Node.js vs Bun: Choosing a Runtime for Web and Server Apps

What this comparison covers

This comparison evaluates Node.js and Bun as production runtimes for server-side JavaScript and TypeScript. A runtime executes application code outside the browser and supplies the facilities needed for files, networking, processes, cryptography, timers, modules, diagnostics, and operating system interaction.

The practical question is whether either runtime fits the application, dependencies, deployment target, and support expectations of your team. Node.js remains the established production default. Bun combines a runtime with a package manager, test runner, transpiler, and bundler in one executable.

The workloads covered here include:

• HTTP APIs using REST or GraphQL
• Server-rendered and hybrid web applications
• WebSocket and other long-lived connections
• Queue workers, scheduled tasks, and batch jobs
• Command-line programs and short-lived automation

Browser execution and isolated microbenchmarks are outside the main scope. A fast router test says little about an application that spends most of each request waiting for PostgreSQL, validating a large payload, calling another service, or rendering a component tree.

The comparison therefore concentrates on measurable runtime behavior, npm compatibility, TypeScript handling, framework support, operations, security, deployment, and migration risk. The right choice follows from those constraints, not from a universal winner.

Node.js and Bun today

Node.js offers the broadest compatibility and production history, while Bun offers tighter integration and often lower startup and tooling overhead. Both run JavaScript on servers, but their engines, APIs, release practices, and surrounding tools differ.

Runtime foundations

Node.js uses Google's V8 engine and libuv for its event loop and asynchronous operating system work. It has developed since 2009, so package authors, hosting providers, monitoring vendors, and operations teams usually treat its behavior as the reference for server-side JavaScript.

Bun uses JavaScriptCore, the engine associated with WebKit, and is implemented largely in Zig. Its runtime exposes Web APIs such as fetch, Request, and Response, implements many Node APIs, and adds Bun-specific facilities such as Bun.serve. The project describes complete Node compatibility as a goal, not a finished state.

The engine difference can affect garbage collection, startup, regular expression execution, object allocation, and optimization of hot functions. It does not mean one engine wins every workload. Code shape and dependencies can produce different results from a simple engine benchmark.

Supported Node.js release lines

Node.js 24 and Node.js 22 are supported LTS lines. Node.js 26 is the Current line and is scheduled to move into LTS in October 2026. Node.js 20 has reached end of life, so services still using it should move to a supported release instead of comparing an obsolete Node version with a current Bun release.

Production applications normally belong on an LTS release unless the team has a specific reason to validate the Current line. Starting with Node.js 27, the project is moving to one major release each year, and every major release will progress to LTS after its Current phase. That change preserves an explicit support window for production planning.

Bun follows a faster 1.x release cadence and does not use Node's LTS model. Pinning the exact Bun version is therefore important for reproducible builds and controlled upgrades.

Built-in tooling

The old description of Node.js as only a runtime is no longer complete. Node now includes stable fetch, a stable node:test test runner, watch features, an inspector, environment-file support, and direct execution of a limited set of TypeScript syntax. Teams can still choose npm, pnpm, Yarn, Vitest, Jest, esbuild, Vite, or webpack when those tools fit better.

Bun places more of the workflow behind one command. bun install, bun test, bun build, and bun run cover dependency installation, testing, bundling, script execution, TypeScript transpilation, and runtime execution. Each part can also be adopted independently. A Node production service can use Bun as its package manager without changing the runtime that executes the deployed application.

Performance: what to measure and why

Runtime performance should be judged with representative application work under controlled resource limits. Public benchmark charts can suggest a test, but they cannot predict the result for a particular framework, database driver, payload mix, or deployment platform.

Define the performance goal

A useful evaluation begins with one primary outcome:

• Lower p95 or p99 response latency for user-facing requests
• More completed requests or jobs per unit of compute
• Lower memory consumption at a fixed traffic level
• Faster startup for autoscaling, serverless, or command-line tasks
• Shorter dependency installation, test, or build time in CI

These goals are related but not interchangeable. A runtime can start faster while using more memory after warm-up. It can produce high throughput while showing worse tail latency during garbage collection. A faster package manager does not make a database-bound endpoint respond faster in production.

Separate runtime work from external waiting

The largest response-time component is often outside the JavaScript engine. Database queries, network calls, object storage, queue brokers, DNS, TLS handshakes, and cache misses can dominate an endpoint. Changing runtimes will have limited effect if 95 percent of request time is spent waiting for PostgreSQL.

