When we think about Edge AI platform the first thing that comes to mind is NVIDIAs Jetson but the market is much broader than that. Of course our choice should depend on the destination of our project but what are the choices exactly? We will do our best to give you an answer to that so there won’t be any doubts anymore.

What is Edge AI?

What exactly is this whole edge AI? Well it’s simply AI integrated into our devices. It analyzes the data locally instead of sending it to a cloud service. Essentially it enables full AI functionality without relying on the internet connection and data transmission speed and quality. It also lowers the latency of getting an answer from a model and further shortens input lag. It is great when it comes to making rapid decisions based on a constantly changing environment. First thing that comes to mind is the automotive section and while that is a correct assumption, in reality it is not as easy and narrow as you may think. This technology has many applications in different market branches.

Why does hardware choices matter for edge AI projects?

For starters, let’s discuss the three main parameters that are critical in differencing Edge AI platforms. These are Power, Latency and Cost. While cost is a pretty obvious thing, as everyone wants to spend less, when it comes to power and latency it is not so clear. 

Power is not only a matter of money spent for our device to run but also a temperature that it achieves. To further discuss that, we need to understand how exactly manufacturers build the device. In most cases, manufacturers seal the Edge AI device in a box to prevent the occurrence of dust particles. And that is a problem when it comes to projects that drain high power. They will simply overheat and throttle their speed or even break. 

The second thing to discuss is latency. And it is a bit simpler than the power problem. It is mostly dictated by the designated usage of our device. If it is something that needs to be precise or makes decisions on the fly, then the latency can’t be too high. If our device analyzes data like sound or video, but its purpose is to classify that data, then we don’t need the fastest reaction time.

What are the threats of choosing the wrong Edge AI platform?

And as you can imagine all 3 criterias are somewhat correlated with each other so the choice is not always that obvious. While we will discuss the choices a bit more further in the text let’s explain what threats the wrong choice may bring. 

The obvious one would be a loss of money and time. While money losses come naturally with every wrong design choice, the amount of time lost can differ based on what went wrong. In our case time consumed for redesigning a project to be compatible with a different edge hardware can be quite high. And it goes back to our criteria, due to the power and latency ones, to be precise. The platforms have different connection types and can even stop working with the wrong type of other components. So the wrong choice not only leads to changing the platform itself but also some other parts of the project.

What is the best Edge AI board?

The answer is: It is complicated.  In this case as in many others there is not a perfect solution. The models are different from each other and it is not a linear difference. Let’s briefly discuss what is available on the market right now. While probably the most common and known is Nvidia’s Jetson there are many more possibilities. To choose a perfect one for our project we need to first focus on the specter of said project. Is it more smart home or smart city oriented? Or maybe it is a MedTech section of the market? While they are not the only options I will focus mostly on them to bring the problem closer. 

Smart Home & City

When it comes to smart home devices, they don’t need to be the most efficient ones when it comes to latency. Consumers also don’t want the highest power bills so our main focus would be on those two criteria. The cost of the board itself is not always the problem, if in the long run it will be a cheaper product to use than the others. Smart city products on the other hand often require to be the most efficient ones, due to their non-stop working processes. 

The power drain is not that simple. It is sometimes required to be very low if the product is used in big batches. In other cases power drain fades to the background as the low latency and fast response become the main focus. If the system is used to detect emergencies or react in real time to the changes of environment it can’t “think” for too long. The production costs are similar to latency. When a product is meant to be bought in big batches it is often meant to be cheap to make but if it’s a crucial one that operates above many others then it is designed to work as fast and efficiently as possible.

MedTech

MedTech products are somehow similar but for different reasons. For example, if our product analyzes data gathered after a patient examination, it doesn’t need to be the fastest but the most precise. In such cases, developers sometimes prefer a board with lower efficiency but better outcome reliability. On the other hand, if the device controls a patient’s vital signs in real time or plays a role in surgery, speed becomes key. Also, a battery-powered device cannot drain too much power; otherwise, it won’t last long or might shut down unexpectedly without our knowledge. However, in MedTech, engineers decide the device type early in the development process, and they establish the focus points at that stage as well.

Overview of leading Edge AI platforms for IoT

After breaking down the theoretical problems lets focus more on the practical side. As of today, the beginning of 2026,  we have 5 main choices of Edge AI platforms. Lets briefly discuss each and every one of them so there will be a clearer vision of the market. 

