BotBrowser Performance Benchmark

Performance data comparing BotBrowser fingerprint protection overhead against stock Chromium, and evaluating Per-Context Fingerprint scalability.

Key Findings

Question	Answer
How much overhead does BotBrowser add?	Near-zero. Speedometer 3.0 shows <1% difference in both headed and headless modes, within run-to-run variance.
Are fingerprint APIs slower?	No. Canvas, WebGL, Navigator, Screen, and Font APIs show identical latency with or without fingerprint profiles on macOS, Linux, and Windows.
How does Per-Context Fingerprint scale?	At 50 concurrent profiles: 29% less memory, 57% fewer processes, 2x faster creation vs launching 50 separate browser instances. Full fingerprint isolation verified.
How does Trimmed Build compare to Standard?	On Linux x64 (400 official samples): 62% lower wall time, 85% faster per-context creation, 68% lower CPU peak, 31% lower PSS peak versus Standard, with 100% success rate and 0 residual processes. See Trimmed Build.

Single Instance Performance

Standard Benchmark Suites

All benchmarks run locally (no network dependency). Each test runs 3 times; median reported.

Test Environment: macOS (Apple M4 Max, 16 cores, 64GB RAM)

Mode	Benchmark	Stock Chrome	BotBrowser + Android	Difference
Headless	Speedometer 3.0	42.8 (±0.31)	42.7 (±0.25)	-0.2%
Headed	Speedometer 3.0	41.8 (±0.21)	42.1 (±0.17)	+0.7%

Methodology: Warm-up run (discarded) + 3 measurement runs with interleaved test order (Stock → BB → Stock → BB → Stock → BB) to eliminate thermal/cache bias. Scores shown as median ± stddev.

Result: BotBrowser’s fingerprint protection adds no measurable overhead to JavaScript and DOM performance. Differences of 0.1-0.3 points are well within run-to-run variance. Consistent across both headed and headless modes.

Fingerprint API Overhead

Individual API latency measured over 1,000 iterations each, median reported (milliseconds).

API	Stock Chrome	BB + Android	BB + Windows	BB + macOS
Canvas2D `toDataURL()`	0.5	0.5	0.5	0.5
Canvas2D `getImageData()`	0.0	0.0	0.0	0.0
WebGL `getParameter(RENDERER)`	0.0	0.0	0.0	0.0
WebGL `getSupportedExtensions()`	0.0	0.0	0.0	0.0
WebGL `getShaderPrecisionFormat()`	0.0	0.0	0.0	0.0
WebGL `readPixels()`	0.2	0.2	0.2	0.2
AudioContext Offline Render + Hash	3.9	2.9	3.2	3.7
Navigator `userAgent`	0.0	0.0	0.0	0.0
Navigator `platform`	0.0	0.0	0.0	0.0
Screen `width` + `height`	0.0	0.0	0.0	0.0
`performance.now()`	0.0	0.0	0.0	0.0
Font `measureText()` (50 fonts)	0.0	0.0	0.0	0.0
Intl `DateTimeFormat.resolvedOptions`	0.0	0.0	0.0	0.0

Result: Fingerprint protection adds zero overhead on all tested APIs. Canvas, WebGL, Navigator, Screen, and Font APIs show identical latency with or without a fingerprint profile. AudioContext variance (2.9-3.9ms) is within normal fluctuation.

Page Load Time

Median load time for a locally-intercepted page (milliseconds).

Configuration	Load Time	vs Stock
Stock Chrome (no profile)	29.7ms	-
BotBrowser + Android	101.6ms	+72ms
BotBrowser + Windows	102.9ms	+73ms
BotBrowser + macOS	105.9ms	+76ms

Note: The additional ~72-76ms comes from one-time profile initialization (reading and applying fingerprint configuration on first page load). All three profile types show similar overhead, indicating no significant difference between cross-platform profiles.

Scale Performance: Per-Context vs Multi-Instance

The core value proposition for enterprise users: running N different fingerprint profiles simultaneously.