CPU-heavy work deserves a separate benchmark. JSON transformation, template rendering, compression, cryptography, image metadata processing, and large validation schemas exercise the engine differently from I/O-heavy handlers. If CPU work blocks the event loop, compare worker-based or multi-process designs as well as single-process speed.

Profile before migrating. Event-loop delay, flame graphs, query timing, allocation data, and downstream service timing reveal whether the runtime is a meaningful part of the current bottleneck.

Build a fair benchmark

Run the same application code, dependency versions, data set, logging level, and database configuration wherever possible. Give each container the same CPU and memory limits. Do not compare an unrestricted local Bun process with a throttled Node container.

A practical service test can use two CPU cores and 1 GiB of memory per container, a three-minute warm-up, a ten-minute measured run, and five repetitions. Use a request mix based on production traffic rather than sending one trivial route continuously. Record medians across runs and retain individual results so intermittent pauses remain visible.

Collect no more than a focused set of signals:

• p50, p95, and p99 latency by endpoint class
• Successful throughput and error rate
• CPU time and event-loop delay
• RSS, heap use, and memory growth over time
• Startup time until the readiness check succeeds

Measure client-side latency from a separate load generator. A load test running on the same limited machine can consume the CPU needed by the service and distort the comparison. Confirm that the generator itself is not saturated.

Interpret the result

Bun often performs well on startup, package installation, built-in HTTP handling, and short scripts. Node may match or exceed it for code paths that V8 optimizes particularly well, and it can benefit from framework adapters refined over many releases. Neither pattern guarantees an application result.

Tail behavior matters more than a single average. Compare error rates, timeouts, garbage collection pauses, connection reuse, and memory after sustained load. A 15 percent throughput gain is unattractive if memory grows without settling or p99 latency breaches the service objective.

Set acceptance criteria before running the test. One example is a required 10 percent reduction in p95 latency with no increase in errors, no more than 5 percent additional RSS, and identical functional test results. Predefined thresholds prevent an appealing but unimportant metric from deciding the migration.

Compatibility with npm packages and Node APIs

Node.js supplies native compatibility with its own APIs, while Bun covers a large and growing portion that still requires application-level verification. Most pure JavaScript packages work in both, but the difficult cases occur in native modules, unusual module loading, process behavior, streams, and operational agents.

Packages that usually transfer cleanly

Libraries based on standard JavaScript, ESM or conventional CommonJS, Web APIs, and documented Node modules are the easiest candidates. Validation libraries, date utilities, HTTP clients, routing packages, and many framework components fall into this group.

Package installation is not proof of compatibility. A dependency may install successfully but fail only during a TLS reconnect, file watch event, worker shutdown, multipart upload, or uncommon error branch. Test the code paths the production service actually reaches.

Compatibility risks

The npm ecosystem contains several categories that deserve direct inspection:

• Native .node extensions and packages that compile platform code
• Install scripts that download binaries or generate artifacts
• Custom ESM loaders, CommonJS hooks, and conditional exports
• Direct use of streams, TLS, child processes, workers, or async context
• APM agents, profilers, error reporters, and test instrumentation

Bun implements Node-API and reports coverage of most of that interface, so many existing extensions load successfully. That is materially better than treating all native addons as unsupported. It is still necessary to test the exact addon version on every target operating system and processor architecture. Addons can depend on behavior outside the stable Node-API boundary or ship binaries only for environments their publisher supports.

Bun's compatibility documentation tracks individual built-in modules and sometimes records behavioral caveats even when broad support is present. An application that depends on a specific edge case should test that behavior directly instead of treating a module name as a binary supported or unsupported answer.

Module resolution and package metadata

ESM and CommonJS differences can surface in package exports, extension handling, dynamic imports, top-level await, and mixed module graphs. Both runtimes support ESM and CommonJS, but they can choose different branches of conditional exports or expose a packaging mistake in different ways.

Review package.json fields such as type, main, module, exports, and engines. Check whether important vendors explicitly list Bun support. An absent Bun entry does not prove failure, but it changes who owns diagnosis if production behavior differs.

Dependency audit procedure

Use a repeatable audit before changing the production runtime:

1. Inventory direct dependencies, transitive native packages, and lifecycle scripts.
2. Search application code for node: imports and Bun-specific globals.
3. Run unit, integration, contract, and end-to-end tests under the candidate runtime.
4. Exercise migrations, queues, uploads, TLS, process signals, and shutdown behavior.
5. Build the production image on every supported processor and operating system combination.

