Start with the operating environment
A model running beside a camera or machine can respond without a network round trip. That matters when connectivity is limited, response windows are short, or continuous raw data would be expensive to transmit.
Privacy can change the economics
Processing video, audio, or sensor data locally can reduce how much sensitive raw information leaves the site. Often the cloud only needs events, aggregates, or selected evidence rather than a continuous stream.
The edge is not free
Distributed hardware introduces deployment, observability, updates, thermal constraints, and model-compatibility work. A small cloud bill does not automatically justify a fleet of devices that cannot be maintained.
Hybrid is the common destination
Real-time inference and filtering happen locally; training, fleet management, deeper analysis, and aggregate learning happen centrally. The boundary should be designed around failure behavior and total operating cost.
Apply this thinking to a real system.
We can map the operating constraint, evidence requirement, and safest path to a representative proof.
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