Edge Computing

Edge Computing is an architecture that runs computation close to where data is created — in a nearby data center, a local gateway, or the device itself — instead of sending everything to a distant central server. The motivation is physics: a round trip across continents costs time no amount of server capacity can recover, so moving the work nearer the user cuts latency. It also reduces bandwidth, since raw sensor or video data can be filtered locally, and helps with privacy and data residency because records can be processed without leaving a region. Cloudflare Workers, Fastly Compute, AWS Lambda@Edge, and Vercel Edge Functions run application code across distributed points of presence, while industrial and automotive systems put inference directly on hardware using chips such as NVIDIA Jetson. On-device AI follows the same logic, with Apple and Google running smaller models on phones so requests never leave the handset. The constraint is that edge runtimes are limited: shorter execution windows, restricted APIs, less memory, and no easy access to a central database. Consistency is the usual pitfall, since state spread across many locations needs deliberate design rather than a shared primary database.