Edge compute moves application logic physically closer to end users or devices by running it on globally distributed infrastructure, rather than in centralized cloud
data centers. The key difference from a multi-
region setup is the granularity: edge platforms can deploy code to hundreds or thousands of locations (Points of Presence or PoPs), often allowing execution within milliseconds of the user. While a
CDN caches static content, edge compute executes your custom code at these PoPs.
You should use edge compute when your application's core constraint is
latency, especially for geographically dispersed data sources like your IoT sensors. It's ideal for preprocessing sensor data, running real-time inference with a small ML model, or handling API requests that require immediate response. For your IoT project, deploying a data validation or aggregation function at the edge would reduce
bandwidth costs and latency by filtering noise before sending only essential data to your central cloud. A standard cloud VM in a few regions is sufficient when latency isn't the primary bottleneck and your workload requires sustained, heavy computation or uses large, complex datasets that are impractical to distribute globally.
For example, an edge function to validate and compress sensor data might look like this:
export default async function handleRequest(request) {
const sensorData = await request.json();
// Validate data locally at the edge
if (!isValid(sensorData)) {
return new Response('Invalid data', { status: 400 });
}
// Aggregate and forward only critical data to central cloud
const criticalData = aggregateData(sensorData);
await fetch('https://central-cloud.com/ingest', {
method: 'POST',
body: JSON.stringify(criticalData)
});
return new Response('OK');
}
The underlying concept is reducing the physical distance data must travel, trading off the simplicity of a centralized architecture for a significant reduction in latency.
