Project demo link: https://image-ignite.vercel.app/
This started as a hobby project. I was thinking about a simple image conversion service for resizing ,convert, compress uploaded images.
Since it was a personal project, cost mattered a lot.
Keeping EC2 instances running 24/7 felt unnecessary for a workload that only existed for a few seconds per request.
So I started experimenting with AWS Lambda.
First Attempt: Node.js + Sharp on Lambda
My initial stack was:
| Component | Tech |
|---|---|
| Runtime | Node.js |
| Processing | Sharp |
| Compute | AWS Lambda |
| Storage | AWS S3 |
The architecture was very simple:
Upload → Lambda → Process Image → Store Result
At first, Lambda felt like the perfect solution:
- no idle server costs
- automatic scaling
- no server management
- pay only when used
Exactly what I wanted for a hobby project.
The Problem I Started Noticing
After some usage, cold starts became noticeable.
Especially for image-heavy requests.
The actual image processing was fast enough, but startup time sometimes became a large part of the total request.
Approximate numbers from my experience:
| Metric | Node.js + Sharp |
|---|---|
| Cold Start | ~700ms – 2s |
| Warm Start | ~50ms – 150ms |
| Memory Usage | ~180MB – 350MB |
For a workload that only runs a few seconds, that startup overhead feels significant.
Why I Switched to Rust
I rebuilt the processor using Rust Lambda.
Not because of hype.
The workload simply matched Rust better.
The difference became noticeable almost immediately.
| Metric | Node.js + Sharp | Rust Lambda |
|---|---|---|
| Cold Start | ~700ms – 2s | ~80ms – 300ms |
| Warm Start | ~50ms – 150ms | ~10ms – 40ms |
| Memory Usage | ~180MB – 350MB | ~40MB – 90MB |
For this type of workload, lower startup time mattered a lot more than I initially expected.
Final Thoughts
This project started as a small hobby image converter where I wanted to avoid the cost of always-on infrastructure. I initially used Node.js with Sharp on AWS Lambda, but cold starts became noticeable for short-lived image processing tasks. Moving to Rust improved startup time and reduced memory usage significantly. More importantly, it changed how I think about infrastructure — some workloads work better as temporary, event-driven compute instead of persistent servers.