Bun in I/O-light, HTTP-heavy scenarios, while database-bound and CPU-bound workloads show diminishing returns.

Approach	Req/sec (HTTP)	p99 Latency (HTTP)	CPU Job Time (1k items)
Bun 1.1.38 (Zig)	41,200	8.1ms	4.312s
Node.js 22.6 (V8)	29,800	11.4ms	4.891s

Key Findings:

• Pure HTTP workloads show a ~38% throughput advantage for Bun, driven by JSC and a streamlined HTTP stack.
• PostgreSQL-bound queries narrow the gap to ~5.6%, proving that database I/O and connection pooling dominate runtime overhead.
• CPU-bound job processing yields a ~12% improvement, confirming that algorithmic execution benefits from JSC but doesn't scale to order-of-magnitude gains.
• The "sweet spot" for Bun adoption is HTTP-heavy, low-complexity business logic. The language rewrite will not materially shift these ratios.

Core Solution

The technical path forward requires shifting focus from language debates to architectural benchmarking and workload-specific validation. Performance gains in Bun stem from JavaScriptCore (JSC) replacing V8, a built-in HTTP server that bypasses libuv, and native TypeScript execution without transpilation. These architectural decisions remain constant regardless of whether the runtime is implemented in Zig or Rust.

Validate performance using your actual production stack rather than isolated micro-benchmarks. The following benchmark suites demonstrate how to measure real-world impact:

# Suite 1: HTTP simple (sin lógica de negocio)
# Mido requests/s con autocannon, 10s, 100 conexiones concurrentes

autocannon -c 100 -d 10 http://localhost:3000/api/health

# Bun 1.1.38:
# Req/sec: 41.200
# Latencia p99: 8.1ms

# Node.js 22.6:
# Req/sec: 29.800
# Latencia p99: 11.4ms

# Suite 2: Query PostgreSQL simple (SELECT por PK, pool de 10 conexiones)
# Mismo endpoint, lógica real de DB

# Bun 1.1.38:
# Req/sec: 9.400
# Latencia p99: 31ms

# Node.js 22.6:
# Req/sec: 8.900
# Latencia p99: 33ms

# Suite 3: Worker de procesamiento de jobs (CPU-bound, sin I/O)
# Proceso 1000 items, mido tiempo wall

time bun run scripts/process-jobs.ts
# real: 0m4.312s

time node --experimental-strip-types scripts/process-jobs.ts
# real: 0m4.891s

Architecture Decisions:

• Prioritize JSC vs. V8 trade-offs based on your specific I/O/CPU ratio.
• Leverage Bun's native bundler and TypeScript support to reduce build friction in agent-driven or rapid-iteration workflows.
• Treat the runtime language as an internal sustainability concern (team velocity, contributor pool, tooling maturity) rather than a user-facing performance lever.

Pitfall Guide

1. Native Module Compatibility Gaps: Bun's N-API implementation diverges from Node.js in edge cases. Modules relying on V8-specific internals or libuv assumptions may fail or behave unpredictably. Validate critical native dependencies before production rollout.
2. Ecosystem Lock-in via Bun-Specific APIs: Adopting Bun.file(), Bun.serve(), or Bun.$ creates architectural dependency on Bun's proprietary surface. The Zig-to-Rust migration does not alter this lock-in; it remains a deliberate trade-off for convenience.
3. Overestimating Cold Start Impact: Bun's ~2x faster startup is valuable for Lambda/Edge functions but irrelevant in persistent container environments (Railway, ECS, K8s). In long-running processes, startup time never impacts request latency or throughput.
4. Relying on Synthetic Benchmarks: "Hello world" or isolated HTTP tests ignore real-world overhead from ORMs, connection pooling, and business logic. Always benchmark your actual stack with representative data volumes and concurrency levels.
5. Assuming Language Rewrite Equals Performance Gain: Rust improves memory safety, team velocity, and ecosystem tooling, but does not directly translate to higher req/sec. Performance differentials are architectural, not linguistic. Expect iteration speed improvements for the Bun team, not runtime magic for end users.

Deliverables

• Runtime Migration Evaluation Blueprint: A step-by-step framework for assessing runtime changes without getting distracted by language debates. Includes workload profiling templates, dependency compatibility matrices, and architectural decision trees for JSC vs. V8 adoption.
• Production Adoption Checklist: Covers native module validation, API lock-in assessment, connection pool tuning, monitoring setup, and rollback procedures. Designed for teams running Next.js + PostgreSQL + job workers in persistent container environments.
• Benchmark Configuration Templates: Pre-configured autocannon commands, time-based CPU job scripts, and PostgreSQL pool settings for reproducible performance testing across Bun and Node.js runtimes.