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ByDAO EDGEEdge AI and Cloud AI are often discussed together, but they serve fundamentally different roles in real-world deployments.
Cloud AI relies on centralized computing resources and continuous data transmission. While powerful, this model depends heavily on stable network connectivity and can be constrained by latency, bandwidth availability, and regulatory requirements related to data handling.
Edge AI, by contrast, processes data locally on edge devices. This reduces dependency on network conditions, minimizes data transfer, and ensures sensitive information remains within the local environment.
In many practical deployments, hybrid architectures are also common. Edge AI handles time-critical inference on-site, while cloud systems are used selectively for model updates, fleet management, or long-term analytics. This approach balances responsiveness with centralized oversight.
In practice, Edge AI and Cloud AI are frequently used in complementary roles. Edge AI enables immediate, on-site decision-making, while cloud systems support long-term analysis, centralized management, or model updates when required.
This article is part of DAO EDGE’s insights on practical Edge AI deployment.
