Navigating the Differences Between Edge AI vs Cloud AI

Cloud computing has finally moved from a buzzword to an industry standard. You’ve just gotten used to storing, managing, and processing data in the cloud, and now, another technology is taking off. The cloud can struggle to keep up with the growing number of connected systems, so edge computing is being touted as the answer. 

But what are the essential differences between Edge AI and Cloud AI, especially within the context of cloud computing in Edge AI? Does the distinction go beyond just speed and performance? We’ll break down these differences to help you navigate the decision-making process more effectively.

Examining the Similarities and Differences of Edge AI vs Cloud AI

Even though edge AI and cloud AI have similarities, there are differences. These differences can affect which technology is best for your organization. 

Cost: Supporting edge AI can be expensive. Businesses should also know it’s probably a long-term cost that’s expected to continue to increase. However, the same can apply to cloud AI. For businesses with high volumes of transactions in the cloud, the lifetime cloud communication cost for the device typically also increases. The decision generally comes down to figuring out which cost will be lower.Reliability/Latency: Will the device always be deployed in locations with high-speed internet, or is some fraction of the deployment going to have slow or intermittent connectivity? Edge AI removes the latency involved with any network transfer. This should be something organizations consider if they have multiple locations. Businesses in rural areas may also benefit from edge AI connectivity.Communications Networks: Does the network your device uses to communicate data carry an incremental cost? Some networks, including cellular networks, do, so developers need to find the right balance. In addition, if you have a network bandwidth-constrained system, you don’t want to be frequently sending data to the cloud because bandwidth is limited and/or network costs are expensive. Edge AI can be a more cost-effective option for businesses paying extra data usage costs. On the other hand, if your business isn’t using a ton of cloud storage, transitioning to edge AI may not be worth the cost. At least, not yet.Data Privacy: Since some applications require data to be stored on-site, would keeping certain data completely on the device increase your business’s security vulnerabilities? Even though edge AI offers the same, and sometimes more, protections than cloud AI, security risks can still exist. Some data storage options may also be limited by privacy laws and industry compliance standards. Where you’re required to store data can play a huge role in whether edge or cloud AI storage is the way to go.Power: When the edge AI sends less data to the cloud, the power drain of network communications is reduced. However, increasing the computational load with ML will increase the power drain, potentially offsetting some of your power savings.Storage: With people, machines, and “things” expected to generate a staggering 2.5 quintillion bytes of data daily, all of that data needs to be stored somewhere, and edge devices generally lack the space to do so. This is one of the biggest benefits of cloud AI. However, don’t forget about the risks, which can include cybersecurity threats.Staying at The Forefront of Innovation

Simply put there’s no one-size-fits-all answer, as edge AI and cloud AI each bring their own strengths, and tradeoffs, to the table. The best fit depends on your specific use case infrastructure and goals. 

So, whether you’re optimizing for speed, cost, privacy, or scalability, understanding these differences makes it a whole lot easier to choose the right path forward—and if you’re still in between? Hybrid models might offer the balance you’re looking for.

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