Record compatibility findings by package and version. A vague statement that the stack works on Bun becomes unhelpful after dependencies change. A small compatibility manifest gives future upgrades a concrete test list.

Tooling and workflow

Bun reduces the number of separate tools required for a common JavaScript workflow, while Node gives teams a wider choice of mature components. Tool consolidation can simplify maintenance, but only when the built-in behavior covers the repository's actual requirements.

Package management and lockfiles

Bun now writes the text-based bun.lock lockfile. The older binary bun.lockb format is obsolete for new projects and can be migrated. Bun can also migrate existing npm, pnpm, and Yarn lockfiles when introduced to a repository.

Do not keep two authoritative lockfiles changing independently. Select one package manager for automated installs, commit its lockfile, and enforce frozen installation in CI. Otherwise, developers may test dependency trees that differ from the deployed artifact.

Bun handles dependency lifecycle scripts differently from traditional npm workflows. It blocks arbitrary scripts unless the package is trusted, while maintaining a default trusted set for common packages. This reduces unsolicited code execution during installation, but it can also leave a native binary or generated client missing until the dependency is approved. Inspect blocked scripts instead of assuming the install completed every package-specific setup step.

Tests

Node's stable node:test runner supports asynchronous tests, mocking facilities, coverage collection, test isolation, and multiple reporters. Established projects may still prefer Jest or Vitest for mature plugin ecosystems, snapshot behavior, browser simulation, and familiar developer workflows.

bun test offers a Jest-like interface, TypeScript support, snapshots, watch mode, coverage, and lifecycle hooks. Compatibility with common Jest assertions does not guarantee compatibility with every Jest transformer, custom environment, timer mock, or module mock. Port one representative test directory before estimating the work for the full suite.

Do not change the runtime, package manager, test runner, and assertion library in one migration. When failures appear, simultaneous substitutions make the cause much harder to isolate.

Bundling and script execution

bun build can bundle JavaScript, TypeScript, JSX, CSS, browser targets, server targets, and standalone executables. It can replace several build dependencies in a straightforward project. Existing Vite, esbuild, Rollup, or webpack configurations may still contain plugins and asset rules that are expensive to reproduce.

Node executes package.json scripts through the selected package manager and can run applications without a server bundle. Many backend services gain little from bundling unless deployment size, startup, dependency isolation, or source distribution creates a specific need.

A low-risk adoption sequence

Adopt Bun's tools independently when that keeps evaluation clear:

1. Measure bun install against the current package manager without changing production execution.
2. Verify that bun.lock produces reproducible dependency trees in CI.
3. Run existing package scripts with Bun and compare their outputs.
4. Port a representative test group to bun test if fewer test dependencies would help.
5. Change the deployed runtime only after application compatibility and operations pass.

This sequence lets a team keep Node in production while taking advantage of Bun where the benefit is already measurable.

TypeScript, builds, and debugging

Both runtimes can execute TypeScript files, but neither replaces static type checking. Their direct execution models also differ enough that a successful development command is not sufficient evidence for a production build.

Node.js TypeScript support

Current supported Node releases can execute TypeScript containing erasable syntax. Node removes annotations at runtime without type checking, and Node 24 provides this type-stripping behavior as a stable feature.

The built-in mode intentionally ignores tsconfig.json. It does not apply path aliases, target conversion, JSX configuration, or other compiler options. TypeScript constructs that require JavaScript generation rather than simple removal need a transform step or a third-party runner. This makes direct Node execution useful for scripts and compatible source files, but it is not a complete substitute for tsc, tsx, or a bundler.

Bun TypeScript support

Bun transpiles .ts, .tsx, JSX, and related files before execution. It supports a broader direct-execution experience than Node's type stripping, especially for projects already using Bun's loader and bundler.

Bun also does not type-check application code merely because it can run the file. Keep tsc in CI with emission disabled when type errors must block a release. Runtime transpilation and static verification solve different problems.

Production build choices

Compile-to-JavaScript remains a sensible production default when portability and artifact inspection matter. It produces an explicit deployable result, catches unsupported compiler assumptions before startup, and allows the same artifact to be tested before release.

Direct TypeScript execution can be appropriate for internal tools, controlled Bun services, development servers, or small applications where a separate artifact adds little value. If production runs source TypeScript, pin the runtime and confirm that source maps, stack traces, dependency loading, and startup failures behave correctly inside the real container.

A runtime switch should not silently change module format or TypeScript semantics. Keep the same tsconfig.json, module targets, strictness settings, and type-check command during the first comparison. Optimize the build only after runtime equivalence is established.

