Cloud vs Edge: Finding the Sweet Spot for Vision
Choosing between cloud, edge or hybrid for computer vision isn’t just about technology — it’s about finding the right balance between speed, cost and control. In this post, we break down the classic Latency–CapEx–Data Gravity triangle, walk through real-world break-even points and offer a practical roadmap from PoC to scalable deployment. Whether you’re tagging products, anonymizing faces, or automating inspections, this guide helps you make smarter architecture decisions — and hit the vision sweet spot in 2025 and beyond.
Prototype in a Day, Scale in a Year: A Hybrid Vision Road-Map
Computer vision is easier to start than ever — thanks to ready-made SaaS APIs that let teams build working prototypes in just hours. But what happens when those prototypes grow into high-traffic features, or when off-the-shelf models no longer meet performance, cost or accuracy demands? This blog post outlines a step-by-step, 12-month roadmap for scaling your vision AI stack — from your first API call to a fully containerized, GPU-powered infrastructure. Learn how to navigate the transition across three critical phases: the Day‑1 sandbox, the Month‑3 pilot, and the Month‑6 production rollout. Along the way, we break down key decision points, hybrid deployment strategies and tips for balancing fast results with long-term control. Whether you're experimenting with OCR, background removal or object detection, this guide helps you scale smart, reduce costs and prepare for growth.