Architecture Comparison

Architecture	How It Works
Multi-Instance	Launch N separate browser processes, each with a different `--bot-profile`
Per-Context (ENT Tier 3)	Launch 1 browser, create N BrowserContexts, each assigned a different profile via CDP `BotBrowser.setBrowserContextFlags`

Resource Usage at Scale

Test Environment: macOS (Apple M4 Max, 16 cores, 64GB RAM), Headless mode

Scale	MI Memory	PC Memory	Savings	MI Processes	PC Processes	MI Create Time	PC Create Time	Speedup
1	16,055 MB	14,022 MB	13%	140	136	1,667ms	627ms	2.7x
10	23,345 MB	19,586 MB	16%	212	150	11,434ms	4,854ms	2.4x
25	30,133 MB	23,781 MB	21%	320	174	28,205ms	14,415ms	2.0x
50	40,218 MB	28,553 MB	29%	492	210	57,891ms	28,946ms	2.0x

Per-Context memory savings increase with scale as the shared browser/GPU/network processes are amortized across more contexts.

Canvas Fingerprint Isolation

Each context/instance receives a unique noise seed, producing distinct canvas fingerprints. Verified across all scale levels:

Architecture	Scale	Unique Hashes	Status
Multi-Instance	10	10/10	PASS
Multi-Instance	25	10/10	PASS
Multi-Instance	50	10/10	PASS
Per-Context	10	10/10	PASS
Per-Context	25	10/10	PASS
Per-Context	50	10/10	PASS

Per-Context Fingerprint provides the same fingerprint isolation as running separate browser instances, while sharing infrastructure resources.

Scaling Characteristics

Multi-Instance (N browsers):

Memory grows linearly: each browser adds ~400-800MB
Process count grows linearly: ~7-10 processes per browser
Creation time grows linearly: ~1s per browser

Per-Context (1 browser, N contexts):

Memory grows sub-linearly: contexts share browser/GPU/network processes
Process count grows more slowly: shared processes are reused
Creation time grows sub-linearly: no browser startup overhead per context

At 50 Concurrent Profiles


Multi-Instance:  40,218 MB memory | 492 processes | 57.9s to create
Per-Context:     28,553 MB memory | 210 processes | 28.9s to create
                 ──────────────────────────────────────────────────
Savings:           29% memory     | 57% processes  |  2.0x faster

Context Lifecycle Performance

Continuous create/destroy cycle test (200 iterations):

Metric	Value
Context creation (median)	278ms
Context creation (p95)	369ms
Context destruction (median)	7.9ms
Context destruction (p95)	16ms
Memory trend (200 cycles)	Stable (no persistent growth)

Context creation is lightweight (~280ms) and destruction is near-instant (~8ms). Memory remains stable over 200 create/destroy cycles with no persistent memory leaks observed.

Trimmed Build: Linux x64 Short-Session Performance

Controlled comparison of BotBrowser 148 Standard Build vs BotBrowser 148 Trimmed Build on the same Linux x64 host using the same benchmark matrix. For positioning, engineering design, when-to-choose guidance, and FAQ, see the Trimmed Build product page. This section is the performance evidence.

Test Inputs

Build	Configuration	Chromium version	Profile
Standard	BotBrowser 148 x64 standard build	148.0.7778.168	Windows x64 demo profile, standard-compatible
Trimmed (ENT Tier3)	BotBrowser 148 x64 trimmed build (May 2026)	148.0.7778.168	Windows x64 demo profile, trimmed-compatible

Matrix: 1..20 contexts × 10 repeats × 2 builds = 400 official samples. Both profiles were verified to support per-context flags, per-context proxy settings, proxy auth, and custom headers before the matrix started.

Method

Each repeat created N independent BrowserContexts, applied BotBrowser.setBrowserContextFlags, assigned per-context external proxy settings, loaded a lightweight external page, and ran a minimal fingerprint and proxy probe.
Process metrics were sampled every 250ms. CPU came from Linux process accounting; RSS/PSS/USS came from process memory rollups.
Each repeat closed the browser and checked for residual Chromium processes.
Both builds used identical launch flags, identical context counts, identical repeat counts, identical warmup shape, identical external target, and identical sampling interval.

