Why Postman Is Slow and Bloated in 2026 (And What to Use Instead)

TL;DR

Postman is an Electron app built on Chromium, and in 2026 that architecture has a visible cost: startup often takes 5–8+ seconds, memory usage can exceed 500 MB with a few collections open, and the app ships a full browser engine to send HTTP requests. This guide explains where that overhead comes from, how to measure it on your machine, and when Apidog is a practical native-first alternative.

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Introduction

Postman started as a Chrome extension in 2012. That was a practical idea at the time: use the browser as a lightweight interface for sending HTTP requests. When Chrome deprecated packaged apps, Postman moved to Electron, the cross-platform desktop framework built on Node.js and Chromium. That migration happened around 2016, and Postman has been an Electron app since then.

The tradeoff is straightforward: Electron bundles Chromium with every desktop app. For Postman, that means launching a browser runtime before you send a single request.

In 2016, this made sense. Cross-platform desktop development was fragmented, and Electron offered a faster path to ship on macOS, Windows, and Linux. In 2026, the cost is harder to ignore: slower startup, higher memory usage, and more background processes.

If you are debugging APIs all day, this matters. Waiting for your API client to become interactive is development friction.

This article breaks down:

• Why Postman feels heavy
• How to measure the impact locally
• Where the startup and memory costs come from
• How Apidog compares for common API development workflows

The Electron problem

Electron embeds a full Chromium browser engine into every app. When you launch Postman, you are effectively launching:

• An Electron main process
• One or more Chromium renderer processes
• GPU and network service processes
• Background utility processes
• Postman’s JavaScript application code

On a MacBook Pro with an M2 chip and 16 GB RAM, typical Postman metrics look like this:

Metric	Typical value
Cold start time	6–9 seconds
RAM at launch	~280 MB
RAM with 3 collections open	450–600 MB
RAM with multiple workspaces and mock servers active	700 MB+
Processes spawned on macOS	8–12

A GUI API client will always use more resources than curl, but the gap is large. For comparison, curl can send an HTTP request in milliseconds and usually uses only a few megabytes of RAM.

curl -w "\nTotal time: %{time_total}s\n" https://example.com

That comparison is not meant to suggest replacing a full API platform with curl. It shows the architectural baseline: sending HTTP requests is cheap; managing a browser-based desktop app is not.

The Chromium engine bundled with Electron is roughly hundreds of megabytes of compiled binaries. That creates a high resource floor before Postman-specific features even start running.

How to measure Postman performance locally

Before switching tools, measure the impact on your own machine. Hardware, network, workspaces, and collections all affect the result.

1. Measure startup time manually

Close Postman completely, including background processes. Then launch it and time how long it takes until the UI is usable.

On macOS:

time open -a Postman

This measures launch command completion, not full UI readiness, so also use a stopwatch for “click to usable UI”.

On Linux, if installed as an app:

time postman

On Windows PowerShell:

Measure-Command { Start-Process "Postman" }

Again, pair this with a manual check for when the UI becomes interactive.

2. Check memory usage

On macOS:

ps aux | grep -i postman

Or use Activity Monitor and search for Postman.

On Linux:

ps aux | grep -i postman

For a process tree view:

pstree -p | grep -i postman

On Windows PowerShell:

Get-Process | Where-Object {$_.ProcessName -like "*Postman*"} |
  Select-Object ProcessName, Id, WorkingSet64

To display memory in MB:

Get-Process | Where-Object {$_.ProcessName -like "*Postman*"} |
  Select-Object ProcessName, Id, @{Name="MemoryMB";Expression={[math]::Round($_.WorkingSet64 / 1MB, 2)}}

3. Test under realistic load

Measure memory in these states:

1. Fresh launch
2. Three collections open
3. Multiple workspaces loaded
4. Collection Runner after a large run
5. Mock server active

This gives you a practical view of how Postman behaves during your actual workflow, not just at idle.

Why Postman keeps getting heavier

Postman has expanded far beyond a basic REST client. The app now includes:

• API design with schema editing
• Mock server management
• Documentation publishing
• Monitoring and alerting
• Flow builder for visual API workflows
• API Network for public API discovery
• Team and workspace collaboration

Each feature adds code, UI state, background work, and sync behavior.

A modern Postman installation can exceed 400 MB on disk and may download additional resources on first launch. Because Postman runs inside Electron, these features execute primarily in a JavaScript environment inside Chromium. That adds overhead compared with a smaller native implementation for the core request engine.

Postman also syncs aggressively with its cloud backend. On startup, it may fetch:

• Workspace state
• Collection updates
• Account metadata
• Team data
• Environment variables
• Remote configuration

On a fast network, this may add a second or two. On a corporate VPN or proxy, it can add several seconds and make the app feel blocked before you can start working.

Memory behavior during a work session

Fresh-launch memory is only the starting point. Real-world usage usually grows over time.

Electron apps use V8, the JavaScript engine behind Chromium and Node.js. V8 manages memory through garbage collection and often keeps memory allocated for reuse instead of immediately returning it to the operating system.

Observed Postman behavior during longer sessions:

Scenario	Typical memory behavior
2 hours of active use with 4–5 collections open	700–900 MB
Collection Runner on a 50-request collection	Spikes to 1 GB+
Mock server active	Adds ~100–150 MB

On a 32 GB workstation, this may not matter. On an 8 GB laptop, it can contribute to memory pressure, swap usage, and slower system responsiveness.