Debugging and diagnostics

Node has mature inspector support and broad integration with editors, profilers, APM products, and error-reporting services. Bun supports interactive debugging and source maps, but vendor support and edge behavior vary by tool.

Validate the complete debugging chain:

• Breakpoints bind to the expected TypeScript lines.
• Production stack traces identify the original source.
• Unhandled rejections and uncaught exceptions reach error reporting.
• Async context preserves trace and request identifiers.
• CPU and memory profiles can be captured during an incident.

A runtime that performs well but cannot supply usable incident data may increase recovery time enough to erase the operational benefit.

Web framework support and application patterns

Frameworks built on documented Node APIs or standard Web request objects are generally the easiest to run under either runtime. Compatibility becomes harder when plugins depend on native code, Node internals, custom loaders, or precise stream behavior.

Common framework families

Express applications often transfer with little code change because Bun implements the Node HTTP interfaces they commonly use. Middleware involving uploads, compression, sessions, proxies, or unusual streaming deserves integration coverage.

Fastify applications rely on a larger plugin and schema ecosystem. The framework may start cleanly while a logger transport, serializer, or plugin exposes a difference. Benchmark Fastify through the same adapter and configuration used in production.

Hono and other frameworks centered on Request, Response, and fetch reduce runtime coupling. Their standard interface can make it easier to compare a Node adapter with Bun's native server facilities without rewriting business logic.

Nest applications often bring dependency injection, decorators, adapters, metadata reflection, database integrations, and a large dependency graph. Test the complete application rather than judging support from a minimal controller.

Server-rendered frameworks require version-specific testing. Development mode, production builds, image processing, middleware, server actions, caching, and deployment adapters do not necessarily use the same runtime facilities. A framework's development server working under Bun does not prove that every production feature does.

Native Bun APIs versus portability

Bun.serve can deliver excellent startup and HTTP performance with a small amount of code. Using it also makes the server entry point Bun-specific. That trade can be reasonable when the team has deliberately selected Bun and maintains a thin adapter around the application.

Keep domain logic independent from the runtime boundary:

• Accept plain application inputs instead of runtime request objects deep in the codebase.
• Isolate server startup, signal handling, and connection configuration.
• Wrap file, queue, and process integrations behind small interfaces.
• Keep framework adapters covered by contract tests.

This structure permits a Node HTTP adapter and a Bun adapter to share business behavior. It also reduces migration work if deployment requirements change later.

Server operations: startup, memory, and concurrency

Bun often has an advantage for process startup, while Node has the deeper collection of established operational practices and vendor integrations. Long-running reliability still depends on load shape, memory behavior, shutdown handling, and external services.

Startup and readiness

Measure startup until the service is genuinely ready, not merely until the process begins. Database pools, schema validation, configuration loading, secret retrieval, module initialization, and cache warming may dominate runtime boot time.

For serverless and rapidly autoscaled containers, even tens of milliseconds can matter when instances start frequently. For a continuously running API, startup speed is usually secondary to latency stability, memory growth, and predictable deployment behavior.

Readiness checks should remain false until required connections and initialization steps have completed. A faster process that accepts traffic before it can serve requests creates avoidable errors during rollout.

Memory behavior

Compare resident memory after warm-up and during a sustained test. Heap size alone omits native allocations, loaded libraries, buffers, allocator behavior, and memory mapped by the runtime.

Watch these operational signals:

• RSS at idle, normal load, and peak load
• Heap growth after repeated traffic cycles
• Garbage collection pause duration
• Event-loop delay during allocation pressure
• Memory returned or retained after traffic falls

Set container limits during testing. An unrestricted process can hide pressure that produces termination or heavy garbage collection under production quotas.

Concurrency and CPU work

JavaScript request handlers normally execute on one main thread per process, even though the runtime performs many I/O operations concurrently. CPU-bound work blocks other handlers unless it is divided among workers, separate processes, or an external service.

Node provides worker threads and mature multi-process patterns. Bun supports Web Worker-style concurrency and process APIs, but existing worker libraries may assume Node details. Test message transfer, termination, error propagation, and memory overhead before relying on identical behavior.

Running one process per allocated CPU is a reasonable starting point, not a law. Measure because shared caches, connection pools, garbage collectors, and scheduler overhead can make fewer or more processes perform better.

Jobs, queues, and shutdown

Queue reliability depends more on acknowledgement, retry, idempotency, and visibility-timeout design than on the runtime. Bun candidates still need tests for broker reconnects, TLS, stalled jobs, duplicate delivery, and process termination.

