Edge or cloud AI? Start with the workload's constraints

List the conditions the product must survive

A factory inspection camera, a mobile assistant, and a document analysis service face different operating conditions. An on-device model may need to continue working without a reliable network. A cloud workflow may need shared access to a large, frequently updated information source. Begin by listing latency, connectivity, data handling, power, and update requirements for the actual product.

Separate hard constraints from preferences. If a device must respond during an outage, that is a design requirement. If a team would merely like to reduce network traffic, it can compare several options. This distinction prevents architectural debates from replacing measurable product requirements.

Benchmark with a realistic operating envelope

MLCommons groups inference benchmarks into scenarios and metrics to make system comparisons more meaningful. An internal benchmark should be equally explicit about workload and quality requirements. Test the intended model, input size, concurrency, and device settings. For an edge deployment, include realistic thermal and power conditions rather than measuring only a short run on an idle device.

For cloud execution, include network transit, service queues, and recovery from connection failure. Compare accepted task outcomes at the response time the user needs. A single peak throughput number does not answer whether a particular product will perform reliably in its operating environment.

Plan updates and fallback behavior

A mixed architecture can keep a bounded capability local and use a remote service for other work. That introduces versioning and consistency questions: which model handled the task, which data was available, and what can the user do when the remote path is unavailable? Define those behaviors before launch. The placement decision is complete only when maintenance, monitoring, security updates, and fallback all have workable owners.