What Edge AI is, and how it differs from IoT and Cloud AI
Short answer: Edge AI runs inference close to a camera, sensor or machine so a decision does not wait for a network round trip. It still needs a network and an operating owner for updates, telemetry and recovery.
What Edge AI solves
Edge fits when latency, intermittent connectivity, stream volume or a data boundary makes sending every payload to a central service a poor fit. The workload must say which results stay on site and which health fields may leave.
IoT is primarily about connecting and transporting data. Edge AI adds local processing and decisions. Cloud AI centralises compute and managed operations. A production architecture can use all three.
Decision table
| Constraint | Edge | IoT + Cloud | Hybrid |
|---|---|---|---|
| latency | responds near the source | depends on the network | splits work by event |
| privacy | payload can stay on site | requires an approved data path | sends only named integrations |
| operations | requires fleet ownership | concentrates platform work | operates two boundaries |
No option wins every row. Record workload, stream count, model, hardware and owner in the Edge AI pillar before choosing.
The work after installation
Plan inventory, patch windows, signed updates, approved telemetry, backups, rollback triggers and an owner for offline recovery. Map the boundary in the Trust Center and validate devices in Hardware.
Limitations
A device without memory or thermal headroom will not hold its target latency. Untested cameras and lighting make results unstable, and an FPS number from one site is not evidence for another.
Start with a feasibility assessment that measures the real workload and can recommend no-go.
Read next
Local AI or Cloud AI: an organisation decision tableChoose a GPU and Edge device from the workload, not just TOPSWhy an AI POC works in a demo but fails in productionContinue with the decision context
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