vfrog.ai: Computer Vision Infrastructure for Developers The Problem Computer vision is becoming essential. Smartphones, drones, robots, smartglasses—all capture images as primary data. But most developers avoid building vision features because the infrastructure is fundamentally broken. Training models takes months, requires PhD-level ML expertise, and scaling requires DevOps wizardry. Teams either skip vision features entirely or hire expensive ML specialists just to ship basic features. Result: missed opportunities, frustrated engineers, bloated headcount. The Solution vfrog is infrastructure for computer vision—like Cursor but for vision models. You describe what you want to detect (objects, defects, poses, custom items). We handle training, optimization, and deployment automatically. You get a production-ready API in hours, not months. No ML degree required. No infrastructure headaches. No scaling nightmares. Just an API endpoint you integrate into your product. Key Features - AutoML Training: Upload your dataset. We automatically train, validate, and optimize models using proprietary algorithms. 95% accuracy vs 80% industry standard baseline. Zero manual hyperparameter tuning. - One-Click Deployment: Deploy to production instantly. Works on web servers, mobile apps, edge devices, drones—wherever you need inference. Fully managed infrastructure that scales automatically. - Sub-100ms Latency: Production-grade performance. Real-time inference at scale. Batch processing and streaming both fully supported. Handles millions of requests daily without configuration. - Pre-Built Models: Object detection, pose estimation, custom classifiers. Start with templates or train your own domain-specific models. Mix and match components.
