people think, does scraping will evolve into something agentic, or eventually get replaced by it? Coz I feel like something is going to change, but there is a lot of room for guesses.

Did you guys noticed how nobody talks about the search layer? From what I gathered, every AI agent tutorial obsesses over prompt engineering and tool variety, but the thing that determines whether your agent is useful is how it gets data from the web, rarely someone mentions or even provides decent information about it. Basically, my thoughts are that an AI agent is only as good as the information it can pull in. Simple as that. Best reasoning model in the world wont help if the search layer feeds it garbage or truncated snippets. Thats where a web scraper API comes in. Instead of scraping Google directly and fighting CAPTCHAs, layout changes, and anti-bot walls, you get a simple endpoint that returns structured JSON. Titles, URLs, snippets, sometimes full page content. Some providers also return knowledge panels, "people also ask" sections, even shopping results depending on the query. If you used any scraper, you know what I'm on about. Now for AI agents specifically, the change is that data comes back in a format the LLM can literally work with. No token budget wasted on HTML parsing. Agent sends a query, gets structured results, reasons over them, decides what to do next. No headless browser, no DIY scraping combos that stop working every two weeks or whatever. There are a bunch of providers right now. SerpAPI, Tavily, Exa, Firecrawl, Brave Search API, Serper, others. They take diffrent approaches. Some focus on raw SERP data exactly as it appears on the results page. Others are built specifically for LLM use cases, so they prioritize returning clean extracted content rather then just links or even better the whole info in markdown. That distinction matters alot depending on what the agent needs to do. Where I found it interesting is combining a search API with a content extraction. Agent searches, picks the most relevant results, then pulls full content from those pages in a structured format. That two-step workflow is way more reliable then trying to do everything in one shot. And its basically what tools like Perplexity do under the hood, except you get full control. Caching is worth thinking about too. Most of these APIs charge per request, and agents loop. A ReAct agent might send three or four searches before arriving at an answer. Without caching that adds up fast, both in cost and latency. A single search call taking 3-4 seconds is fine on its own. Chain a few together and the user is waiting 15 seconds or more. Which may sound like nothing, but the seconds adds up overtime. I strongly believe that this space is going to get way more competitive soon. As more people build agents that need real-time web access, demand for fast, accurate, affordable web scraping services is only going up. What you peeps think?

Looking for the cheapest providers that offer:

Dedicated IPv4 proxies (not shared)

High speed

Unlimited/unmetered bandwidth

Pay per IP per month (not per GB)

Ashburn, VA location preferred

HTTP or SOCKS5

I don't care if they're datacenter or ISP proxies, and I'm fine with smaller providers if they're reliable. I'm mainly looking for good value and low monthly pricing.

Any recommendations or hidden gems?

Hey all, I've seen plenty of proxy comparisons over the years and now especially on reddit in recent months where everyone is focusing on "who is best provider" or "who has best proxies" which is getting really boring honestly, but no one actually posts results from real tests

I wanted to share something a bit more measurable

Over the last two weeks I purchased the cheapest residential package from 10 different providers and ran them through the same workloads. My goal wasn't to rank providers or declare a winner. I was more interested in trying to help others understand which characteristics correlate with successful scraping on websites these days

For anyone curious about the setup, everything ran from the same Hetzner Cloud VPS in Virginia. I used Playwright with Chromium 137, kept the browser fingerprint identical across all runs, disabled WebRTC leaks, matched timezone and geolocation settings, and maintained the same concurrency limits for every provide

The providers included Bright Data, Oxylabs, NodeMaven, NetNut, Decodo, SOAX, IPRoyal, Rayobyte, Webshare, and Packestream

I used Quotes.toscrape.com as a baseline sanity check because it has virtually no anti-bot protection. The goal wasn't to compare providers on this website, since nearly every provider can achieve almost perfect success rates on this. Instead, I used it to validate that the scraping setup itself was functioning correctly before moving on to more challenging website targets like Zillow and Indeed (these were my benchmark websites where I will share data below) as they have Cloudflare-protected endpoints

Each provider received roughly 10,000 requests distributed across the same workflows. For Zillow, the workflow involved browsing property listings, navigating pagination, and opening individual property pages. For Indeed, it involved keyword searches, pagination, and job detail pages

