PUBLIC VALIDATION

Public validation benchmarks and consistency evidence

Review public validation scope, benchmark results, and cross-platform consistency evidence in one place.

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40+ public check scenarios4 supported platforms100+ isolated contextsOne browser model

Public validation scope

Evidence is organized around signal dimensions, runtime paths, and public checks that show how the model stays aligned.

Consistency evidence

The same profile is expected to stay aligned across supported platforms and runtime modes.

Benchmark and rollout depth

The key parts of the browser model remain stable from first validation to scaled deployment.

What this public evidence covers

Validation scope, benchmark coverage, and consistency standards describe the supported operating model.

VALIDATION METHODOLOGY

How the public validation model works

The public proof model is organized around scope, method, reproducible benchmarks, and alignment criteria.

Validated scope

Signal groups, runtime paths, and platform combinations that represent the current public scope.

Validation method

Public checks, reproducible replay methods, and cross-platform runs under the same browser model.

Alignment standard

Stable profile output, no obvious runtime drift, and consistent behavior across the supported rollout path.

40+

Validated scenarios

Public checks plus reproducible validation paths

Supported platforms

Windows, macOS, and Linux hosts plus Android targets

100+

Isolated contexts

Independent runtime model for scaled workloads

Browser model

The same core from first validation to enterprise rollout

PUBLIC BENCHMARKS

Public benchmark evidence under load and scale

Public benchmark results cover both performance pressure and concurrent profile scale.

<1%

Speedometer overhead

Headed and headless runs stayed within run-to-run variance versus stock Chromium.

0ms

Added API latency

Canvas, WebGL, Navigator, Screen, and Font APIs showed no measurable added latency.

29%

Less memory at 50 profiles

Per-context architecture used less memory than launching 50 separate browser instances.

57%

Fewer processes at 50 profiles

Shared browser infrastructure cut process count at the same 50-profile scale.

Benchmarks were run locally across macOS, Linux, and Windows.

Public benchmark runs also include roughly 2x faster profile creation at 50 concurrent profiles.

Benchmark scope, repeatable method, and operating behavior are all documented publicly.

PUBLIC VALIDATION COVERAGE

What public validation covers today

The signal dimensions and runtime paths below are the public evidence areas used to judge whether the model stays aligned.

Signal collection coverage

Public proof includes the browser-facing signal groups most likely to reveal drift or leakage.

CanvasWebGLAudioUA-CH

Tracking correlation coverage

Validation also looks at where browser, network, and storage state start to correlate across runs.

Proxy pathSession continuityStorage stateIP linkage

Runtime parity coverage

The model is checked across runtime modes and host environments where output often starts to drift.

Headless parityGUI parityLinux runtimeAndroid targets

Identity control coverage

The proof surface also includes the layers that matter once the browser becomes part of a larger workflow.

FontsWebRTCGPU pathContext isolation

CONSISTENCY MODEL

Proof is about keeping the same model stable across checks and rollout stages

The strongest public proof point is that the same browser model stays coherent across platforms and rollout stages.

Same profile across platforms

The product is designed to keep one profile aligned across Windows, macOS, and Linux hosts, while Android targets are simulated from that same model.

Runtime discipline

Proxy paths, isolated contexts, and browser-level control stay part of the same deployment model.

Scale without model drift

The same proof model extends from early validation into long-running and enterprise-oriented deployment stages.

RELATED GUIDES

Guides that support the public validation claims

These guides explain how the checks work and which browser signals matter most when you evaluate consistency.

PlatformMay 31, 2026

Cross-Platform Browser Profiles: One Identity Across Hosts

Run the same browser profile across Windows, macOS, Linux, and Android-target environments while keeping browser signals aligned with the selected identity.

Read guide

FingerprintApr 1, 2026

Client Hints Fingerprinting: HTTP Headers as Identity

Client Hints headers like sec-ch-ua expose browser brand, version, platform, and device details with every HTTP request. Learn how inconsistencies in these headers create trackable signals and how to maintain consistency.

Read guide

FingerprintApr 1, 2026

WebRTC Codec Fingerprinting: Media Capabilities Leak

WebRTC codec enumeration through getCapabilities() and SDP offers exposes hardware-specific media capabilities that differ across operating systems. Learn how codec lists become a platform fingerprint and how to control them.

Read guide

DeploymentMar 25, 2026

Scaling Browser Contexts: 100+ Identities Per Machine

How to run over 100 concurrent browser contexts with independent fingerprints using Per-Context Fingerprint architecture. Includes benchmark data, Puppeteer examples, and production optimization tips.

Read guide

FingerprintApr 24, 2026

Canvas Fingerprinting: How It Works and How to Stop It

Canvas fingerprinting uses HTML5 rendering differences to track users across sessions without cookies. Learn how engine-level control produces consistent, authentic Canvas output on every platform.

Read guide

FingerprintApr 24, 2026

WebGL Fingerprinting: Control GPU Identity and Renderer

WebGL exposes your GPU model, driver version, and rendering output as high-entropy fingerprint signals. Learn how to control all WebGL parameters at the engine level for consistent protection.

Read guide

Map your target checks to the right public validation path

Match public checks, platform scope, and rollout stage to the right model path.

Request validation walkthrough Open proof docs