Startup time breakdown

Postman startup usually happens in several phases.

1. Electron bootstrap

Electron loads the Chromium runtime and initializes the main process.

Typical cost:

1–2 seconds

2. Application JavaScript initialization

Postman’s JavaScript bundles load inside the renderer process. This includes parsing, initialization, and application state setup.

Typical cost:

1–3 seconds

3. Cloud sync

Postman fetches workspace and account state from its backend.

Typical cost:

1–2 seconds on good broadband
3–5 seconds on VPNs, proxies, or slower networks

4. UI rendering

The React-based UI renders after required data is ready.

Typical cost:

<1 second once data is available

Total cold start commonly lands between 4 and 9 seconds, depending on hardware and network conditions. Warm starts are usually faster, around 2–4 seconds.

For context, VS Code is also Electron-based but is heavily optimized and often cold-starts in 2–3 seconds on the same class of hardware. Postman can feel slower than a full IDE.

How Apidog compares

Apidog uses a different architecture philosophy. Its core HTTP engine is native code instead of JavaScript running inside a browser renderer. The UI layer is lighter than a full Chromium-based stack.

Observed metrics for Apidog desktop on an M2 MacBook Pro:

Metric	Typical value
Cold start time	2–3 seconds
RAM at launch	~180 MB
RAM with 3 collections open	280–350 MB
RAM with mock server active	380–420 MB

The difference is most noticeable on lower-spec machines, older Intel MacBooks, mid-range Windows laptops, and environments where multiple tools are open at once.

Apidog also avoids relying on an npm dependency chain for its core HTTP functionality. That matters for two practical reasons:

1. Fewer moving parts in the request-sending path.
2. Lower supply chain exposure for the core HTTP engine.

This does not mean every team should switch immediately. It means the architecture has a lower baseline cost for common API development tasks.

Offline mode and local-first storage

A major practical difference is startup behavior.

Apidog stores data locally by default, and cloud sync is opt-in. That means the app can open local collections without waiting for a remote workspace sync.

This helps when you are working:

• Behind a strict corporate proxy
• On a VPN
• With intermittent connectivity
• In local development environments
• During travel
• In secure environments where cloud sync is restricted

Postman’s architecture is more cloud-first. Even when data is cached locally, Postman still attempts to sync on launch. If the Postman API is slow or unreachable, startup can stall.

A local-first model removes that blocking dependency from the critical startup path.

The feature bloat question

Postman includes many features that not every developer needs:

• Flow builder
• API Network
• Monitoring
• Public API discovery
• Advanced team workflows
• Enterprise governance features

These are useful for some organizations, but they add cost for everyone because the app still ships and initializes a large platform.

This is both a product strategy issue and a technical issue. A tool that tries to cover every API-related workflow will usually be heavier than a tool focused on the core API development lifecycle.

Apidog focuses on:

• API design
• Request testing
• Mocking
• Documentation
• Team collaboration

It does not include a direct equivalent to Postman Flows or a public API marketplace. Whether that tradeoff works depends on your team’s workflow.

If your daily work is mostly sending requests, validating responses, maintaining collections, mocking APIs, and sharing documentation, a leaner tool can reduce friction.

When Postman’s performance cost is worth it

Postman may still be the right choice if your team is deeply invested in its ecosystem.

Postman’s performance cost may be acceptable if:

• You rely on Postman Flows for API orchestration
• Your team uses the API Network for public API discovery
• Enterprise Postman features are part of compliance workflows
• Migration cost is higher than the performance gain
• Your hardware has enough memory that Postman overhead is not noticeable

The performance argument is strongest for:

• Developers on lower-spec machines
• Teams with many open collections
• Teams running mock servers frequently
• CI/CD workflows where startup time affects pipeline duration
• Developers whose primary use case is HTTP request testing
• Teams that prefer local-first API tooling

Practical migration checklist

If you want to evaluate Apidog against Postman, run a small migration test instead of switching everything at once.

1. Pick one active API project

Choose a project with:

• A few collections
• Multiple environments
• Auth configuration
• Common request scripts or tests
• Documentation or mock requirements

Avoid starting with your largest workspace.

2. Export from Postman

In Postman:

1. Open the collection
2. Select Export
3. Choose the recommended collection format
4. Export related environments separately if needed

3. Import into Apidog

Import the exported collection and verify:

• Requests
• Headers
• Query parameters
• Body payloads
• Environment variables
• Authentication settings
• Example responses

4. Compare common workflows

Run the same tasks in both tools:

• Open the app from cold start
• Send a basic GET request
• Send an authenticated request
• Switch environments
• Run a collection
• Start a mock server
• Edit documentation

Track startup time and memory usage during each step.

5. Decide based on your workflow

A good decision rule:

If your team uses Postman-specific platform features daily,
the migration cost may not be worth it.

If your team mostly tests, mocks, documents, and collaborates on APIs,
a faster local-first tool may be a better fit.

Conclusion

Postman’s performance issues are not mysterious. They come from architectural choices that made sense in 2016: Electron, bundled Chromium, cloud-first sync, and a growing all-in-one feature set.

Those choices now create noticeable overhead for many API development workflows.

If Postman starts slowly, consumes too much memory during long sessions, or blocks your work during cloud sync, measure the impact on your own machine. Then compare it with a local-first alternative like Apidog using the same collections and workflows.

For teams focused on core API development—design, testing, mocking, documentation, and collaboration—the performance difference can translate directly into a smoother daily workflow.