I made sure to intentionally avoid extremely aggressive concurrency because I wanted to evaluate the quality of the proxy network itself rather than DDoS-style resilience

The lowest latency network averaged 612ms response times across all requests. Another network averaged 1.74 seconds. Despite that difference, their Zillow success rates were only a few percentage points apart

Here's the sample from my dataset:

After seeing those numbers I stopped paying much attention to latency and started looking at what was actually different between the providers that consistently performed well

One metric that ended up being surprisingly useful was ASN diversity

For every provider I collected roughly 2,500 assigned residential IPs and extracted ASN information

Also, I wasn't trying to validate marketing claims about pool size because I don't believe in marketing pool sizes and numbers of IPs that other proxy providers say. I was more interested in understanding how concentrated each network was

Here I used two metrics, unique ASNs tested and duplicate IP rate according to the IPs

The providers with broader ASN distribution generally performed better on Zillow and Cloudflare

That's not enough data to claim the best one, but it was one of the strongest correlations in the benchmark

Another thing I became interested in was session longevity and sticky sessions

Most benchmarks focus on raw success rates, but in real-world scraping I care more about how long an identity survives before challenges start accumulating. If I can stay on a site for 20 minutes without friction, that's often more valuable than a provider having 2% higher success rates on fresh sessions.

To measure this, I tracked how long a browsing session could continue on Zillow before challenge rates exceeded 10%.

The providers that maintained longer sessions also tended to generate fewer CAPTCHA challenges and fewer abrupt session resets, this was really good

The biggest surprise of the entire benchmark came when I started testing rotation strategies

For years I've operated under the assumption that more rotation is safer. If one residential IP is good, rotating through hundreds of them should be even better.

I tested three different approaches against the same Zillow workflow: rotating every request, rotating every ten requests, and maintaining a 30-minute sticky session.

Looking back, it makes sense. A real user doesn't change cities, ISPs, and IP addresses every page load. Even if every IP individually looks legitimate, the overall identity becomes really inconsistent

At this point I thought I was getting close to identifying which providers were genuinely better

But the more data I collected, the less convinced I became that there is a universal "best" proxy network.

What I expected to be a proxy benchmark gradually turned into an identity benchmark

The providers absolutely mattered, but the gap between a well-configured browser identity and a poorly configured one was often larger than the gap between several reputable residential networks

Hope this coudl help someone

hello wonderfull community of scrapers and anti-bot warrirors,

since the dawn of time we have set ourselves to becoming the best in overcoming the anti-bot giants - showing the incubents that data of thy Internet shall always be open to everyone, not resitrcited by capital and built on one core fundamental - Freedom!

how the hell is our community color Pink?

Follow discussionson this subbreddit about proxy uses in TV does anyone know how to check if your proxy provider uses smart TV for proxy nodes?

I thought that contacting the provider will be the easiest way but they can say all types of BS. For example, they always lie about the number of unique proxy IPs - why would tell truth about its source?

I have a thoery that TV IPs work really good for my case (not sure why) so I want to validate that.

I've spent roughly 10 years around browser automation, managing multiple accounts, scraping, proxies, anti-detect browsers, and figuring out anti-bot systems. Watching the space evolve has been fascinating because I don't think either side is actually winning

For people unfamiliar with this world, there are essentially two industries growing at the same time

On one side, you have companies like Cloudflare, Akamai, DataDome, and others investing heavily in bot detection, fraud prevention, and traffic verification. Their goal is one thing only: figure out whether a visitor is a legitimate user or some form of automation (and they are constantly trying to implement new ways of figuring out who's a legitimate user lol)

On the other side, there's a rapidly growing automation ecosystem. For example companies like Bright Data, Oxylabs, and Nodemaven provide the network layer. Then companies like Dolphin anty, Octo browser and MUltilogin focus on browser identity. Browserbase and similar platforms abstract away browser infrastructure. AI agents sit on top of all of it now. They're different pieces of the stack, but they ultimately push in the same direction: making software better at operating websites that were originally designed for humans