Nvidia Jetson

The first one in NVIDIA’s Jetson and while it is probably the most known one we will summarize its strengths and weaknesses. For starters it is versatile because it supports almost any AI framework without heavy conversion so you don’t need specialists specifically for the platform but with a broader spectrum of knowledge in terms of programming languages. If you need more details on specific models, we have a full comparison of NVIDIA Jetson boards available. Its GPU is the key here as it is similar to a desktop card one. The downside of it is a moderate efficiency compared to some of its competitors. Nevertheless it is mostly used in devices that are pretty advanced and complex. For example autonomous robots with both vision and data analysis done at the same time. And that is because of the high GPU power that makes it possible to process multiple high-definition video streams. But we wouldn’t recommend it for simple task projects as it will just be an overkill. The costs will be too high and its possibilities won’t really be reached.

Google Coral

Now let’s focus on something different. Google Coral, unlike the previous case, is meant to be used for simple and repeated tasks. We can say that Google Coral is an option to consider due to its high-speed vision and low power drain. Engineers target applications that require repetitive identification, such as doors that open via facial scans or systems that monitor product inventory. Because a battery powers the board, developers can integrate it into standalone devices. But when it comes to its complexity it lacks the computing power. In devices that are versatile it will be a burden at least due to its destined usage. Another downside is its narrow support for frameworks as it only supports the TensorFlow Lite models.

Raspberry Pi

In the Raspberry Pi case, it is pretty simple as its efficiency is pretty low compared to other boards. But it still has its usage. Mostly in prototyping as it is cheaper and simpler to use than its competitors. (However, for more demanding projects, you might want to check these cost-effective alternatives to Raspberry Pi 5). It is also pretty versatile and not focused on a single use case but is also not the best in any category. It has more latency than other products and lacks the ability to process data from many inputs at once. Furthermore, the engineers did not design it to operate differently than other boards. However, it remains the best option if you want to learn how to operate Edge AI devices or if you plan to build an automated machine yourself, provided you do not intend it for mass production. Also when it comes to educational projects it fits right in thanks to its low cost.

AMD Kria

When it comes to latency AMD Kria has little to none due to its design and destination. It is meant to be used in the production line machines that must be in perfect sync. It achieved its speed by allowing the AI model to connect directly to hardware logic and bypass the possible CPU bottleneck. Essentially it means that the board is applying AI decisions in real time and doesn’t wait for the CPUs acceptance. Considering using Kria comes only if you are focused on the Smart Factory section of the market. It otherwise doesn’t make sense due to its infrastructure. Normal devices are CPU oriented as they are mostly responding or analysing something first while still making room for the components other than just the AI model. So it is natural for them to have this control over everything and not having something bypass that completely.

Intel OpenVINO

The last but not least option is the Intel OpenVINO. It is different from the other ones. While developers deploy previous products in a development state, users commonly integrate this one into existing Intel-based infrastructure. This toolkit rather than a single product boosts the efficiency of older devices. It assists the CPU by splitting tasks to prevent overloading. Its main downside remains its power consumption; it consumes more power than the Google Coral, even though its creators intended it for simple tasks like POS systems or counting people in lines.

Comparison table

Here is a table to briefly summarize everything from the previous comparison:

Platform Power consumption Cost Latency Common usage
NVIDIA Jetson
5W – 25W
$250 – $500
Sub-10ms
Autonomous robots, drones
Google Coral
2W – 4W
$100 – $150
Sub-5ms
Smart doorbells, wildlife cameras
Raspberry Pi 5
5W – 12W
$130 – $180
15ms-50ms
Home automation, student projects
AMD Kria
5W – 15W
$250 – $350
Deterministic
Precision industrial picking
Intel OpenVINO
15W – 65W+
$300 – $800
5ms-15ms
Smart kiosks, older infrastructures

Data based on official manufacturer specifications as of Q1 2026.

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Conclusion

We hope that now topic of choosing of Edge AI platform is not as hard and distant as it was before. It is a technology that constantly evolves and new platforms are in the making as we speak but even so let’s now hesitate in using it. It is a versatile and powerful tool. Choosing one platform incorrectly has its risks but it’s the same story with many other components.

 At the end of the day it may still be hard if your project is something that can’t be put in one category. But don’t be discouraged and feel free to reach out to us, we will gladly help with making the best choice as we already did projects that involved Edge AI. We have real passionates in WizzDev and they would love just to consult with you what are your options and what they think is the best. And who knows maybe it will turn out that the potential of your project is much higher than expected and can be expanded further or used in a slightly different way to make a massive success. We have covered a major topic and we hope it was useful for you and it will make life easier in the future with making new devices.