Overall Result

Negative delta means Trimmed is faster/lighter than Standard.

Metric	Standard mean / median / p95	Trimmed mean / median / p95	Trimmed delta mean / median / p95
Wall time ms	7,262.91 / 7,191.04 / 11,666.96	2,771.09 / 2,818.88 / 3,758.50	-61.85% / -60.80% / -67.79%
Avg create ms/context	443.44 / 418.73 / 604.90	65.10 / 64.75 / 75.54	-85.32% / -84.54% / -87.51%
Avg first nav ms/context	2,456.19 / 2,417.16 / 3,072.68	1,505.45 / 1,515.68 / 1,849.09	-38.71% / -37.30% / -39.82%
CPU avg %	4.12 / 4.22 / 4.78	1.83 / 2.02 / 2.89	-55.42% / -52.06% / -39.51%
CPU peak %	25.77 / 27.82 / 40.38	8.29 / 9.11 / 12.10	-67.81% / -67.25% / -70.03%
RSS peak KB	2,591,811 / 2,585,150 / 3,762,505	2,249,774 / 2,236,104 / 3,303,095	-13.20% / -13.50% / -12.21%
PSS peak KB	960,327 / 959,095 / 1,309,422	660,188 / 656,149 / 895,552	-31.25% / -31.59% / -31.61%
USS peak KB	800,760 / 793,448 / 1,128,869	497,876 / 497,038 / 718,904	-37.82% / -37.36% / -36.32%
RSS stable KB	1,499,234 / 1,491,875 / 1,819,937	1,377,981 / 1,360,862 / 1,758,705	-8.09% / -8.78% / -3.36%
PSS stable KB	637,313 / 635,427 / 747,051	492,405 / 490,987 / 591,358	-22.74% / -22.73% / -20.84%
USS stable KB	489,925 / 489,912 / 592,544	346,048 / 343,908 / 441,309	-29.37% / -29.80% / -25.52%
Process peak	26.27 / 27.00 / 40.10	19.50 / 19.50 / 28.05	-25.76% / -27.78% / -30.05%
Success rate	100%	100%	unchanged
Residual processes	0	0	unchanged

Per-Context Matrix

Wall time, context creation, and first navigation at every context count from 1 to 20. Negative percentage means Trimmed is faster than Standard at that context count.

N	Wall Standard ms	Wall Trimmed ms	Wall delta	Create delta	First nav delta	CPU avg delta	CPU peak delta	PSS peak delta
1	1,564.92	748.41	-52.18%	-85.49%	-52.51%	-95.52%	-97.09%	-27.89%
2	3,507.80	1,948.97	-44.44%	-88.33%	-43.61%	-92.05%	-89.86%	-28.94%
3	3,988.82	1,992.15	-50.06%	-87.99%	-43.78%	-80.93%	-63.42%	-29.17%
4	3,955.15	2,136.14	-45.99%	-84.01%	-37.29%	-74.09%	-52.54%	-30.15%
5	4,432.40	2,204.01	-50.28%	-85.08%	-37.87%	-68.19%	-51.68%	-30.41%
6	5,592.26	2,316.21	-58.58%	-87.36%	-40.32%	-63.93%	-66.42%	-29.53%
7	5,141.93	2,342.06	-54.45%	-83.81%	-35.61%	-59.40%	-61.96%	-31.03%
8	6,149.22	2,556.26	-58.43%	-84.29%	-37.96%	-54.73%	-59.51%	-30.61%
9	6,147.59	2,701.18	-56.06%	-83.77%	-31.58%	-53.26%	-66.09%	-31.56%
10	6,985.46	2,735.62	-60.84%	-85.10%	-36.79%	-54.62%	-68.59%	-32.06%
11	7,258.48	2,891.23	-60.17%	-84.34%	-36.46%	-52.11%	-61.73%	-31.18%
12	7,628.21	2,971.22	-61.05%	-83.98%	-33.97%	-48.32%	-68.29%	-31.84%
13	9,976.22	3,009.45	-69.83%	-87.65%	-45.89%	-49.07%	-71.87%	-31.06%
14	9,262.40	3,222.77	-65.21%	-85.47%	-37.76%	-47.50%	-67.77%	-32.02%
15	9,518.44	3,271.86	-65.63%	-85.19%	-38.25%	-44.81%	-65.17%	-32.83%
16	9,709.20	3,935.21	-59.47%	-79.76%	-29.05%	-34.23%	-65.82%	-32.73%
17	10,223.93	3,619.99	-64.59%	-84.21%	-37.29%	-41.66%	-68.99%	-31.83%
18	11,073.62	3,520.06	-68.21%	-85.94%	-39.39%	-43.13%	-70.31%	-32.56%
19	11,321.90	3,572.46	-68.45%	-86.59%	-40.62%	-43.81%	-72.06%	-30.90%
20	11,820.21	3,726.52	-68.47%	-84.57%	-41.15%	-39.52%	-71.87%	-31.44%

