Rust vs TinyGo WebAssembly: Engineering Decision Guide

Rust Performance

Avg Execution Time: 179.82ms

Avg Memory Usage: 4.12MB

Success Rate: 100.0%%

TinyGo Performance

Avg Execution Time: 197.17ms

Avg Memory Usage: 103.55MB

Success Rate: 100.0%%

⚖️ Performance Stability

Stability Score: 86.1%

Consistency Leader: Rust

Risk Assessment: Moderate

📊 Statistical Significance

Execution Time p-value

min <0.001; median 0.000; 9/9 significant

Memory Usage p-value

min <0.001; median 0.001; 5/9 significant

Effect Size

Large (avg d=29.64; med=3.92; min=0.00; max=221.51; n_large=13/18)

Confidence Level

95%

📈 Performance Comparison Charts

Distribution & Variance Analysis: Performance Consistency

Box plots show data distribution, variance, and stability patterns for engineering decision-making.

Execution Time Comparison: Rust vs TinyGo (Mean ± SE with Medians)

Memory Usage Comparison: Rust vs TinyGo (Mean ± SE with Medians)

Cohen's d Effect Size Heatmap

📊 Performance Stability Analysis

🎯 Overall Stability Assessment

Stability Score: 86.1%

High Variance Ratio: 13.9%

Risk Level: Moderate

Consistency Leader: Rust

🦀 Rust Performance Consistency

Stability Score: 100.0%

Avg Execution CV: 0.005

Avg Memory CV: 0.000

High Variance Count: 0

🐹 TinyGo Performance Consistency

Stability Score: 72.2%

Avg Execution CV: 0.007

Avg Memory CV: 0.184

High Variance Count: 5

📋 Engineering Insights

Performance consistency leader: Rust
Deployment risk level: Moderate
Stability confidence: 86.1%
High variance reduces the reliability of mean-based performance comparisons
Effect size interpretations should account for variance heterogeneity

📋 Detailed Performance Breakdown

Task	Scale	Rust (ms)	TinyGo (ms)	Speed Advantage	Memory Advantage	Overall Recommendation
jsonParse	small	2.2	45.1	Rust 95.2% faster	No significant difference	👍 Moderate recommendation: rust (strong performance advantage)
jsonParse	medium	6.5	9.8	Rust 34.0% faster	Rust 98.7% less memory	🔥 Strong recommendation: rust (consistent winner in both metrics)
jsonParse	large	13.2	20.1	Rust 34.7% faster	Rust 98.9% less memory	🔥 Strong recommendation: rust (consistent winner in both metrics)
mandelbrot	small	19.3	19.8	Rust 2.8% faster	No significant difference	👍 Moderate recommendation: rust (strong performance advantage)
mandelbrot	medium	148.4	152.3	Rust 2.5% faster	Rust 66.7% less memory	🔥 Strong recommendation: rust (consistent winner in both metrics)
mandelbrot	large	1165.6	1192.5	Rust 2.3% faster	Rust 80.5% less memory	🔥 Strong recommendation: rust (consistent winner in both metrics)
matrixMul	small	17.3	23.2	Rust 25.5% faster	No significant difference	👍 Moderate recommendation: rust (strong performance advantage)
matrixMul	medium	56.9	73.7	Rust 22.7% faster	No significant difference	👍 Moderate recommendation: rust (strong performance advantage)
matrixMul	large	189.1	237.9	Rust 20.5% faster	No significant difference	👍 Moderate recommendation: rust (strong performance advantage)

🎯 Engineering Decision Matrix

🚀 Choose Rust When:

Maximum performance is critical
Memory efficiency is paramount
Team has Rust experience
Complex algorithms/computations
Long-term project maintenance
Zero-cost abstractions needed

⚖️ Choose TinyGo When:

Faster development cycles preferred
Team familiar with Go ecosystem
Simpler deployment requirements
Rapid prototyping needed
Good-enough performance acceptable
Integration with Go services

🚨 Avoid When:

Rust: Steep learning curve unacceptable
Rust: Compilation time is bottleneck
TinyGo: Performance is critical
TinyGo: Memory constraints strict
Either: No WebAssembly experience
Either: Legacy system constraints

🎯 Primary Recommendation

Based on comprehensive performance analysis, **Rust is recommended** for WebAssembly applications requiring optimal performance. Rust demonstrates superior performance with 9 execution time wins and 4 memory efficiency wins across 9 benchmark scenarios.

🔧 Technical Implementation Notes

🖥️ Experimental Environment

Hardware: AWS EC2 c7g.2xlarge (4 CPU, 16GB RAM)

Operating System: Ubuntu 22.04 (Linux/x86_64)

Browser: Headless Chromium 140+ (Puppeteer 24+)

Language Toolchains:

Rust 1.89+ (stable) targeting wasm32-unknown-unknown
TinyGo 0.39+ + Go 1.25+ targeting WebAssembly
Node.js 22 LTS, Python 3.13+

⚙️ Build Configuration

Rust (Bare Interface):

Optimization: opt-level=3, lto="fat", codegen-units=1
Panic: abort, Strip: debuginfo
Post-processing: wasm-strip + wasm-opt -O3

TinyGo:

Build flags: -opt=2, -panic=trap, -no-debug, -scheduler=none, -gc=conservative
Post-processing: wasm-strip + wasm-opt -Oz

🎯 Benchmark Tasks

Mandelbrot: CPU floating-point intensive (256×256 to 1024×1024)

JSON Parsing: Structured data processing (5K to 30K records)

Matrix Multiplication: Dense numerical computation (256×256 to 576×576)

Verification: FNV-1a hash consistency across languages

📈 Scalability Analysis

Progressive GC pressure design across scales:

Small: No GC trigger (< 1MB usage)
Medium: Light GC trigger (2-4MB usage)
Large: Moderate GC trigger (6-10MB usage)

🚀 Deployment Considerations

Rust: Zero-cost abstractions, compile-time memory management, no GC overhead

TinyGo: Garbage collector with GC pauses and allocation overhead

⚠️ Limitations & Trade-offs

Rust: Steeper learning curve, longer compile times, complex dependency management

TinyGo: GC pauses affect latency-critical applications, limited Go standard library

🔬 Methodology & Validation

📊 Benchmark Design

Tasks: Mandelbrot, JSON parsing, matrix multiplication
Scales: Small, medium, large across all tasks
Environment: Headless Chromium with Puppeteer
Verification: FNV-1a hash consistency

🔄 Reproducibility & Environment

Environment Fingerprinting: Toolchain version locking
Deterministic Execution: Fixed random seeds
Hardware Consistency: Controlled test environment
Version Control: Git-based artifact tracking

📈 Data Collection

Warmup: 15 runs (discarded)
Measurement: 50 runs per configuration
Repetitions: 3 full cycles
Timing: performance.now() API

✅ Quality Control

CV Threshold: < 15% variation
Min Samples: 30 per dataset
Hash Verification: Cross-language consistency
Seed: Fixed random seed (42)

🧮 Statistical Analysis

Significance: Welch's t-test (p < 0.05)
Effect Size: Cohen's d (0.3/0.6/1.0 thresholds)
Confidence: 95% intervals
Outliers: IQR method (1.5×IQR)

🎯 Validation Mechanisms

Cross-Language Verification: Identical hash outputs
Memory Monitoring: Browser performance API
Execution Stability: Coefficient of variation checks
Result Integrity: SHA256 checksum validation

🦀 Rust vs 🐹 TinyGo WebAssembly