Where Is The Best Place To Run AI?

Many applications require artificial intelligence (AI) these days. However, developers frequently underestimate the many crucial, upfront decisions they must make to ensure their implementation works well within the budget they have appropriated.

The first decision must be whether the AI will process the data it consumes in the cloud or on the network edge. There are speed and cost advantages to deploying AI at the edge, but the cloud has captured the imagination of the majority due to its massive compute power and storage capacity. Whether using cloud or edge (or even a combination of the two), there are aspects to consider.

Data Volume Introduces Latency

Uploading vast amounts of data to the cloud can often introduce very high latency, in many cases surpassing hundreds of milliseconds, which can seriously impair operations to the point of rendering them ineffectual at best and total wastes of time and money at worst. The ability to handle large amounts of data almost effortlessly and consistently is a key—but too often overlooked—factor in the effective deployment of AI.

Moving Data Around

In terms of bandwidth to move data around, you typically not only must pay for a lot of it, but you must also pay for it to be available at peak performance 100% of the time. You don’t want to be paying to process bad data. It costs the same, but bad data can be very expensive to root out and discard without ruining the good data.

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