Node.js 22 vs Bun vs Deno trade‑offs — Cost Optimization

2. Measuring Memory Footprint & CPU Usage

Resource consumption translates directly into cost on platforms billing by resource utilisation.

Example: Running a simple HTTP server that responds with JSON:

// Node.js 22: server.js
import http from 'http';

const server = http.createServer((req, res) => {
  res.writeHead(200, {'Content-Type': 'application/json'});
  res.end(JSON.stringify({status: 'ok'}));
});

server.listen(3000, () => console.log('Node.js server running on port 3000'));

// Deno 1.35+: server.ts
import { serve } from "https://deno.land/std@0.203.0/http/server.ts";

serve((_req) => new Response(JSON.stringify({ status: "ok" }), {
  headers: { "Content-Type": "application/json" },
}), { port: 3000 });

// Bun v1.x: server.ts
import { serve } from "bun";

serve({
  port: 3000,
  fetch() {
    return new Response(JSON.stringify({ status: "ok" }), {
      headers: { "Content-Type": "application/json" },
    });
  },
});

console.log("Bun server running on port 3000");

3. Benchmarking

Use lightweight benchmarking tools (e.g., autocannon, wrk) or cloud provider metrics to measure CPU time and memory per request under load.

Track cold start time using provider metrics or by deploying minimal serverless functions. Node.js may require optimisations such as

--enable-source-maps

disabled or snapshotting.

Common pitfalls

• Module compatibility: Bun, while fast, can struggle with some Node native modules and native bindings affecting production stability and thus unexpected costs for debugging and fixing in Bun.
• Security model differences: Deno’s default sandboxing requires explicit permission flags (--allow-net, --allow-read), which can complicate deployment scripts or inflate cold start times if misconfigured.
• Tooling and ecosystem maturity: Node’s ecosystem is the most mature; Bun and Deno may require additional setup or remain incompatible with some logging, monitoring, or tracing providers, impacting observability and cost management.
• Startup script size: Large dependencies increase cold start costs in all environments; both Bun and Deno’s bundled transpilation may add overhead.

Validation

To validate cost optimisation, integrate profiling and monitoring tools:

• Node.js: Use --inspect, clinic.js, or built-in perf_hooks to measure CPU and memory usage.
• Deno: Use deno profiler and runtime diagnostics (--inspect) to trace performance.
• Bun: Currently offers runtime stats through bun --stats and built-in inspector compatibility; still maturing.

Run automated load tests mimicking your workload on your preferred cloud infrastructure to collect real-world execution metrics.

Checklist / TL;DR

• Choose Node.js 22 if: You prioritise ecosystem stability, wide library support, and mature monitoring, and are less sensitive to startup overhead.
• Choose Bun if: You value minimal cold start latency, bundled tooling, and lower memory usage but can invest in validating your dependencies and ecosystem readiness.
• Choose Deno if: You need built-in TypeScript support, enhanced security by default, and are willing to manage permission granularity despite a small startup penalty.
• Measure and profile in your exact deployment environment — cloud instance types, serverless platforms (AWS Lambda, Cloud Run, Azure Functions), or edge runtimes will influence cost impact.
• Test cold start times especially if using serverless or ephemeral compute; small differences can amplify billing.
• Consider development and operational costs alongside runtime — ecosystem maturity often reduces hidden costs.

References

• Node.js 22 Release Notes
• Bun Official Documentation
• Deno Manual – Official
• Node.js Profiling Guide
• Deno v1.35 Release Blog
• Bun Performance and Ecosystem Updates

#NodeJS #Bun #Deno #JavaScriptRuntime #CostOptimisation #Serverless #CloudComputing