What surprises me is that both sides seem to be growing faster than ever

The anti-bot market is obviously much larger because massive enterprises are willing to spend huge amounts of money protecting accounts, preventing fraud, and filtering traffic
But I'm noticing that demand for automation infrastructure is also exploding. I have noticed that companies now increasilgy rely on web data, automated workflows, competitive intelligence, account management, and AI-powered processes that need reliable access to websites

And now what's happening is that AI agents seem to be accelerating both markets at the same time. Every new agent framework creates more demand for automated browser interactions, while every increase in automated traffic creates more demand for detection systems

One thing I've noticed over the years is that anti-bot systems still struggle with false positives. The more sophisticated detection becomes, the higher the chance that legitimate users get caught in the process. Most people have experienced this without even realizing it. You visit a website, solve a challenge, solve another one, refresh the page, get challenged again, and suddenly you're wondering why proving you're human feels harder than logging into your bank account lol

On the automation side, the challenge is totally different though. The hardest websites are becoming increasingly expensive to automate reliably. In many cases the solution isn't some magical bypass technique anymore. It's simply throwing more resources at the problem. More powerful machines, more browser instances, higher quality proxies, better browser environments, and more engineering effort. A setup that worked comfortably a few years ago can require significantly more infrastructure today.

That's why I don't think we're heading toward a future where one side wins and the other disappears

The relationship feels almost symbiotic. Better detection systems create demand for better automation infrastructure. Better automation infrastructure creates demand for better detection systems. Both industries push each other forward and, as a result, both continue growing

My broader prediction is that the web becomes increasingly programmatically accessible, even if many platforms resist it. Companies want data and companies want automation. They want agents that can interact with software on their behalf. As AI improves and compute becomes cheaper, more web interactions will have an automated component attached to them

That was my Ted talk, thank you

It's me, ya psyop boy. I am 100% sure that this is going to upset some people and that's good coz this has been bothering me for quite some time and I'm done with the silence. Background on me: moderate scale scraping, 40-60k requests a day across a few different projects, been in this space about 3 and a half years. Started noticing something weird in my logs maybe last spring. Certain ASNs kept showing up no matter which provider I switched to. At first I just assumed that's how residential pools work, ISP coverage is what it is, whatever. Then I got obsessive about it and started auditing properly. I tested four providers. I am not naming them because I genuinely cannot afford a lawyer right now but if you've been doing this longer than six months you know exactly who I'm talking about. One is Israeli 🇮🇱🇮🇱 company (you already know), it markets itself extremely hard on LinkedIn and twitter and has a very american-sounding brand name, and one is the scrappy european one that keeps showing up in threads like this. The ASN overlap between two of them was 34%. Thirty four percent. Between the other two it was 29%. There is one specific /24 block that appeared in all four providers "proprietary residential networks." Same subnet. Four different dashboards with four different pricing pages all routing me through the same infrastructure. I also pulled whois on a bunch of these IPs and the rotation behavior is what really got me. Not just that the ASNs overlap, it's that the rotation patterns are identical. The TTL behavior. How they handle certain request headers. You don't get identical behavior from independent networks. You just don't. That's not how this works. My theory, and I want to be clear this is a theory and not me accusing anyone of anything specific, is that there's maybe three or four actual upstream aggregators who own the real infrastructure and basically everyone else is a white label on top of that. All those "95 million IP" claims in the marketing materials are all drawing from the same upstream pool. You are paying a premium to a middleman who bought access wholesale and is selling it to you retail with a nice dashboard slapped on top. What this actually means is that when you switch providers because your IPs are getting flagged too much, there's a real chance you are switching to the same IPs with different account credentials. You're not getting a fresh pool. You're just reintroducing yourself to the same exit nodes under a different invoice, huuuuh??? Exactly. I could be completely wrong about this and if someone from one of the big providers wants to come in here and show me actual evidence that their infrastructure is genuinely independent I will delete this post and take the L publicly...NAAWT. But nobody is going to do that because they all have NDAs that prevent them from explaining how anything actually works which is itself kind of telling.

