Serverless Product Photo Pipeline for Small Online Sellers
Build a serverless pipeline on AWS, Azure or Google Cloud that takes phone photos from small online sellers, strips location data, resizes and converts them to WebP, makes thumbnails and serves them fast through a CDN, with a per-image cost estimate and a dead-letter queue for failures. A beginner-friendly cloud final year project that exercises object storage events, functions, IAM and cost control.
Beginner
Achievable by one student in a semester with familiar tools.1 semester · 1 to 2 students
6 to ship
4 optional extrasSuggested stack
What you should ship
- An upload page that gets a short-lived signed URL and uploads a photo straight to a private bucket, never through your own server
- An event-triggered function that strips EXIF metadata including GPS location, resizes to three sizes, converts to WebP and writes the results to an output bucket
- A CDN in front of the output bucket serving images over HTTPS, with sensible cache headers
- A dead-letter queue or retry policy for failed images, and an alert when failures pass a threshold
- Least-privilege identities: the function can read only the input bucket and write only the output bucket
- All infrastructure in Terraform, plus a cost report estimating cost per 1,000 images from the provider's pricing calculator and measured usage
If you have time left
- Background removal or automatic cropping to a square product shot
- A WhatsApp or Telegram bot that accepts photos and returns a product link
- A second cloud version with the same interface, comparing cost and cold starts
- Lifecycle rules that move original photos to a cheaper tier after 30 days
The problem: good product photos are slow, heavy and leak location
Small sellers on Instagram, Facebook Marketplace, Daraz and their own Shopify or WooCommerce stores shoot product photos on their phones. The files are several megabytes each, load slowly on mobile data, and often carry GPS coordinates in their metadata that reveal where the seller lives or works. Resizing and cleaning each photo by hand takes time most sellers do not have.
What already exists and the gap
- Cloudinary and imgix are powerful image platforms with transformations and CDNs, but they are aimed at developers and businesses with a budget, and their pricing is built around usage tiers.
- Shopify and other store builders optimise images inside their own platform, which does not help sellers who sell through social media or several channels.
- Phone apps can resize photos, but each seller must remember to use them, and they rarely strip location data.
The gap is a simple, cheap pipeline a small seller, or a developer serving many small sellers, can run on their own cloud account and understand completely, with a clear cost per image.
Inspiration
This is the classic "thumbnail on upload" serverless pattern that every cloud provider uses in its own tutorials, applied to a real audience and finished properly: private storage, least privilege, failures handled, a CDN and a cost report. It is also a common building block inside larger e-commerce platforms.
What you build
Choose one cloud. On AWS: an S3 input bucket triggers a Lambda function, which writes to an output bucket behind CloudFront. On Azure: Blob Storage events trigger Azure Functions, with Front Door in front. On Google Cloud: a Cloud Storage event triggers a Cloud Run function, with Cloud CDN behind a load balancer. A small web page requests a signed URL and uploads directly. Everything is defined in Terraform.
The technically interesting part
Getting the event-driven details right: avoiding an infinite loop where the function's output triggers itself, handling large files and time limits, making processing idempotent so a retry does not create duplicates, and measuring cold starts. The cost report is where you show judgement: requests, compute time, storage, CDN data transfer and logs.
How you will evaluate it
Process a few hundred real product photos. Measure size reduction, page load time before and after on a throttled mobile connection, processing time including cold starts, failure handling, and cost per 1,000 images. Confirm with a metadata viewer that no location data remains.
Path to a product
Package it as a one-click deployable template for developers who build stores for small businesses, or run it as a hosted service with a WhatsApp upload flow and a flat monthly fee for sellers. Agencies that manage many small stores are a natural first customer.
Scope warning
Do not build a full store or an image editor. One cloud, one clean pipeline, a CDN, failure handling and an honest cost report is the project. Keep AI features as stretch goals.
Learn what you need on Sefism
- Cloud engineering roadmap: pick AWS, Azure or Google Cloud and work through identity, compute, storage, networking, cost and infrastructure as code in order.
- Cloud engineering cheat sheet: the service name map across the three clouds, IAM policy anatomy, budgets, cost traps and Terraform basics in a line or two each.
- AWS Documentation, Azure documentation or Google Cloud Documentation: the storage events, functions and CDN pages for your cloud.
- AWS Skill Builder or Google Skills: free labs on functions and object storage.
- Terraform Tutorials: define the buckets, function, permissions and CDN as code.
Get help with this idea
The risk with this project is stopping at a working demo, without least privilege, failure handling or the cost numbers that make it convincing. Tauseef can help you cut the scope to one semester, choose the cloud and services, and plan an evaluation your supervisor will accept. If you are new here, create a free account first.
Ideas and guidance, not finished projects
These are project ideas and scoping guidance, published free for students to use as a starting point. I do not build, write, or sell final-year projects, and I do not complete coursework for anyone. Take an idea, make it yours, and build it.
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