When we started building MakeMyVisuals, our goal wasn't just to remove image backgrounds—it was to create a tool that felt fast enough for everyday users.
No one enjoys waiting 15–20 seconds just to edit a single image.
Whether you're an e-commerce seller removing backgrounds from product photos or a designer creating transparent PNGs, speed matters just as much as accuracy.
Here's what we learned while optimizing our AI image processing pipeline.
The Initial Problem
Our first implementation worked well.
The AI accurately segmented subjects, handled complex edges like hair, and produced clean transparent backgrounds.
The downside?
Large upload times
Heavy image preprocessing
Slow AI inference
Unoptimized output generation
For high-resolution images, the complete workflow could take far longer than users expected.
That wasn't acceptable for a modern web application.
Bottleneck #1 — Uploading Massive Images
Most smartphone photos today are between 5 MB and 20 MB.
Many users uploaded images directly from modern phones with resolutions exceeding 4000×3000 pixels.
The AI didn't actually need every single pixel.
Solution
Instead of processing the original file immediately, we:
Read image dimensions first
Generated an optimized working copy
Preserved the original for export
Sent only the required resolution to the AI model
This significantly reduced unnecessary computation.
Bottleneck #2 — Blocking the User Interface
Initially, preprocessing happened synchronously.
That meant users watched a loading spinner while the browser resized large images.
Not a great experience.
Solution
We moved expensive operations off the main thread.
This kept the UI responsive while background processing continued.
The result felt dramatically faster—even before total processing time changed.
Sometimes perceived performance matters as much as raw performance.
Bottleneck #3 — Processing Images Larger Than Necessary
Many uploaded photos contained far more detail than required for segmentation.
Running the AI against extremely large images wasted GPU resources.
Solution
We introduced adaptive resizing.
Instead of using a fixed resolution, we calculated an optimal size based on:
Original dimensions
Subject size
Required output quality
The AI processed fewer pixels without noticeably affecting quality.
Bottleneck #4 — Repeated Processing
Users often downloaded multiple versions of the same image.
Originally, every export triggered another processing cycle.
Solution
We cached intermediate AI results.
Only the final rendering changed.
That eliminated redundant work and reduced repeat processing times.
Bottleneck #5 — Sending Too Much Data
Large PNG files are expensive to generate and transfer.
Instead of producing oversized outputs every time, we optimized export generation by:
Compressing transparent PNGs
Optimizing metadata
Generating only required image sizes
Smaller outputs meant faster downloads.
Performance Isn't Just About AI
Most optimization came outside the AI model.
We found improvements in:
Image preprocessing
Browser rendering
Memory management
Network transfer
Export generation
Caching
The AI itself was only one piece of the puzzle.
Real User Experience Matters
When people upload an image, they don't care which neural network you're using.
They care about three things:
Does it work?
Does it look good?
Does it finish quickly?
Optimizing those small steps made the biggest difference.
Building MakeMyVisuals
These optimizations are part of what powers MakeMyVisuals, an AI-powered platform for image editing, optimization, and document processing.
Besides background removal, the platform includes tools for:
AI Product Photo Enhancement
Image Compression
Image Resizing
Format Conversion
AI Portrait Editing
Document & PDF Processing
Explore the platform here:
👉
Image Optimization Tools:
👉
Document Tools:
👉
Final Thoughts
Building AI products isn't only about choosing the best model.
It's about removing every unnecessary millisecond from the user journey.
Sometimes the biggest performance gains come from optimizing everything around the AI—not the AI itself.
What performance optimization had the biggest impact on your projects?
I'd love to hear your experience in the comments.
SOCIAL SHARE CARD GENERATOR