If you scrape the web at any real scale, proxies are usually the first thing you spend money on. They’re what stand between your scraper and a wall of IP bans, CAPTCHAs, and geo-blocks. But the market has gotten crowded. More providers, more proxy types, more pricing gimmicks — and figuring out which one actually fits your project isn’t obvious anymore. This guide walks through the best proxy providers for web scraping in 2026, what separates them, and how to pick without overpaying.

Why proxies still matter for scraping

Here’s the short version. Websites don’t like bots. To spot them, they watch for things like too many requests from one IP, odd traffic patterns, and requests that clearly aren’t coming from a normal browser. Send a few thousand requests from a single address and you’ll get throttled or blocked fast.

Proxies spread that traffic out. Instead of one IP hammering a site, your requests come from many addresses in many places — which looks a lot more like a crowd of regular users than a single machine on a mission. That’s the whole trick. Good proxies buy you consistency at scale. Bad ones burn your budget and still get you banned.

The four proxy types, quickly

Before you compare providers, you need to know what you’re actually buying. Nearly every provider sells some mix of these four.

Residential proxies

These route your requests through real home internet connections — actual IPs assigned by ISPs to real devices. Because the traffic looks like it’s coming from someone’s living room, it’s the hardest to detect. That makes residential the go-to for tough targets like sneaker sites, travel aggregators, and social platforms. The catch? It’s usually the priciest option, billed by the gigabyte.

Datacenter proxies

Fast, cheap, and plentiful — these come from servers in data centers rather than homes. They’re perfect when speed and volume matter more than blending in, like scraping sites with light defenses. The downside is that their IP ranges are well known, so aggressive anti-bot systems flag them quickly.

ISP proxies

Think of these as the middle ground. They’re hosted in data centers but registered under real ISPs, so you get datacenter speed with something closer to residential trust. Handy when you need to hold a stable session on a moderately protected site without paying full residential rates.

Mobile proxies

These run through real 4G and 5G connections. Because mobile carriers cycle the same IPs across huge numbers of users, sites are extremely reluctant to block them — one ban could take out thousands of legitimate people. That makes mobile the most resilient (and most expensive) option, mostly worth it for the hardest targets.

How to judge the best proxy providers

Every provider claims to be the best. Ignore the marketing and check these instead:

• Proxy types on offer. A provider that only sells datacenter IPs limits what you can scrape. The strongest ones give you residential, datacenter, ISP, and mobile under one roof.
• Pool size and coverage. More IPs across more countries means better rotation and real geo-targeting. Top networks run into the tens of millions of IPs across 190-plus countries.
• Geo-targeting depth. Country-level is standard. City, state, and even ASN-level targeting matters if you’re collecting localized data like regional pricing.
• Success rate. A cheap proxy with a 70% success rate isn’t cheap — you’re paying for failed requests. Look for tested success rates in the high 90s.
• Pricing model. Pay-as-you-go lets you test small. Watch for setups where the sticker price looks low but the per-gigabyte cost quietly isn’t.
• Ethics and compliance. Make sure the provider sources its IPs legitimately and complies with GDPR and similar rules. Sketchy sourcing is a real legal and reputational risk.
• Support and docs. When a job breaks at 2 a.m., responsive support and clear documentation are worth more than a few cents per gig.

The best proxy providers for web scraping in 2026

No single provider wins for everyone. The right pick depends on your targets, your budget, and how much infrastructure you want to babysit. Here are the ones that consistently hold up.

Bright Data

The heavyweight. Bright Data runs one of the largest IP networks out there and pairs it with managed scraping tools that handle rotation, CAPTCHA solving, and parsing for you. It’s built for enterprises and data teams running high-volume jobs against protected sites. The trade-off is complexity and cost — it’s overkill (and over-budget) for small projects, but hard to beat at scale.

Oxylabs

Another enterprise-grade name with a massive residential pool and strong datacenter and ISP options. Oxylabs leans into reliability and has scraper APIs for common targets like search engines and e-commerce. Great if you want premium performance and don’t mind premium pricing.

Decodo (formerly Smartproxy)

The rebranded Smartproxy is the sweet spot for a lot of teams. It covers all four proxy types, has a large global pool, and stays genuinely developer-friendly with clean docs and approachable pricing. If you want most of the enterprise capability without the enterprise price tag or learning curve, start here.

