Every AI vision project faces the same architectural fork: run the models in the cloud, where compute is elastic and updates are easy, or at the edge, on site, where the cameras are. Neither answer is universally right — but the deciding factors are consistent.
When the cloud wins
Cloud analytics shine when sites have solid connectivity, when models change often, and when you want one place to manage many sites. Uploading events rather than raw video keeps bandwidth sane, and adding a new detection capability is a configuration change, not a site visit.
When the edge wins
Edge processing wins wherever bandwidth is scarce or expensive — rural sites on 4G, vessels, remote infrastructure — because raw video never leaves the site; only results do. It wins on latency where a decision must happen in milliseconds, such as safety interlocks. And it wins on privacy: footage can be analysed and discarded on site, with only anonymised counts or alerts transmitted.
Modern edge AI boxes run serious models on a few watts, which changes the economics: a one-off hardware cost on site replaces a recurring per-camera cloud analytics fee.
The hybrid that most sites end up with
In practice most deployments settle on a hybrid: edge devices filter and pre-analyse — cutting false triggers and bandwidth — while the cloud aggregates events across sites, hosts dashboards and handles long-term storage. You get the responsiveness and privacy of edge with the manageability of cloud.
Getting it right
The right split depends on connectivity, camera count, model churn and data sensitivity — and it is cheaper to design it up front than to re-architect later. Take 2 Technology supplies edge AI hardware and the connectivity around it, and we are happy to talk through the architecture before you commit to either extreme.