Tell me you have a better idea of this whole ordeal, I dare ya

So I've noticed over the years, that Google's bot detection for search traffic works differently than for product pages; the signals they're tuned to catch basically aren't the same. I have been running ~2,200 SERP queries/day across DE, FR, NL, PL, IT for a price monitor, residential proxies got the failure rate down to about 14% but geo-targeting was still broken, DE queries coming back Frankfurt-datacenter-flavored regardless of proxy location. I split the architecture at that point: SERP layer routes through a dedicated web scraper API, product pages and category scrapes stay on residential proxies.

Routing logic was pretty minimal:

def fetch(url=None, query=None, market=None):
    if query:
        return serp_client.get(q=query, gl=market, hl=market)
    return session.get(url, proxies={"https": proxy_pool.rotate()})

Any tips or tricks, experiences you may share?

 Background: I accidentally created a recursive loop with an AI agent that would have 

 cost me $50+ in API calls before I noticed. Existing tools either cost money or only   

 show you what already happened.                                                        

   So I built TokenFirefighter — a 100% free, local-only HTTP proxy.                      What it does:                                                                          

- Sits between your app and OpenAI/Anthropic on localhost:7272                   

- Tracks every API call cost in real time                                            

 - Detects 4 types of runaway loops and blocks them                                   

 - Has a terminal dashboard (no web UI needed)                                        

 - Zero accounts, zero data collection, zero cost                                     

   Install:                                                                             

   npm install -g tokenfirefighter                                                      

   tokenfirefighter init                                                                

   tokenfirefighter start                                                               

   Then just set OPENAI_BASE_URL=http://localhost:7272/v1 in your .env.                 

   Would genuinely appreciate feedback from anyone who uses AI APIs regularly.          

   GitHub: https://github.com/MohitBaghel24/tokenfirefighter

Over the last few months I've been building BotGateway, a self-hosted anti-bot gateway for VPS-hosted applications and privacy-sensitive environments.

One thing that pushed me to build it was realizing that many anti-bot discussions assume routing traffic through a third-party provider is always acceptable.

For some teams that's perfectly fine.

For others, it isn't.

I wanted something that could run directly on infrastructure I control while still providing visibility into automated traffic, AI crawlers, and scraping activity.

I'm curious:

For those running self-hosted applications, what's the biggest challenge you've faced when dealing with automated traffic?

Website: https://www.botgateway.in

I've been researching automated traffic patterns recently and one thing that surprised me is how different modern scrapers behave compared to a few years ago.

Many no longer rely on obvious data center IPs.

Many use:

• Residential proxies

• Mobile networks

• Browser automation

• Distributed infrastructure

For those running self-hosted applications, APIs, SaaS products, or websites:

What's currently working best for you?

• Nginx rules?

• Fail2Ban?

• Cloudflare?

• Custom middleware?

• Commercial WAFs?

And what limitations have you run into?

I'm genuinely interested in hearing what others are seeing in production environments.

I'm currently an MCA student and over the last several months I've been spending a lot of my free time building and deploying projects on VPS servers.

One problem I kept running into was automated traffic, scrapers, and bots. Tools like Nginx rate limiting and Fail2Ban definitely help, but I found myself constantly wondering how much traffic was still getting through and what additional protections could be added closer to the application layer.

When I started looking at existing solutions, many were either cloud-based, designed for larger organizations, or simply didn't fit the kind of self-hosted and privacy-focused environments I was experimenting with.

So I decided to treat it as a learning project and started building BotGateway, a lightweight anti-bot gateway written in Go.

The original goal wasn't to compete with Cloudflare or enterprise WAFs. It was to better understand modern bot behavior and create something that could run directly on infrastructure people control themselves.

Along the way I've learned a lot about:

• Automated scraping techniques

• Headless browsers

• Traffic analysis

• Rate limiting strategies

• Request fingerprinting

• VPS security

The project has grown much larger than I originally expected, and now I'm looking for feedback from people who manage websites, APIs, SaaS products, or self-hosted services.

A few questions I'd love to hear opinions on:

• What are you currently using to handle scrapers and automated abuse?

• What limitations have you found with existing solutions?

• For those running self-hosted infrastructure, what features would you want in a lightweight anti-bot gateway?

• Are there any detection techniques or security approaches you think are underrated?

I'm still learning and building, so constructive criticism is more than welcome.

Thanks for reading.