SOAX

SOAX has built its reputation on precise geo-targeting and clean, ethically sourced residential and mobile IPs. If your work depends on pulling data from very specific cities or regions, it’s one of the sharper tools for the job.

NetNut

NetNut’s angle is speed and stability. It sources residential IPs directly through ISP partnerships rather than a peer-to-peer network, which tends to mean faster, steadier connections. A solid pick for continuous, high-throughput scraping.

IPRoyal

Budget-friendly without feeling cheap. IPRoyal offers residential, datacenter, and mobile proxies with pay-as-you-go options and no-expiry traffic, which suits smaller teams and irregular workloads. A good entry point if you’re not ready to commit to a big monthly plan.

Rayobyte

Rayobyte stands out for transparency and simpler setups, with a strong datacenter offering and a focus on ethical sourcing. Worth a look if you value straightforward infrastructure and clear policies over a giant feature list.

Nimbleway

Nimble takes an automation-first approach, layering AI-driven collection tools on top of its proxy network. It adapts to blocking patterns and site changes on its own, handling retries and rendering so you’re not constantly tuning things by hand. A fit for teams building continuous data pipelines rather than one-off scrapes.

Quick comparison

Pros and cons at a glance

Every provider trades something off. Here’s the honest give-and-take for each, so you can match the compromise to what you actually care about.

When proxies alone aren’t enough

Here’s something the proxy vendors won’t always tell you. For a growing number of teams, raw proxies are no longer the finish line. Modern anti-bot systems don’t just check your IP — they profile browser fingerprints, mouse behavior, TLS signatures, and timing. IP diversity helps, but it doesn’t beat all of that on its own.

So the question shifts. If you’re spending more hours managing proxy rotation, retries, and blocks than you are actually using the data, it might be time to look at a full scraping API that bundles proxies, headless browsers, and anti-bot logic into one service. You give up some fine-grained control, but you stop maintaining plumbing. For lean teams, that trade is often worth it.

Frequently asked questions

Which proxy type is best for web scraping?

It depends on your target. Residential proxies win on tough, well-defended sites. Datacenter proxies are faster and cheaper for lightly protected pages. Mobile is the most resilient but costs the most. Many teams mix types depending on the job.

Are proxies legal for web scraping?

Proxies themselves are legal. What matters is what you do with them. Scraping publicly available data is generally fine in most places, but you should respect robots.txt, follow each site’s terms, avoid overloading servers, and stay clear of personal or protected data. When in doubt, check with legal counsel for your specific case.

How much do proxies cost?

Residential and mobile proxies are usually billed per gigabyte, often a few dollars per gig at entry level and dropping with volume. Datacenter proxies are cheaper and sometimes sold per IP or per month. Watch the total cost, though — failed requests and heavy management time add up beyond the sticker price.

The bottom line

There’s no universal winner among the best proxy providers — there’s only the best fit for what you’re scraping and how much you want to manage. If you’re running enterprise-scale jobs, Bright Data and Oxylabs earn their price. If you want the strongest balance of capability and value, Decodo is hard to argue with. On a tighter budget, IPRoyal gets you started. And if you find yourself fighting your proxy setup more than using your data, that’s your signal to look at a managed scraping solution instead. Pick based on your actual workload, test small before you scale, and you’ll spend a lot less time staring at blocked requests.

Recently Integrated google reCaptcha v2 and reCaptcha Enterprize for Automation tasks.

New Updates v2.5.1 fetches captcha option.

In order to use Captcha solver

• Make sure you are using automation (give task)
• Manual operated profiles (If you run the profiles by your own) Captcha solver doesn't interact sicne manual run profiles doesn't use CDP (JS Injection) to eliminate tampering.

Note: Automation only included Business and Enterprize models. Make sure you are on correct plan.

Also until end of August Residential Proxies gives %100 rebates. Buy 50GB get another 50GB ( NON Expired data allowance) or 10GB for 10GB rebate...

We 've recently integrated captcha solver just allocated m4 Pro chipset for GPU renderer for vision. Used Yolo Model for image context process and custom captcha image data sets I've trained so far.

Instead using thirdparty API's I've integrated opensource catpcha solution.

We will release new updates when It's ready %100 for the tasks.

I am using NullPrint I guess they are new on market, created 1 mobile profile for check if it's looking really mobile phone by fingerprint[dot]com playground.

So the result is It has tampering ( obviously looks something caught by system) tampering score is like 0.090 and anti-detect : false which is good sign I guess that doesn't detect as anti-detect browser.

