TL;DR
Postman is an Electron app built on Chromium, and in 2026 it shows. Startup times regularly exceed 5-8 seconds on modern hardware, RAM usage can climb past 500MB with a few collections open, and the app ships a full browser engine to send HTTP requests. This article breaks down where the performance goes, why it matters, and how Apidog compares as a native-first alternative.
Introduction
Postman started as a simple Chrome extension in 2012. A browser extension for sending HTTP requests was a clever idea, and it grew fast. When Chrome deprecated packaged apps, Postman migrated 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 ever since.
The problem is that Electron apps bundle an entire Chromium browser engine, which is hundreds of megabytes of code, to run what is fundamentally a JavaScript application. The tradeoff made sense in 2016 when cross-platform desktop development was fragmented. In 2026, it’s increasingly hard to justify.
Developers on Reddit and Hacker News have noticed. “Postman takes longer to start than my IDE” is a complaint that surfaces regularly. Performance issues in API tools translate directly into development friction. Every second waiting for Postman to load is a second you’re not writing code or debugging an API.
This article takes an honest technical look at what causes Postman’s performance issues and what the alternatives actually deliver.
The Electron problem
Electron embeds a full Chromium browser engine into every app. When you launch Postman, you’re launching a browser. The initial process tree includes a main process, a renderer process for the UI, and often several background utility processes.
On a MacBook Pro with an M2 chip and 16GB RAM, typical Postman metrics:
- Cold start time: 6-9 seconds from click to usable UI
- RAM at launch: ~280MB
- RAM with 3 collections open: 450-600MB
- RAM with multiple workspaces and mock servers active: 700MB+
- Number of processes spawned: 8-12 on macOS (main, renderer, GPU, network service, etc.)
For comparison, a terminal-based tool like curl sends an HTTP request in milliseconds and uses about 3MB of RAM. Obviously a GUI tool with collections management and documentation requires more overhead than curl, but the question is whether that overhead needs to be this large.
The Chromium engine Postman bundles is roughly 300MB of compiled binaries. Even before Postman-specific code runs, those binaries are in memory. This is the architectural floor for any Electron app.
Why Postman keeps getting heavier
Postman’s feature set has expanded dramatically since 2016. The app now includes:
- API design with schema editor
- Mock server management
- Documentation publishing
- Monitoring and alerting
- Flow builder (visual API workflow tool)
- API network (public API repository)
- Team and workspace collaboration features
Each of these features adds weight. A 2024 Postman install is over 400MB on disk, and the application actively downloads additional resources on first launch. Electron’s architecture means all these features run in a JavaScript environment inside a browser, which adds a performance tax compared to compiled native code.
Additionally, Postman syncs aggressively with its cloud backend. On startup, it fetches workspace data, collection updates, and account state. On a slow or corporate network, this sync phase is where a lot of startup latency comes from. The app is doing cloud operations before it’s even interactive.
Memory behavior over a work session
The RAM numbers above are for fresh launch. Real-world memory usage climbs over a work session.
Electron apps use V8’s JavaScript engine, which manages garbage collection. V8 tends to hold onto memory longer than native allocations, releasing it in batches. An Electron app that’s been running for two hours often uses significantly more RAM than it did at launch, even without any changes in what collections are open.
Measured observations from extended Postman sessions:
- After 2 hours of active use with 4-5 collections open: typically 700-900MB
- After running Collection Runner on a 50-request collection: RAM often spikes to 1GB+ and doesn’t fully return to baseline
- With mock server active: add another 100-150MB
On machines with 8GB of RAM, Postman becomes noticeable in the system’s memory pressure. On 16GB machines it’s tolerable. On 32GB workstations it’s a non-issue. But “tolerable” and “fast” are not the same thing.
Startup time breakdown
Postman’s startup involves several sequential phases:
- Electron bootstrap: The Electron runtime loads. On fast SSDs, this takes 1-2 seconds.
