Bun Production Deployment: AI-Optimized Technical Reference

Platform Selection Matrix

Platform	Cold Start	Memory Usage	Best Use Case	Setup Complexity	Critical Limitations
Docker Container	N/A (always warm)	~96MB base	Long-running services, microservices	Medium	File watching breaks in containers
AWS Lambda	~31ms (vs Node.js ~127ms)	Pay per invocation	Event-driven functions	Low	Custom runtime layer required
Vercel	~50ms	Automatic scaling	Frontend apps, API routes	Very Low	Some Node.js APIs unavailable in edge runtime
Google Cloud Run	~100ms	128MB-8GB configurable	Containerized microservices	Medium	Best serverless option for Bun
Fly.io	~50ms globally	256MB-8GB configurable	Global edge deployment	Low	Excellent global distribution

Docker Production Configuration

Multi-Stage Build (Recommended)

FROM oven/bun:1.2.21-alpine AS builder
WORKDIR /app
COPY bun.lockb package.json ./
RUN bun install --frozen-lockfile
COPY . .
RUN bun build --compile --minify ./src/index.ts --outfile server

FROM gcr.io/distroless/cc-debian12:nonroot
WORKDIR /app
COPY --from=builder /app/server ./
EXPOSE 3000
ENTRYPOINT ["./server"]

Resource Requirements:

• Final image size: ~50MB (vs ~200MB+ with full runtime)
• Memory usage: 30-50MB typical (vs 50-80MB Node.js equivalent)
• Container startup: 2-3x faster than Node.js

Critical Failure Points:

• Native modules break compilation → Use runtime image fallback
• Alpine vs Debian compatibility issues → Test exact build on target platform
• Platform architecture mismatches → Use --platform linux/amd64 for M1 Mac builds

Production Breaking Points

• Memory leaks in long-running containers - Requires periodic restarts
• Environment variables load differently - Can cause 30-minute outages if config isn't read properly
• Process monitoring tools don't recognize Bun processes - Custom monitoring needed
• Auto-restart scripts written for Node.js - May not handle Bun's exit codes correctly

Serverless Deployment

AWS Lambda Performance

• Cold start advantage: 80ms vs 300ms+ Node.js equivalent
• Setup complexity: High - requires custom runtime layer via bun-lambda
• Cost efficiency: Very high for intermittent workloads
• Breaking point: 15MB deployment package limit can be hit with large dependencies

Google Cloud Run (Recommended Serverless)

• Advantages: Full container control, no Lambda runtime limitations
• Performance: Fast cold starts due to Bun's startup speed
• Scaling: Scales to zero, spins up fast
• Cost model: More predictable than Lambda for consistent traffic

Platform-Specific Gotchas

• Vercel: Node.js API limitations in edge runtime, test thoroughly
• Railway: Good for side projects, free tier sleeps after inactivity
• Netlify: 10-second timeout kills long-running tasks
• Fly.io: Transparent pricing, excellent for global low-latency APIs

Production Failure Modes

Environment Configuration

// Fail-fast config validation
const requiredEnvs = ['DATABASE_URL', 'REDIS_URL', 'JWT_SECRET'];
for (const env of requiredEnvs) {
  if (!process.env[env]) {
    console.error(`Missing required environment variable: ${env}`);
    process.exit(1);
  }
}

Database Connection Issues

Root Cause: Default pool sizes too small for production load
Solution: Increase pool size to 50+ connections, add connection timeout handling

const pool = new Pool({
  max: 50, // Increase from default 10
  connectionTimeoutMillis: 5000, // Fail fast instead of hanging
});

Memory Monitoring (Critical)

Monitor these metrics:

• Response time (95th percentile, not average)
• Error rate (4xx and 5xx responses)
• Memory usage (RSS and heap)
• Database connection pool utilization

Memory leak indicators:

• Unclosed database connections
• Event listeners not removed
• Large objects in global variables
• Uncleared timers

Performance Optimization

Bundle Analysis

bun build --analyze --minify ./src/index.ts --outdir ./dist

Tree-shaking works properly - Unlike webpack where importing lodash gets entire library

Load Testing Thresholds

• Concurrent connections: 20-50% more than Node.js on same hardware
• P95 response time: Target <100ms
• P99 response time: Target <200ms
• Memory growth: Set alerts at 500MB heap usage

Error Handling (Production Critical)

// Global error handlers prevent silent failures
process.on('uncaughtException', (error) => {
  // Log structured error data
  console.error(JSON.stringify({
    type: 'uncaught_exception',
    timestamp: new Date().toISOString(),
    error: { message: error.message, stack: error.stack }
  }));
  process.exit(1);
});

Security Requirements

Essential Security Measures

• Rate limiting: Use Redis-based rate limiting (100 requests/60 seconds typical)
• Input validation: Zod for schema validation before database
• Container security: Run as non-root, no shell access in distroless images
• Resource limits: Always set memory/CPU limits in orchestrator
• HTTPS: Let's Encrypt for automated SSL

Container Security

# Kubernetes resource limits (required)
resources:
  requests:
    memory: "64Mi"
    cpu: "50m"
  limits:
    memory: "128Mi"
    cpu: "200m"

Advanced Production Patterns

WebSocket Scaling

• Bun native WebSocket support - Superior to Node.js implementations
• Connection management - Implement room-based subscriptions
• Scaling consideration - Use Redis for multi-instance pub/sub

Background Job Processing

// Redis-based job queue with retry logic
emailQueue.add('send-email', { to, subject, body }, {
  attempts: 3,
  backoff: 'exponential',
  delay: 5000,
  removeOnComplete: 100,
  removeOnFail: 50
});

Caching Strategy (Multi-Layer)

1. L1: In-memory (LRU cache, 5-minute TTL)
2. L2: Redis (Distributed cache, 5-minute TTL)
3. L3: Compute (Database/API calls)

Critical Production Checklist

Pre-Deployment

• Health check endpoint with dependency validation
• Graceful shutdown handling (SIGTERM)
• Memory usage monitoring
• Error logging with structured JSON
• Rate limiting implementation
• Resource limits configured

Post-Deployment Monitoring

• Cold start metrics tracking
• Database connection pool utilization
• Memory leak detection
• Error rate alerting
• Performance regression detection

Breaking Change Indicators

• Sustained memory growth - Restart containers as temporary fix
• Database connection timeouts - Increase pool size or connection limits
• High error rates - Check environment variable loading
• Slow cold starts - Bundle size analysis needed

Resource Requirements

Minimum Production Setup

• Memory: 128MB container minimum, 512MB recommended
• CPU: 0.1 vCPU minimum, 0.5 vCPU recommended
• Storage: 1GB for logs and temporary files
• Network: Load balancer with health checks

Scaling Thresholds

• Horizontal scaling trigger: CPU >70% or memory >80%
• Database connections: Monitor pool utilization >80%
• Memory alerts: Heap usage >500MB indicates potential leak
• Response time alerts: P95 >200ms indicates performance degradation

Cost Optimization

Serverless vs Container Decision Matrix

• High traffic (>1000 req/min): Container deployment more cost-effective
• Sporadic traffic (<100 req/hour): Lambda pay-per-request wins
• Global distribution needed: Fly.io or Cloudflare Workers
• Enterprise requirements: Google Cloud Run or Azure Container Apps