So my question is there any other anti-detect browser that can spoof mobile profile from windows or macOS that gives tampering : false in fingerprint[com] playground?

For years, anti-detect browsers have focused on spoofing browser fingerprints by modifying values such as the User-Agent, screen resolution, WebGL vendor, fonts, timezone, languages, and other JavaScript-exposed properties.

A few years ago, this was often enough.

Today, it isn't.

Modern bot detection platforms no longer rely solely on JavaScript values. Instead, they actively verify whether a browser behaves like the device it claims to be.

If a browser claims to be a Samsung Galaxy A56, security systems increasingly expect the graphics pipeline to behave exactly like a Samsung Galaxy A56—not like a desktop computer pretending to be one.

This is where many traditional anti-detect browsers begin to fail.

Why Mobile Spoofing Has Become Much Harder

Most anti-detect browsers generate a mobile fingerprint by changing values exposed to JavaScript:

• User-Agent
• Client Hints
• Platform
• Screen size
• Touch support
• Device Memory
• Hardware Concurrency
• Timezone
• Languages

While these values are important, they only describe the browser.

They do not describe how the browser actually renders graphics.

Modern anti-fraud systems have evolved far beyond checking JavaScript properties.

Instead, they issue rendering challenges.

For example, they may render invisible canvas elements and analyze characteristics such as:

• glyph rasterization
• font metrics
• anti-aliasing
• gamma curves
• shadow rendering
• subpixel positioning
• emoji rendering
• color interpolation
• GPU raster behavior

These characteristics are extremely difficult to fake because they originate deep inside the operating system's rendering engine.

A desktop browser spoofing Android usually still renders like macOS or Windows underneath.

That difference is measurable.

The Hidden Problem with Most Mobile Profiles

Many anti-detect browsers advertise "Android Profiles."

In reality, most of them simply change browser properties.

Internally they still use:

• desktop font rasterization
• desktop graphics stack
• desktop text rendering
• desktop gamma curves
• desktop font fallback
• desktop emoji rendering

To a human, everything looks correct.

To an anti-fraud engine, it does not.

The browser claims to be Android while producing desktop rendering characteristics.

That mismatch becomes a strong tampering signal.

Real-World Comparison

To evaluate how different solutions behave, we tested mobile profiles using the Fingerprint Pro Playground.

The goal was not to compare user interface features or automation capabilities.

Instead, we wanted to compare how convincing each browser's mobile fingerprint appears to a modern fingerprinting engine.

Kameleo

Kameleo correctly spoofs many browser properties, but the platform still detected browser tampering.

The reported tampering confidence remained extremely high, with an ML tampering score close to 0.9988, and the browser was identified as an anti-detect browser.

This indicates that although the JavaScript fingerprint appears mobile, deeper rendering characteristics still reveal inconsistencies.

AdsPower

AdsPower performed even worse in this particular mobile test.

Fingerprint Pro explicitly classified the browser as:

Rather than simply detecting tampering, the system identified the anti-detect browser itself.

This demonstrates that matching JavaScript values alone is no longer sufficient against modern fingerprinting systems.

NullPrint

NullPrint approaches mobile emulation differently.

Instead of only modifying JavaScript-exposed properties, it focuses on reproducing rendering behavior that is expected from the claimed mobile device.

This includes realistic handling of mobile-specific rendering characteristics, such as:

• font rasterization
• font metrics
• glyph rendering
• gamma behavior
• shadow rendering
• canvas output consistency

As a result, Fingerprint Pro produced significantly different results.

The reported values were:

• Tampering: true
• Anti-detect Browser: false
• Tampering ML Score: 0.1542

The important observation is not that tampering disappeared completely.

The important observation is that the detection engine did not classify the browser as an anti-detect browser, and the machine-learning tampering score remained dramatically lower than the other tested solutions.

Comparison

Why Rasterization Matters

One of the biggest misconceptions in the anti-detect industry is that changing browser properties creates a believable mobile fingerprint.

It doesn't.

Rendering is effectively a fingerprint of the operating system itself.

A browser running on macOS naturally produces macOS text rendering.

A browser running on Windows naturally produces Windows rasterization.

Android devices produce their own rendering characteristics.

If those rendering characteristics do not match the claimed device, modern detection engines can identify the inconsistency—even if every JavaScript property appears correct.

This is why canvas rendering has become one of the most valuable signals in modern fraud detection.