A production process should stop accepting new work after a termination signal, finish or return in-flight work within a deadline, close listeners, flush telemetry, and exit. Test forced termination after the deadline too. Shutdown bugs usually appear during deployments and autoscaling, not during local development.

Keep sessions, durable job state, and uploads outside the process. Disposable instances make horizontal scaling and rollback safer under either runtime.

Stability and security considerations

Node.js offers clearer long-term support conventions, while Bun requires more frequent version validation and closer attention to compatibility changes. Security for either runtime also depends heavily on dependency installation, patch timing, and artifact control.

Release and upgrade policy

Use supported Node LTS releases for production and schedule minor updates promptly. Test major upgrades against native modules, framework adapters, observability, and changes in runtime defaults.

Pin Bun to an exact version in development images, CI, and production. A fast release cadence can deliver fixes quickly, but automatic adoption makes regressions harder to attribute. Promote a new version through the same test and canary process used for application changes.

A sensible runtime policy includes:

• An owner who tracks runtime releases and security notices
• A defined maximum delay for security patches
• Automated compatibility and application tests
• Versioned immutable deployment artifacts
• A documented route back to the previous working image

Do not use an end-of-life Node release because it appears stable. Lack of change after support ends also means lack of project security fixes.

Dependency and installation security

Commit one lockfile, review unexpected dependency changes, and build from a clean environment. An audit command can identify known advisories, but it cannot detect malicious unpublished behavior, compromised maintainer accounts, or unsafe application configuration.

Bun provides bun audit for packages recorded in bun.lock. Its restricted lifecycle-script model creates a useful approval boundary, provided the team reviews packages before adding them to trustedDependencies. npm users can disable scripts in sensitive build stages and allow required compilation in a controlled stage.

Apply these supply-chain controls:

• Restrict who can change runtime versions and lockfiles.
• Review newly introduced install scripts and native binaries.
• Generate a software bill of materials for released artifacts.
• Scan the final container as well as source dependencies.
• Rebuild and redeploy when the runtime or base image receives a fix.

Runtime choice does not replace application protections such as input validation, authorization, secret management, secure cookies, rate limits, and least-privilege infrastructure.

Deployment and observability checklist

Both runtimes can run effectively in containers and on supported hosting platforms, but the exact deployment target must support the chosen executable, architecture, system libraries, and monitoring stack. Local success is only the first validation stage.

Environment parity

Pin runtime and package manager versions in the repository and build image. Install from the committed lockfile, use the same module and environment configuration in staging, and reproduce production CPU and memory limits.

Confirm these environment details:

• Processor architecture and operating system match supported runtime builds.
• Native dependencies compile or download the expected binary.
• Temporary storage and working-directory assumptions are valid.
• Certificate stores, DNS, proxies, and outbound TLS behave correctly.
• Process signals and container health checks reach the application.

Container base images for Node are available across many vendors and environments. Bun publishes its own deployment options, but third-party platforms may still assume Node. Serverless services can require a custom runtime or container for Bun, so support must be verified before application work begins.

Edge platforms are a separate category. Many expose a restricted Web API environment rather than a full Node or Bun process. Code that runs in Node or Bun locally may still use unavailable filesystem, socket, process, or native addon features at the edge.

Logging, metrics, and traces

Structured logs should retain timestamps, severity, request identifiers, and error details without blocking the event loop. Confirm that log flushing works during graceful shutdown and that high log volume does not dominate benchmark results.

Metrics need to expose request duration, error counts, event-loop delay, memory, process restarts, queue depth, and downstream timing appropriate to the service. Compare metric correctness as well as collection overhead.

Tracing requires context to survive promises, framework middleware, database calls, queue publication, and background work. Node integrations have a long production history. Bun support varies across telemetry libraries and commercial agents, so run a trace through every important boundary and inspect the resulting spans.

Production rollout checks

Before shifting traffic, verify:

• Functional parity for API responses, jobs, migrations, and scheduled work
• Stable latency and memory during a production-length load test
• Correct readiness, liveness, timeout, and shutdown behavior
• Complete logs, traces, source maps, alerts, and error reports
• Canary routing with automatic or operator-controlled rollback

Keep the deployment shape constant during the first runtime comparison. The same environment variables, resource limits, entry behavior, and service dependencies make differences easier to attribute.

Which runtime should you choose?

