So That’s What Edge Computing Does: AI at the Advertising Edge

Last updated on October 29th, 2025 at 12:55 pm

So listen, after a few months of experimenting with TensorFlow Lite and Edge Impulse, this is what I discovered: edge computing AI isn’t some kind of tech mumbo-jumbo. It is repairing actual pain points that cloud-based AI can’t address.

The Latency Issue, And How I Almost Missed a Demo

I was in the midst of creating a quality control system for a manufacturing client. First attempt? Cloud-based. The camera would snap a defect, send the images to AWS for analysis and then flag the issue once feedback arrived. Sounds good until you consider that items are moving 60 units a minute on a conveyor belt.

The lag — only 200 to 300 milliseconds was enough time for defective products to be three positions down the line when we caught them. Total disaster.

Edge AI runs on-device, not from a far-away data center there-and-back-again. I eventually migrated to an edge environment with TensorFlow Lite on an NVIDIA Jetson. The detection occurred in less than 50 milliseconds. Problem solved.

The upshot is that with edge AI, decisions can be made instantaneously without the need to wait even for a millisecond.” And this is important when you consider time-to-decision or response time-sensitive applications. Vehicles that drive themselves don’t have time to wait around for answers from the cloud when they spot a pedestrian stepping onto the road.

Vital signs monitoring medical devices ought to be able to notify doctors right away, not after traveling back and forth across the internet to a server farm in Virginia.

The Privacy Disaster That No One’s Talking About

This one I learned the hard way with a healthcare prototype. We were creating a wearable that measured patient vitals. The first time, the legal team took one look at our cloud architecture and shut it down. Why? By processing sensitive data locally, you mitigate transmission risk and remain compliant with privacy laws such as GDPR and CCPA.

Edge AI stores patient data on the device. Heart rate spikes? The wearable snags it, processes the data and notifies the care team all without your medical data bouncing back and forth through three continents. So do security cameras in office buildings or financial systems that process transactions.

And when I moved my model deployment to Edge Impulse, the entire pipeline remained local. No data leaks. No compliance headaches. Just intelligence where it’s needed.

The Bandwidth Bill I Could Not Believe

Here’s a fun one: A customer was deploying AI-powered cameras in 50 retail outlets. All the cameras beamed video feeds to the cloud for analysis of customer behavior. Their monthly bandwidth costs? Over $40,000.

Studies demonstrate that edge-cloud systems can reduce the cost by 36% compared to cloud-only systems and data transfer volume by 96%. I installed edge models that would process video on the spot and only send summary information how many customers, the traffic pattern and dwell times. Monthly costs for bandwidth plummeted to less than $8,000.

The math is straightforward: cloud AI equals perpetually uploading data. Edge AI equals you only send what matters.

The “No Internet” Crisis

I was working as a consultant for an agriculture tech company which was using crop monitoring systems. Their fields? Middle of nowhere. Cellular coverage? Spotty at best. For hours, when their connection was down, their cloud-based system would freeze.

For such use-cases, edge AI is capable of functioning independently of the internet even without network connectivity, making it reliable in remote locations or network constrained environment. We redesign their sensors to have edge inference capability. Now they monitor soil moisture, detect patterns in pest behavior and turn on irrigation all without tapping a signal.

Same goes for maritime operations, mine sites or disaster response areas where there’s no connectivity but still decisions need to be made.

The Nuts and Bolts (What Actually Works for Me)

Having used both TensorFlow Lite and Edge Impulse, this is what I would recommend:

Start small. For prototyping, I just used the free tier of Edge Impulse it’s perfect to see if edge deployment makes sense for your use case. Their framework takes care of the model optimization for them, which saves weeks of trying to do this manually in order to compress a network.

It depends more on hardware than you’d believe. Select your hardware to match with your use cases: economy of scale should go from IoT device with AI processor to edge server doing massive work. I’ve run it even on Raspberry Pi (for basic image classification) and Qualcomm-powered phones (which you need for real-time video analytics).

Hybrid is your friend. The edge takes away time-sensitive and local tasks but the cloud is still useful for model training and long-term analytics. I train models in the cloud with GPU clusters and deploy the compressed versions to edge devices. Best of both worlds.

The Bottom Line

Edge computing AI isn’t a Rebellion against the cloud it’s an antidote to problems of cloud AI. Latency issues? Solved. Privacy concerns? Addressed. Bandwidth costs? Slashed. Connectivity requirements? Optional.

I have seen edge AI go from experimental to necessary in the past two years. The worldwide edge AI market was worth about $21.19 billion in 2024, and will continue to rise until it reaches roughly $143.06 billion by 2034, and you know what? It’s not hype. It’s companies coming to understand that sometimes the smartest place for intelligence is right where the data is.

FAQs

Does edge AI really work offline or is that just marketing speak?

It actually works offline – I’ve set up systems that run for weeks on end with no network connection. Among the benefits of edge AI is running with or without internet, which allows it to work in places or during times that have poor network connection.

The catch? You’ll eventually want cloud access for model updates or to combine insights, but otherwise day-to-day operation proceeds without support.

What is the actual cost disparity between edge and cloud AI?

Based on my deployments, the upfront cost of hardware is more with edge (plan for spending between $500 and $5,000 per location based on the computing needs), but the operational savings arrive sooner. Edge-cloud hybrid systems as much as 36% cheaper than cloud-only setups, and reduce data transfer by 96%.

I’ve heard claims of 6 to 18 months for a payback period depending on how much data you need processed.

Can I use my AI models at the edge, or do I have to rebuild them?

You can tune most but they require tuning. The likes of TensorFlow Lite and Edge Impulse manage the conversion techniques like quantization shrink model size by converting weights from FP32 to INT8 with minimal loss of accuracy.

I’ve taken cloud models and thrown them on the edge by compressing 75% while still preserving above 90% accuracy. It’s work, but you’re not building from zero.