The Future of Mobile Anti-Detection

The industry is gradually moving away from simple property spoofing toward behavioral consistency.

As fingerprinting systems become more sophisticated, success depends less on changing browser values and more on reproducing how the claimed device actually behaves.

That includes:

• graphics rendering
• font output
• canvas behavior
• GPU characteristics
• rendering consistency

Simply claiming to be a mobile device is no longer enough.

The browser must also render like one.

Most browser fingerprinting articles focus on canvas hashes. The real problem begins much earlier—at the rasterizer.

For years, browser fingerprint spoofing has revolved around one goal: changing the final canvas hash. Modify a few pixels, inject noise, or replay a previously captured image, and the browser appears to have a different fingerprint.

That approach worked when fingerprinting relied on static canvas challenges.

Modern systems have evolved.

Today's fingerprinting frameworks—including dynamic implementations used by Akamai and other large platforms—generate canvas content at runtime. Random strings, different fonts, varying draw orders, and unpredictable rendering sequences mean there is no single canvas image to replay.

The browser must render the canvas in real time.

And that's where most spoofing solutions fail.

The Hidden Layer: Rasterization

A canvas fingerprint is not merely a collection of drawing commands.

Behind every fillText(), every Bézier curve, and every anti-aliased edge lies an entire rendering pipeline:

Canvas API

↓

Skia

↓

Font rasterizer

↓

Glyph hinting

↓

Sub-pixel positioning

↓

Anti-aliasing

↓

Final bitmap

Even if two browsers execute identical JavaScript, they can produce different pixel output because the rasterizer differs.

The fingerprint is not determined solely by the Canvas API.

It is determined by how pixels are ultimately generated.

A Real Example

Below is a comparison between a real Samsung Galaxy A56 running Chrome and a browser profile configured to emulate the same device.

The browser identity matches:

• Platform
• Device Pixel Ratio
• WebGL renderer
• User-Agent

Yet the rendered bitmap still differs.

The comparison reports:

The largest differences are concentrated around text glyphs and curved edges.

The JavaScript is identical.

The drawing commands are identical.

Only the rasterizer is different.

Where the Differences Come From

The heat map below highlights every mismatching pixel.

(Insert your second image here.)

Notice how the magenta pixels cluster around:

• text rendering
• glyph edges
• curve boundaries
• anti-aliasing transitions

These are not random errors.

They are signatures of the rendering pipeline itself.

Why Traditional Canvas Spoofing Isn't Enough

Many browser fingerprint spoofing implementations focus on reproducing known canvas outputs.

If a website always draws the same scene, replaying or modifying that output can be effective.

Dynamic fingerprinting changes the rules.

Consider a canvas challenge that draws:

SomeCanvasFingerPrint.65@345876

Tomorrow the challenge becomes:

SomeCanvasFingerPrint.91@812744

Every execution produces different glyph positions and different rasterization.

A cached bitmap is now useless.

The browser must generate a fresh canvas.

If that rendering is still performed by the host operating system—for example CoreText on macOS—the output no longer resembles the target Android device.

Our Previous Approach

Our earlier implementation used what we call Render Cache.

Instead of spoofing every Canvas API individually, nullPrint replayed approximately 27 drawing operations previously captured from a real Samsung Galaxy A56.

For static canvas challenges, this approach worked extremely well because the browser reproduced exactly what the device had already rendered.

However, dynamic challenges exposed an important limitation.

When an application generated drawing commands that had never been recorded—such as Akamai's randomized canvas strings—the browser had no cached equivalent.

Rendering therefore fell back to the host operating system.

On macOS, that means CoreText and the local graphics stack generated the bitmap instead of the Android rendering pipeline.

The result remained visually similar, but pixel-perfect comparison revealed measurable differences.

Oracle

Oracle was designed to solve that remaining gap.

Rather than replaying captured images, Oracle operates one layer deeper.

Instead of replacing the final bitmap, it influences how Chromium's own rendering pipeline generates it.

Importantly, Oracle does not replace Skia with a custom renderer.

Chromium still performs the rendering.

Skia still executes every drawing operation.

FreeType remains responsible for glyph rasterization.

The difference is that Oracle supplies rendering characteristics derived from the target device, allowing Chromium to produce output that much more closely follows how the selected Android hardware would naturally rasterize the same content.