Trimmed is faster than Standard at every context count for wall time, average context creation, average first navigation, CPU avg, CPU peak, and PSS peak. The gap is largest in CPU peak (-51% to -97%) and per-context creation (-79% to -88%), which directly drive throughput for short-session privacy workloads that rotate identities quickly.

Stability

Browser main process, network service, GPU, and utility process counts are unchanged across builds.
Renderer count scales with context count in both builds.
Trimmed reduces peak process count overall: mean 26.27 → 19.50, median 27.00 → 19.50, p95 40.10 → 28.05.
All official runs exited cleanly: 400/400 successful samples, 0 errors, 0 residual Chromium processes.

Scope Notes

Linux x64 is the deployment shape Trimmed Build is built for: short-session, high-concurrency Ubuntu fleets are where the gap is largest.
The benchmark includes external navigation and uses a fixed matrix for both builds.
Profile bundles, host class, launch flags, and target endpoint were held identical between builds; runtime credentials and absolute paths are intentionally omitted from this customer-facing report.

Next Steps

Product overview, engineering design, and FAQ: TRIMMED_BUILD.md
Access: Enterprise or Pricing
Public mirror with extended commentary: Trimmed Build on botbrowser.io

Cross-Platform Verification

BotBrowser supports applying any platform’s fingerprint on any host OS. Tested on macOS, Linux, and Windows.

Fingerprint API Latency: macOS Host

All values in milliseconds (median of 1,000 iterations). Headless mode.

API	Stock Chrome	BB + Android	BB + Windows	BB + macOS
Canvas2D `toDataURL()`	0.5	0.5	0.5	0.5
WebGL `readPixels()`	0.2	0.2	0.2	0.2
AudioContext Render	3.9	2.9	3.2	3.7
All other APIs	0.0	0.0	0.0	0.0

Fingerprint API Latency: Linux Host

Tested in both headless and headed modes.

API	Headless Stock	Headless BB	Headed Stock	Headed BB
Canvas2D `toDataURL()`	0.3	0.3	0.3	0.3
WebGL `readPixels()`	0.1	0.1	0.1	0.1
AudioContext Render	8.5	7.1	9.0	7.7
Font `measureText()`	0.1	0.1	0.1	0.1
All other APIs	0.0	0.0	0.0	0.0

Fingerprint API Latency: Windows Host

Tested in both headless and headed modes.

API	Headless Stock	Headless BB	Headed Stock	Headed BB
Canvas2D `toDataURL()`	1.8	1.8	1.6	1.5
Canvas2D `getImageData()`	0.7	0.6	0.6	0.6
WebGL `readPixels()`	0.5	0.7	0.6	0.6
AudioContext Render	10.5	9.1	8.9	10.8
Font `measureText()`	0.3	0.3	0.3	0.3
All other APIs	0.0	0.0	0.0	0.0

Note: The Windows test environment uses software rendering (no discrete GPU), resulting in higher Canvas/WebGL baseline latency compared to macOS. The relative overhead between Stock and BotBrowser remains zero.