- App JavaScript loads: Postman’s application code runs inside the Chromium renderer. Webpack bundle parsing and initialization takes 1-3 seconds.
- Cloud sync: Postman fetches workspace state from its API. On good broadband this adds 1-2 seconds. On corporate proxies or VPNs, 3-5 seconds.
- UI render: The React-based UI renders. Usually under 1 second once data is loaded.
Total cold start: 4-9 seconds depending on hardware and network. Warm starts (already-loaded system resources) are faster, typically 2-4 seconds.
For comparison, VS Code (also Electron, but heavily optimized) cold-starts in 2-3 seconds on the same hardware. Postman is slower than a full-featured IDE.
How Apidog compares
Apidog’s desktop app is built with a different architecture philosophy. The core HTTP engine is native code, not JavaScript running in a browser renderer. The UI layer uses a lighter rendering approach than a full Chromium stack.
Observed metrics for Apidog desktop on an M2 MacBook Pro:
- Cold start time: 2-3 seconds
- RAM at launch: ~180MB
- RAM with 3 collections open: 280-350MB
- RAM with mock server active: 380-420MB
The difference is most noticeable on startup and on lower-spec machines. A developer using a 2020 Intel MacBook Pro or a mid-range Windows laptop will feel the gap more than someone on a high-end workstation.
Apidog doesn’t bundle an npm dependency chain for its core HTTP functionality. This matters for two reasons. First, it means fewer potential failure points in the HTTP stack. Second, it reduces supply chain risk: a compromised npm package can’t affect the core request-sending functionality if that code isn’t Node.js-based.
Offline mode and local-first storage
Another practical performance difference: Apidog stores data locally by default. Cloud sync is opt-in.
This means Apidog’s startup doesn’t include a mandatory cloud sync phase. The app opens your locally stored collections immediately, without waiting for a server round-trip. On corporate networks with strict proxy settings or in environments with intermittent connectivity, this difference is especially noticeable.
Postman’s architecture ties collection state to the cloud. Even with collections “cached” locally, Postman wants to sync on launch. If the Postman API is slow or unreachable (it happens), the app stalls during startup. Apidog’s local-first model sidesteps this entirely.
The feature bloat question
Postman ships a lot of features most users don’t need. The Flow builder, API Network, and monitoring features are sophisticated tools. They’re also the kind of features that add startup weight and memory overhead for everyone, including developers who will never use them.
This is a product strategy question as much as a technical one. A tool that tries to be everything for every API-related workflow will always be heavier than a tool that does less. Postman has made clear bets on being a full-platform API solution. The performance cost is a consequence of those bets.
Apidog covers the core API development lifecycle: design, test, mock, document. It doesn’t include visual flow builders or a public API marketplace. Whether that tradeoff is right depends on what your team actually needs, but the result is a leaner binary and a faster workflow for the common case of sending requests and running tests.
When Postman’s performance is worth it
To be fair: for teams that are deep into Postman’s ecosystem, the performance cost may be acceptable.
If your team uses Postman Flows for complex API orchestration, that’s a capability Apidog doesn’t have. If you rely on Postman’s API Network for discovering public API specs, there’s no direct equivalent. If your organization has Postman enterprise features baked into compliance workflows, migration cost outweighs the performance gain.
The performance argument is strongest for:
- Developers on lower-spec machines
- Teams with many open collections and mock servers
- CI/CD environments where startup time affects pipeline duration
- Anyone whose primary use case is HTTP request testing and team collaboration
Postman’s performance issues aren’t mysterious. They’re the direct result of architectural decisions made in 2016 that made sense then and are showing their age now. A bundled Chromium engine, cloud-first data sync, and an expanding feature set add up to a tool that’s noticeably heavier than it needs to be for most API development work. If you’re spending meaningful time waiting for Postman to start or watching your system slow down during a long testing session, the performance numbers support trying an alternative.