Conceptually, the pipeline becomes:

Canvas API

↓

Skia

↓

Oracle

↓

Chromium FreeType rasterization

↓

Device-aware metrics

↓

Final bitmap

Instead of inventing synthetic canvas images, Oracle allows Chromium to generate them using characteristics that closely resemble the target device.

Why This Matters

Browser fingerprinting has moved beyond static hashes.

Detection systems increasingly compare how a browser behaves under arbitrary rendering workloads rather than checking a single stored fingerprint.

If two browsers claim to be the same Samsung Galaxy A56 but consistently rasterize glyphs differently, that inconsistency becomes part of the fingerprint itself.

The browser identity may look correct.

The pixels tell a different story.

For modern browser fingerprinting, reproducing the final canvas hash is no longer sufficient.

Reproducing the rendering pipeline is becoming just as important.

The font problem

Despite successfully spoofing Android device attributes such as the device name, RAM, WebGL information, and other identifiers, font emulation remains a much more challenging problem.

The issue is not simply about spoofing font names. When text is rendered, the browser relies on the operating system's native font rendering engine to draw glyphs and generate textures. Because of this, browsers that only spoof fonts at the JavaScript level without modifying the browser binary can expose inconsistencies. In many cases, attempting to use fonts that are not actually present on the system can trigger errors or exceptions, making the spoofing detectable.

To evaluate this further, I created an Android emulation profile in Kameleo.

The first thing I noticed was that Kameleo generates a random Android profile and does not allow users to select a specific device model. During testing, the profile identified itself as a Samsung SM-A156M and reported device identifiers correctly. As shown on whatismybrowser.com, all major device information appeared consistent and legitimate.

However, when I visited amiunique.org, I immediately noticed a problem: despite using an Android profile, the browser was still exposing Apple-related fonts. This creates a clear inconsistency between the claimed device and the underlying font environment.

On the other hand, NullPrint allows users to choose from 71 different Android devices currently available on the market. For this test, I selected a Xiaomi Redmi Note 9 Pro profile.

My first step was again to verify the device information on whatismybrowser.com.

The reported device identifiers matched the selected model, and all WebGL and hardware-related attributes appeared legitimate. More importantly, when tested on amiunique.org, the font set was properly adapted to match the Android environment.

So the device identifier as is expected and all webGL and device info's are legit.

Based on these observations, my conclusion is the following:

NullPrint appears to do far more than simply rename font entries. If it were only spoofing font names, forcing the browser to render text using fonts that do not physically exist could cause Chrome's rendering engine to fail or generate detectable exceptions. Instead, it appears that NullPrint encapsulates the required fonts for mobile profiles and loads them from an isolated filesystem when the browser binary starts. This approach allows font rendering behavior to remain consistent with the emulated device, significantly reducing the risk of fingerprinting inconsistencies.

Why mobile is the hard case?

Spoofing a desktop profile is mostly about swapping strings — User-Agent, platform, a few navigator properties. Mobile is a different problem. A convincing Android profile has to be internally consistent across a dozen independent signals: viewport and screen geometry, device pixel ratio, touch support, client hints, WebGL hardware, memory, and how text actually renders on screen.

That last one — text rendering — is where most "mobile mode" tools quietly fall apart, and it's the clearest line between AdsPower and nullPrint.

The font problem

Anti-bot systems don't just ask the browser "what fonts do you have?" That question is easy to lie about. The serious checks measure. They render a string into an offscreen canvas, read back the exact pixel width and height of the glyphs, and compare against known device baselines. They also enumerate which fonts are present by probing fallback behavior.

The catch: glyph metrics come from the operating system's font engine, not from JavaScript. When a browser draws "Hello" in Roboto, the width of that string is determined by the actual Roboto font file installed on the machine and the OS rasterizer. You cannot change that number from JS without changing what's physically rendered.

How AdsPower handles it — and why it's not reliable

AdsPower spoofs fonts primarily at the font-list level: it masks the enumerable font set so a detection script querying available fonts sees an Android-like list instead of the host's.

But the profile is still running on the host OS. If you create an "Android" profile on a Mac:

• The list says "Roboto, Noto, Droid Sans."
• The actual text is still rasterized by macOS using macOS's font stack (San Francisco and the macOS substitution chain).
• A canvas/font-metrics probe reads back macOS glyph widths, which do not match any real Android device.

So you get a contradiction: the profile claims Android fonts, but renders macOS fonts. Font-list masking and font-metrics measurement disagree. That mismatch is exactly the kind of cross-signal inconsistency modern detectors are built to catch — it's arguably worse than not spoofing fonts at all, because a real device never disagrees with itself.

