๐ฆ๐๐ผ๐ฝ ๐ง๐ต๐ฒ ๐๐ฑ๐ฎ๐ฝ๐๐ฒ๐ฟ ๐๐๐ฟ๐ฑ๐ฒ๐ป ๐๐ป ๐๐
We scaled our multi-modal AI app. We made a big mistake. We hardcoded API endpoints for every model.
This created the Adapter Burden. You spend 10% of your time on your product. You spend 90% of your time on request wrappers. You fight JSON schemas from five different vendors.
Scaling showed us these flaws:
- Some endpoints are fast. Others are slow.
- Error codes mean different things on each platform.
- Large images and videos slow down the system.
Our translation layer broke every time an API changed.
We changed our design. We stopped using blocking HTTP requests. We used isolated background tasks.
We added a task gateway. Our app now uses one OpenAI-compatible endpoint. Requests go into a queue.
This hides the post-processing layer. The backend ignores the model type. The system handles polling and errors automatically.
We cut repetitive backend work by 60%.
Small teams need lean operations. Hardcoding wrappers is technical debt. It grows faster than your user base.
Do not write custom routing from scratch. Use a multi-model API aggregation tool. Keep your architecture clean.