How to Make Cloud Computing and Edge AI Work Together

Imagine you’re building a new Internet of Medical Things (IoMT) device. Your team proposes the go-to approach — push all the data to the cloud for processing and analysis. 

Maybe that’s the right choice, but how do you know?

For starters, how much data are you sending to the cloud, and are you satisfied with the costs? Processing large volumes of data can get expensive. A 2024 report from CloudZero found that “less than half of companies reported healthy cloud costs, with 58% of respondents saying their costs are too high.” 

Secondly, how are you handling data security? The cloud is just someone else’s computer, after all. Healthcare accounted for 28% of all third-party data breaches last year (the highest for any sector). What kind of sensitive data are you willing to share with third parties when third-party breaches are one of the fastest-growing threats? 

Finally, what about speed? When you send data to the cloud, there’s a latency tax. Seconds might not matter so much when you’re processing smart fridge temps, but for, say, a wearable device monitoring a cardiac patient? And sometimes, for whatever reason (like a power outage), a critical care device might get knocked offline altogether. What happens then? How does your device respond? Can it? 

If costs, security, or speed are concerns, then relying strictly on cloud computing might be a mistake. 

What Is Edge AI?

Edge AI refers to deploying an AI model directly onto an IoT device. The hardware and software to support Edge AI (e.g., low-power AI chips, optimized machine learning algorithms) only recently became powerful and cost-effective enough for mainstream adoption.

What does this mean for your IoT (or, for our example, your IoMT) ecosystem? Let’s keep it simple:

• Costs: Processing data locally with Edge AI reduces reliance on cloud resources, cutting expenses for storage, computation, and bandwidth.

• Security: Sensitive data can stay on the device. You can make it so the cloud only sees summaries or anonymized results, making compliance easier.

• Speed: Edge AI dramatically reduces latency. No round-trip to the cloud. No waiting. Decisions are made the moment they’re needed, even without Internet connectivity.

Of course, there are limitations to what you can do at the edge. Your hardware will constrain your processing power, so you’ll have to run lighter-weight models.

Cloud Computing vs Edge AI

Some organizations, especially smaller or less tech-savvy ones, default to cloud-centric solutions because of familiarity or hypothetical simplicity, missing out on the potential benefits of the edge.

But the thing about the cloud vs the edge is that it doesn’t have to be a “vs” at all. They can and do complement each other.

The cloud can do your heavy lifting. It can analyze trends across hundreds or thousands of devices, scale infrastructure, and update your AI models. Simultaneously, you can use the edge for reliable instant responses — making those models actionable in real time while keeping patient data more secure. 

So, if you’re building IoT devices and you’re all-in on the cloud, you might be making a strategic mistake. Edge AI isn’t the future. It’s not something “down the road.” It’s already here.

The market leaders are using them both.

That’s why DeviceFlow, our IoT platform, includes support for Edge AI, because we know it’s vital to our customers’ success. We specialize in building smaller, more efficient models trained on proprietary and sensitive data sets. If you want to learn more about Edge AI strategies or how DeviceFlow can help you build an end-to-end IoT ecosystem, reach out to our team. We can help.