Bun vs Node.js: Six Months Running Both in Production
We moved one internal service to Bun as a low-risk trial six months ago, while leaving the rest of our stack on Node.js, specifically to answer whether the migration was worth doing more broadly rather than trusting benchmark posts. Running both side by side in production for that long surfaced a more nuanced picture than either the "Bun is just faster" or "Bun isn't ready" camps suggest.
Where Bun's Speed Claims Actually Held Up
Bun's built-from-scratch runtime, using JavaScriptCore instead of V8, genuinely delivers on install speed and cold start time, the two areas where the difference was most visible to us day to day. Dependency installation on our monorepo dropped from a multi-minute npm install to single-digit seconds with bun install, which mattered far more for CI pipeline time and developer experience than any raw request-throughput number.
Where the Throughput Gap Shrank in Practice
Synthetic HTTP benchmarks showing Bun several times faster than Node don't hold up once real middleware, database calls, and validation logic enter the picture. On our actual service, doing real database queries and JSON validation per request rather than just echoing a response, the gap between Bun and Node's throughput was real but modest, nowhere near the multiples that pure hello-world benchmarks advertise. The database round trip dominates the request time either way.
Native Addon Compatibility Was the Real Blocker
Bun implements the large majority of the Node.js API surface at this point, but native addons compiled against V8's internals, like bcrypt's compiled bindings, simply don't work, since Bun runs on JavaScriptCore instead. Swapping bcrypt for a pure-JavaScript alternative or Bun's built-in password hashing API was a small, manageable change on the trial service; on our larger monolith with more native dependencies, it would have been a bigger undertaking than we were ready to sign up for.
Built-in Tooling Reduced Our Dependency Count
Bun bundles a test runner, a bundler, and a SQLite client directly into the runtime, which let us drop Jest and a separate bundler from the trial service's package.json entirely. Fewer dependencies meant fewer version mismatches to track, though it also meant leaning on tooling with a shorter production track record than the Node.js ecosystem's more battle-tested equivalents.
The Windows Question, If It Applies to You
Our production environment is entirely Linux containers, so Windows stability wasn't a factor for us, but for teams with Windows-based developer machines or Windows-hosted services, this is worth checking directly against Bun's current release notes before assuming parity with its Linux and macOS support.
What We Didn't Migrate, and Why
We deliberately kept the trial scoped to one low-traffic internal service rather than our customer-facing API. The main reason wasn't performance, it was operational maturity: our on-call runbooks, monitoring integrations, and years of accumulated tribal knowledge about Node-specific failure modes don't transfer automatically, and re-establishing that operational confidence for a new runtime on a service that pages people at 3am felt like the wrong place to take on that risk first.
Hybrid Approach: Bun for Tooling, Node for Runtime
The pattern that ended up delivering the most value with the least risk was using bun install and bun test locally and in CI, for the install-speed and test-speed wins, while still shipping the actual production build to run on Node.js. This got us a meaningful chunk of Bun's developer-experience benefit without betting the runtime itself on a newer, less operationally proven system.
Memory Footprint Under Sustained Load
Beyond cold start and throughput, we tracked memory usage on the trial service over several weeks of real traffic rather than a short synthetic burst. Bun's process held a noticeably smaller resident memory footprint than the equivalent Node service under comparable load, which mattered more for our container density, how many service instances fit on a given node, than for latency, and it's a dimension the popular benchmark posts rarely cover in favor of raw request-per-second numbers.
The Ecosystem Trust Question, Honestly
Node's ecosystem maturity isn't just about API compatibility, it's about the accumulated years of Stack Overflow answers, battle-tested error handling patterns, and postmortems written about specific failure modes. Hitting an obscure Bun-specific bug meant searching a much smaller pool of prior discussion than an equivalent Node issue would have, and twice that meant filing a GitHub issue and waiting rather than finding an existing answer, a real cost that doesn't show up in any benchmark.
What Would Change My Mind About the Production Runtime
I'm not opposed to running Bun in production for the customer-facing API eventually, the trial genuinely went well. What's actually holding that back is wanting to see a couple more major Bun releases pass without a serious regression reported against the specific native dependencies our larger service relies on, and wanting our own on-call team to build up incident-response familiarity with the runtime on lower-stakes services first, rather than any specific technical blocker we've hit so far.
Final Verdict
Six months in, Bun earned a permanent place in our tooling, install speed and the built-in test runner are genuine, everyday improvements, but it hasn't replaced Node.js as our production runtime, and I don't think it needs to in order to be worth adopting. For a greenfield service without native addon baggage, running Bun as the runtime directly is a reasonable default in 2026; for an established Node codebase, starting with the tooling layer is the lower-risk path to the same benefits.