Page Load: Windows Host

Mode	Stock Chrome	BotBrowser + Android
Headless	28.7ms	27.9ms (-0.8ms)
Headed	27.9ms	27.1ms (-0.8ms)

Page Load: Linux Host

Mode	Stock Chrome	BotBrowser + Android
Headless	7.4ms	8.4ms (+1.0ms)
Headed	8.6ms	6.9ms (-1.7ms)

Result: Fingerprint protection overhead is zero for all core APIs across all three platforms and both rendering modes.

Methodology

Test Configuration

Setting	Value
Chrome version	145
Automation	Playwright (playwright-core)
Benchmark deployment	Local HTTP server (no network dependency)
Test categories	Speedometer 3.0, Fingerprint API micro-benchmarks (13 APIs), Page load timing, Per-Context vs Multi-Instance scale

How to Reproduce

All benchmark scripts are available in docs/benchmarks/:

Script	Purpose
`bench-baseline.js`	Single-instance benchmark (Speedometer + Fingerprint API + Page load)
`bench-scale.js`	Per-Context vs Multi-Instance scale test
`run-all.js`	Full automated orchestration
`fingerprint-bench.html`	Fingerprint API overhead test page
`config.js`	Platform detection and path configuration
`utils.js`	Shared utilities (stats, memory sampling)

To run them:


# 1. Clone benchmark suites locally
cd docs/benchmarks
./setup-benchmarks.sh
 
# 2. Install dependencies
npm install
 
# 3. Set environment variables
export BROWSER_PATH=/path/to/botbrowser/chrome
export PROFILES_BASE=/path/to/your/profiles
 
# 4. Run baseline benchmark (single instance)
node scripts/bench-baseline.js --mode headless
 
# 5. Run scale benchmark (per-context vs multi-instance)
node scripts/bench-scale.js --mode headless --max-scale 50

Statistical Method

Benchmark suites: Warm-up run (discarded) + 3 interleaved measurement runs per configuration, median ± stddev reported
Fingerprint APIs: 1,000 iterations per API, median reported
Page loads: 10 measurements per configuration, median reported
Scale tests: Single run per scale level (deterministic setup)

Environment Details

macOS (primary test platform):

Apple M4 Max (16 cores), 64GB RAM
macOS 15.3, arm64

Linux (cross-platform verification):

Ubuntu 22.04, x64

Windows (cross-platform verification):

Intel Xeon (20 cores / 40 threads), 64GB RAM
Windows 11, x64

Summary

BotBrowser’s fingerprint protection engine is designed for production workloads:

Zero measurable overhead on Speedometer 3.0: headless 42.7 vs 42.8 (-0.2%), headed 42.1 vs 41.8 (+0.7%)
Zero fingerprint API overhead. Canvas, WebGL, Navigator, Screen, Font APIs show identical latency on macOS, Linux, and Windows, in both headed and headless modes
Per-Context Fingerprint provides resource savings at scale with full fingerprint isolation:
- 29% less memory at 50 concurrent profiles
- 2x faster profile creation
- 57% fewer OS processes
- 100% canvas fingerprint isolation verified (each context produces a unique hash)
Consistent across platforms, tested on macOS arm64, Linux x64, and Windows x64 with identical results

For enterprise users running concurrent fingerprint profiles, Per-Context Fingerprint (ENT Tier 3) delivers the same privacy protection with lower infrastructure cost and verified fingerprint isolation between contexts.

Guides - Comprehensive guides for all BotBrowser features
Main README - Project overview and quick start
Per-Context Fingerprint - Architecture and API details
Validation Results - Research data across 31+ tracking scenarios
Advanced Features - Comprehensive technical capabilities
CLI Flags Reference - Complete command-line options

Related guides: Performance Optimization

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