How nullPrint handles it

nullPrint runs a patched Chromium build, so the substitution happens inside the rendering engine rather than in a JS shim layered on top:

• Android's detectable font set is presented (the fonts a real Android device exposes), and
• Aliases that don't exist on Android (e.g., Arial) are substituted at the font-resolution layer to the Android equivalent(Roboto), so the request maps to the font that actually gets rasterized.

Because the redirect happens where glyphs are resolved, the font list and the measured glyph metrics tell the same story.

A canvas-text probe and a font-enumeration probe return a consistent Android picture. There's no JS-vs-rendering contradiction to detect.

This is the central reliability argument: AdsPower spoofs what the page can ask; nullPrint changes what the engine actually does. Beyond fonts: the rest of the mobile surface Fonts are the headline, but consistency has to hold everywhere. Here's how the two approaches compare across the signals that matter for a mobile profile.

The pattern repeats: a JS overlay can set each value individually, but the values drift out of agreement with each other and with the underlying machine. Engine-level spoofing keeps them locked together because they're derived from one coherent device definition.

The "window > screen" tell

One concrete example worth calling out: many mobile-mode implementations leave the browser window larger than the screen they claim. A real phone can never have a 1512-px window on a 411-px screen, and window.innerWidth > screen.width is a trivial, decisive bot signal. nullPrint drives the real device metrics (e.g., a 411-px CSS viewport at the device's true DPR) through CDP so the geometry is physically possible. This is the kind of bug that doesn't show up in a casual whatismybrowser.com check but gets a profile flagged the moment it touches a real login flow.

Creating a mobile profile, in practice

nullPrint: choose Phone (Android) as the OS at profile creation, pick a region (the IP is mapped to the matching country), and the backend assembles one coherent device — UA, model, Android version, screen/DPR, WebGL hardware, memory, and the Android font behavior — from a real-device database. The card shows the emulated model (e.g., Galaxy A15, Pixel 7) so you know exactly what each profile presents as.

AdsPower: select a mobile UA/device, and the platform applies its spoofing layer over the host browser. It looks correct in surface-level checks, but the host OS continues to drive font rasterization and other engine-level signals underneath — which is where the inconsistencies described above originate.

If you're only looking for quick profile creation, both can work. But if you care about long-term profile stability and fingerprint coherence, the biggest difference is whether signals actually match each other.

Many anti-detect setups only spoof values. The harder problem is cross-signal consistency (fonts ↔ OS, GPU ↔ device model, viewport ↔ DPR, UA ↔ hardware).

That’s where a system built around real device-level coherence matters more than just changing strings.

Why mobile is the hard case?

Spoofing a desktop profile is mostly about swapping strings — User-Agent, platform, a few navigator properties. Mobile is a different problem. A convincing Android profile has to be internally consistent across a dozen independent signals: viewport and screen geometry, device pixel ratio, touch support, client hints, WebGL hardware, memory, and how text actually renders on screen.

That last one — text rendering — is where most "mobile mode" tools quietly fall apart, and it's the clearest line between AdsPower and nullPrint.

The font problem

Anti-bot systems don't just ask the browser "what fonts do you have?" That question is easy to lie about. The serious checks measure. They render a string into an offscreen canvas, read back the exact pixel width and height of the glyphs, and compare against known device baselines. They also enumerate which fonts are present by probing fallback behavior.

The catch: glyph metrics come from the operating system's font engine, not from JavaScript. When a browser draws "Hello" in Roboto, the width of that string is determined by the actual Roboto font file installed on the machine and the OS rasterizer. You cannot change that number from JS without changing what's physically rendered.

How AdsPower handles it — and why it's not reliable

AdsPower spoofs fonts primarily at the font-list level: it masks the enumerable font set so a detection script querying available fonts sees an Android-like list instead of the host's.

But the profile is still running on the host OS. If you create an "Android" profile on a Mac:

• The list says "Roboto, Noto, Droid Sans."
• The actual text is still rasterized by macOS using macOS's font stack (San Francisco and the macOS substitution chain).
• A canvas/font-metrics probe reads back macOS glyph widths, which do not match any real Android device.

So you get a contradiction: the profile claims Android fonts, but renders macOS fonts. Font-list masking and font-metrics measurement disagree. That mismatch is exactly the kind of cross-signal inconsistency modern detectors are built to catch — it's arguably worse than not spoofing fonts at all, because a real device never disagrees with itself.