Choose Node.js when compatibility, vendor support, and predictable maintenance outweigh tooling speed; choose Bun when controlled dependencies and integrated tools produce a measured benefit. Pilot both when the evidence is incomplete or the application contains uncertain integrations.

Existing Node.js applications

Stay on Node.js by default when the service is stable, dependency-heavy, and already meets its cost and performance objectives. A migration with no defined target creates work without proving user or business value.

Bun can still help without replacing production Node. Trial its package manager on a branch, use it for an isolated script, or test a small stateless worker. This reveals lockfile, lifecycle-script, and dependency issues before the main service is exposed.

A runtime migration becomes reasonable when profiling identifies engine or startup overhead, infrastructure cost is material, and a representative Bun deployment meets predefined acceptance criteria.

New services

Bun is a credible starting point for a greenfield HTTP service when dependencies are mainstream, the deployment platform supports it directly, and the team is willing to validate upgrades. Using Web API request objects and isolating Bun-specific code preserves an exit route.

Node.js remains a strong default when engineers need the broadest selection of APM agents, authentication SDKs, database integrations, deployment examples, and experienced operators. Its larger ecosystem can save more engineering time than a faster install or startup.

The choice does not need to apply to every repository. A company can standardize Node for customer-facing services while using Bun for internal tools, or adopt Bun for new isolated services while keeping legacy Node systems unchanged. Define ownership and support expectations for each runtime to avoid accidental fragmentation.

Long-term maintenance

Count operational effort as part of runtime cost. Include version testing, incident diagnosis, vendor support, security response, onboarding, CI minutes, compute use, and the number of runtime-specific workarounds maintained in application code.

If two runtimes perform similarly, choose the one the team can operate with less risk. If Bun produces a substantial measured improvement, document the compatibility evidence and the conditions under which the decision should be reviewed.

How to evaluate and migrate with low risk

A safe runtime evaluation changes one controlled slice, proves functional equivalence, measures production-relevant behavior, and preserves an immediate rollback. Treat it as an engineering experiment rather than a rewrite.

1. Choose a representative pilot

Select a stateless service, read-only endpoint group, command-line task, or queue consumer with realistic dependencies. Avoid beginning with payment processing, authentication, large file uploads, or a service whose failures are difficult to reverse.

The pilot must be representative enough to expose genuine compatibility issues. A hello-world server proves only that the runtime starts. Include the real framework, database client, validation, logging, configuration, and telemetry used by the target service.

2. Establish a Node baseline

Upgrade the comparison service to a supported Node LTS release before measuring. Fix failing tests, remove obsolete dependencies, and record current operational results. Otherwise, the experiment may credit Bun for improvements caused by moving away from an old Node version or cleaning the application.

Capture the build duration, artifact size, startup readiness, load-test results, idle memory, sustained memory, error rate, and deployment behavior. Store raw results with hardware and configuration details.

3. Change only the runtime

Run the same code under Bun before adopting Bun-specific server APIs or replacing build tools. Compatibility failures at this stage identify the true runtime boundary.

Resolve issues with small adapters where practical. Avoid broad rewrites that make performance and reliability comparisons invalid. If an important dependency requires an unsupported behavior, record it as a migration blocker rather than hiding it behind an unmaintainable patch.

4. Validate real failure modes

Test database outages, queue disconnects, DNS failures, invalid certificates, slow downstream responses, memory pressure, termination during active work, and repeated restarts. Confirm that retries do not multiply requests and that shutdown does not lose acknowledged jobs.

Run the production observability stack during these tests. The pilot has not reached parity if the service works but traces disappear, source maps point to the wrong code, or the monitoring agent cannot report runtime failures.

5. Canary and decide

Deploy an immutable Bun artifact beside the Node artifact and send a small traffic percentage to it. Compare the predefined acceptance criteria over a period long enough to include normal load variation, scheduled work, and deployment cycles.

Proceed only if the measured benefit justifies the added support surface. Keep the Node artifact available until the Bun deployment has survived normal traffic, failures, upgrades, and at least one routine release cycle.

For teams using Koder.ai, planning mode can record the pilot requirements and acceptance criteria before implementation. Source export allows the resulting project to enter the team's normal review and CI process, while snapshots and rollback provide recovery points during changes. Koder.ai's primary backend technology is Go, so a Node.js versus Bun test applies to a separate or exported JavaScript service rather than the platform's Go service layer.

Document the final decision with runtime version, supported dependencies, benchmark configuration, known differences, rollback procedure, and conditions that trigger another review. That record turns a one-time experiment into maintainable production policy.