YOLO vs Cloud API for Object Detection — Which One Should You Actually Use?

You need object detection in your app. You have two paths: run YOLO on your own GPU, or call a cloud API over HTTP. YOLO is free and fast, but it requires a GPU, PyTorch, CUDA drivers, and ongoing maintenance. A cloud API is simple and scalable, but adds network latency and costs money. Here's an honest comparison to help you decide. Quick Comparison Criteria YOLO (Self-Hosted) Cloud API Setup time ~30 min (Python, PyTorch, GPU drivers) ~2 min (get API key) Infrastructure GPU required None — fully managed Cost (1K images/mo) "Free" + GPU hosting ($50–200/mo) $12.99/mo Latency ~20–50ms (local GPU) ~200–500ms (network) Custom training Full fine-tuning Pre-trained only Maintenance You manage everything Zero Offline support Yes No YOLO: The Setup Reality YOLO looks simple in tutorials. The actual setup: # 1. Virtual environment python -m venv yolo-env && source yolo-env/bin/activate # 2. Install PyTorch with CUDA (~2.5 GB download) pip install torch torchvision --index-url https://download