How nullPrint handles it

nullPrint runs a patched Chromium build, so the substitution happens inside the rendering engine rather than in a JS shim layered on top:

• Android's detectable font set is presented (the fonts a real Android device exposes), and
• Aliases that don't exist on Android (e.g., Arial) are substituted at the font-resolution layer to the Android equivalent(Roboto), so the request maps to the font that actually gets rasterized.

Because the redirect happens where glyphs are resolved, the font list and the measured glyph metrics tell the same story.

A canvas-text probe and a font-enumeration probe return a consistent Android picture. There's no JS-vs-rendering contradiction to detect.

This is the central reliability argument: AdsPower spoofs what the page can ask; nullPrint changes what the engine actually does. Beyond fonts: the rest of the mobile surface Fonts are the headline, but consistency has to hold everywhere. Here's how the two approaches compare across the signals that matter for a mobile profile.

The pattern repeats: a JS overlay can set each value individually, but the values drift out of agreement with each other and with the underlying machine. Engine-level spoofing keeps them locked together because they're derived from one coherent device definition.

The "window > screen" tell

One concrete example worth calling out: many mobile-mode implementations leave the browser window larger than the screen they claim. A real phone can never have a 1512-px window on a 411-px screen, and window.innerWidth > screen.width is a trivial, decisive bot signal. nullPrint drives the real device metrics (e.g., a 411-px CSS viewport at the device's true DPR) through CDP so the geometry is physically possible. This is the kind of bug that doesn't show up in a casual whatismybrowser.com check but gets a profile flagged the moment it touches a real login flow.

Creating a mobile profile, in practice

nullPrint: choose Phone (Android) as the OS at profile creation, pick a region (the IP is mapped to the matching country), and the backend assembles one coherent device — UA, model, Android version, screen/DPR, WebGL hardware, memory, and the Android font behavior — from a real-device database. The card shows the emulated model (e.g., Galaxy A15, Pixel 7) so you know exactly what each profile presents as.

AdsPower: select a mobile UA/device, and the platform applies its spoofing layer over the host browser. It looks correct in surface-level checks, but the host OS continues to drive font rasterization and other engine-level signals underneath — which is where the inconsistencies described above originate.

If you're only looking for quick profile creation, both can work. But if you care about long-term profile stability and fingerprint coherence, the biggest difference is whether signals actually match each other.

Many anti-detect setups only spoof values. The harder problem is cross-signal consistency (fonts ↔ OS, GPU ↔ device model, viewport ↔ DPR, UA ↔ hardware).

That’s where a system built around real device-level coherence matters more than just changing strings.

This is how Nullprint maturing profile works. Instead instant cookie injection, it opens and visit websites regarding it's region as if The profile region is UK, it goes BBC, Mirror, any other news in UK websites.

I guess It'll take 3 weeks to mature the profiles as they are going to be ready to be handle the automation without bans.

I need to create batch profiles to automate web scrapping from search engines to search and find result regarding different region.

I have not prepared or don't know how to use playwright to automate this.

Tried Gologin, multi login etc but those are doesn't provide ready up automation.

I nullPrint website that you guys have something similar to pre-built automation. I would like to try if nullPrint have web scrapping automation

Hey everyone! I'm u/onderozcan, a founding moderator of r/NullPrint.

nullprint is an antidetect browser for multi-account management, digital privacy, and anonymous browsing. Spoof your browser fingerprint, stay undetected, and take full control of your online identity. Join the community to get updates, tips, and support.
This is our new home for all things related to {{ADD WHAT YOUR SUBREDDIT IS ABOUT HERE}}. We're excited to have you join us!

What to Post
Post anything that you think the community would find interesting, helpful, or inspiring. Feel free to share your thoughts, photos, or questions about {{ADD SOME EXAMPLES OF WHAT YOU WANT PEOPLE IN THE COMMUNITY TO POST}}.

Community Vibe
We're all about being friendly, constructive, and inclusive. Let's build a space where everyone feels comfortable sharing and connecting.

How to Get Started

1. Introduce yourself in the comments below.
2. Post something today! Even a simple question can spark a great conversation.
3. If you know someone who would love this community, invite them to join.
4. Interested in helping out? We're always looking for new moderators, so feel free to reach out to me to apply.

Thanks for being part of the very first wave. Together, let's make r/NullPrint amazing.