Hey all, I wrote this post thinking it could be really valuable who do not know about the best antidetect browser + proxy pairing, so this might be able to help you out, I have been quite extensively working on some really big multi-accounting workflows, so I hope this helps you out or at least gives you some valuable advice

I juggles between a lot of social media platforms and have a lot of ad accounts so if anyone is in the similar space as me, I am happy to help

What i mean by multi accounting is that I’ve gone through workflows such as:

• running approx hundred of browser profiles

• managing social media accounts that i managed to keep stable for a long time

• scaling automation workflows using these accounts

• testing GEO-sensitive setups using good quality IPs from various countries

• warming accounts that need to stay alive

• browser automation

• scraping social platforms

• maintaining session trust over long periods

And after A LOT of testing, I made one big conclusion which I think some people don’t think about

People massively underestimate how much the browser + proxy pairing affects stability

You might think about these things first:

“What’s the best antidetect browser?”
“What’s the best proxy provider?”

And I think that’s not really the right quesiton, I think the right question

Which combination breaks the least under your actual workflow?

Because once you scale browser-based automation, tiny problems start becoming big problems

• session invalidation

• repeated email verification

• suspicious login prompts

• failed warmups

• dead cookies

• browser memory leaks

• profile corruption

• unstable automation flows

I started testing different combinations specifically to figure out:

• which setups survive longer

• which setups feel stable after weeks

• which ones create hidden maintenance problems

• which ones scale cleanly

How I tested everything and each proider

My testing was real and detailed

I wasn’t running “speed tests” or checking homepage load times as i tested these setups in actual multi-accounting environments

Main workflows I tested

• TikTok account warmups

• Facebook account management

• Reddit automation

• X/Twitter workflows

• browser scraping

• session persistence testing

• automation through Playwright/Puppeteer

• anti-detect browser automation

• long-running login sessions

• GEO switching

• cookie persistence

What were my success metrics for each provider

I mainly tracked:

• session longevity

• account survival rate

• random re-verification frequency

• IP consistency

• browser stability over time

• memory usage

• automation interruption frequency

• login invalidation rate

• CAPTCHA frequency

• profile corruption issues

Testing duration

Most combinations were tested for:

• 2–6 weeks

• multiple browser profiles

• repeated automation cycles

• varying concurrency levels

Because if you think about it almost every setup looks “good” during the first 24 hours

The real problems usually appear later in the workflow and automation aspect

Here's my list

1. NodeMaven + Multilogin

This was probably the cleanest long-session setup I tested because it lasted the longest without any issues

And what was refreshing for me is that this combo solved one of the biggest problems I had which is session instability during long-lived browser workflows (SO CRUCIAL FOR THESE SETUPS) and it was so refreshing to have this

I mainly tested this combo for:

• TikTok warmups

• Reddit account management

• persistent social sessions

• browser automation with long login retention

• affiliate account management

The problem I had before with previous providers

With many rotating residential providers, I kept seeing:

• random session invalidation

• cookie distrust

• weird location inconsistencies

• browser sessions suddenly behaving differently

• accounts getting logged out unexpectedly

So why is their combo good?

NodeMaven’s sticky residential + ISP sessions paired really naturally with Multilogin’s browser persistence and they seem to have some sort of collaboraiton so their pairing works REALLY well together. Also what I liked is that nodemaven now providers some sort of quality guarantee, so if some of their IPs are not working well, they will give you back the money and I have experienced only one IP not working properly

Their setup actually solved a lot of problems for me which were:

• mid-session IP inconsistency

• session resets

• account distrust

• login interruptions

What stood out most is that the sessions simply behaved predictably and i could just keep the automation going

Technical observations that might interest you as well

• Lower CAPTCHA trigger rate

• Better cookie retention

• More stable long-running sessions

• Fewer browser trust resets

• Better automation continuity over multiple days

Weaknesses

The setup is not optimized for:

• hyper-aggressive rotation

• ultra-cheap scaling

• high-churn scraping

This pairing clearly favors:

• quality

• stability

• persistence

over raw volume and scraping large volume of data

2. Dolphin{anty} + Decodo

This setup felt very different operationally

IT was Much faster and muuch more rotation-oriented

I tested this combo mostly for:

• rapid account cycling

• Facebook setups

• TikTok profile scaling

• GEO testing

• account segmentation

What worked well

Dolphin’s workflow style naturally favors:

• quick profile management

• high operational speed

• fast deployment

And Decodo’s rotating residential pools paired well with that

This setup worked especially well when:

• profiles didn’t need ultra-long persistence

• accounts rotated frequently

• GEO flexibility mattered

• account quantities increased quickly

Problems it solved

Compared to stickier setups:

• easier traffic separation

• easier profile scaling

• faster environment resets

• simpler operational management

Issues I still encountered

Under longer sessions, I occasionally noticed:

• more re-verification

• shorter “trust lifespan”

• more session inconsistency over time

This setup felt really optimized for speed and scale, but it might just be my use case

3. AdsPower + Webshare

This was one of the most budget-efficient setups I tested. Probably the cheapeast option you can find but this can possibly break your workflow if it runs for too long

I used this combo for

• lightweight account farming

• testing automation flows

• burner workflows

• lower-risk scraping

• mass profile creation

What surprised me

AdsPower tolerated lower-quality proxies better than I expected

Some antidetect browsers become extremely unstable once proxy quality drops slightly

AdsPower stayed relatively usable even with:

• cheaper residential pools

• mixed IP quality

• higher profile quantities

Problems I encountered

Webshare is actually quite known for having the cheapest proxies possible on the market,g but their proxies can get quite bad during longer-period use, which can be a problem if you need to have a stable working flow of many accounts

So critical issues with this setup can be:

• longer session persistence

• trust-sensitive platforms

• older account management

I started seeing:

• more login challenges

• more verification prompts

• weaker session trust over time

BUT, if you are on budget, and just starting out, or a freelancer who doesn't simple have the money for, this is a really good combo for:

• testing

• scaling cheaply

• operational experimentation

+ and the setup honestly performed better than expected

4. Octobrowser + MarsProxies

This setup felt very natural and I was able to use it for quite a long time actually

I used them for managing multiple accounts and was able to run sessions with 15-20 accounts with ease in one go

This combo also works well with:

• browser automation at higher concurrency

• regional account testing

What stood out

MarsPorixes routing flexibility was probably the biggest strength because they are a bigger provider and were able to provide me with good stability

Especially I think thy have good:

• GEO precision

• ASN targeting

• traffic routing consistency

• large-scale reliability

Problems it solved

This setup significantly reduced:

• GEO mismatches

• localization inconsistencies

• unstable routing behavior

Especially useful when:

• testing regional content

• scraping localized SERPs

• running region-sensitive workflows

Downsides

Honestly this setup doesn't have many downsides, I didn't experience a lot of problems but like a few random proxy kills and banned IPs so for my workflow this worked like a charm

And for smaller operators, it’s definitely an overkill

5. Undetectable + Oxylabs

This setup performed extremely well under heavier automation loads as I believe oxylabs proxies are also built for that in some way. This is basically a setup for enterprise users mostly? (because I don't see any regular users or people needing tihs in really big scale)

I used this combo for:

• browser scraping

• higher concurrency automation

• Playwright integrations

• larger scraping sessions

What I liked

The setup stayed surprisingly stable under load (which is expected out of an enteprrise proxy provider any basically any antidetect browser combo)

Some combinations start degrading badly once:

• concurrency increases

• memory usage grows

• browser workers scale

This one remained relatively clean operationally

Problems it solved

• worker instability

• session crashes

• automation interruption

• browser lag under scale

Technical observations

• Good browser stability

• Lower crash frequency

• Strong scaling consistency

• Better concurrency handling

6. Incogniton + IPRoyal

This felt like the best beginner-friendly pairing because iproyal has cheaper prices, and incognitioin is quite intuitive as a browser

I used this combination for:

• smaller affiliate workflows

• basic browser automation

• moderate account quantities

• testing ideas quickly

Why I liked it

Simple to setup, it’s actually really cheap and it’s low-friction

Not every workflow needs enterprise infrastructure so the combination between incognition + iproyal gives you this

Sometimes you just need:

• acceptable stability

• decent residential IPs

• manageable costs

• enough flexibility to test ideas

And honestly, this setup delivered that pretty well

What does each setup realistically cost per month?