From processing transactions to managing inventory, a retail business needs a reliable AI infrastructure. Edge AI and Cloud AI have their unique benefits and drawbacks that you’ll need to be aware of. For POS resellers, it may become challenging to find the right infrastructure for a retail business’s AI POS system.
In this article, you’ll learn the differences between Edge AI and Cloud AI. Once you know their capabilities and requirements, you’ll be able to choose the right infrastructure for high-traffic retail businesses.
Comparison of Features: Edge AI vs. Cloud AI
| Feature | Edge AI | Cloud AI |
| Need for Internet Connection | Low | High |
| Latency | Low | High (depends on the network connection) |
| Computing Power | Low to Moderate (depends on the device’s hardware) | High |
| Storage Capacity | Low to Moderate (depends on the device’s hardware) | High |
| Data Privacy | Less risk | Vulnerable to cyberattacks |
| Initial Costs | High | Low |
| Operational Costs | Low to Moderate (depends on the hardware) | High (scales with the data transmission requirements) |
| System Reliability | High | Moderate (depends on the network connection) |
| Scalability | Low to Moderate (depends on the hardware) | High |
What is Edge AI?
Edge AI is deployed directly to a POS device or to a local server. Unlike Cloud AI, it doesn’t rely on a remote server or cloud computing for its AI processes. By computing on the edge of a network, Edge AI provides real-time data. It even enables retail businesses to access its AI capabilities offline. However, Edge AI requires powerful hardware to do so.
What is Cloud AI?
Cloud AI uses a cloud platform specifically designed for its processes. From machine learning to other complex AI tasks, Cloud AI doesn’t require on-premises servers. Retail businesses can easily deploy AI in POS systems and process large amounts of data using Cloud AI. Because of this, Cloud AI also requires specialized hardware to support a business’s needs.
Need for Internet Connection
Caption: AI POS systems need reliable internet connections to transmit and analyze real-time data [Image: Nory Agentic AI]
An AI infrastructure’s internet reliance affects the AI POS system’s reliability. One of the benefits of an integrated AI POS system is its ability to centralize a retail store’s operations. Both Edge AI and Cloud AI can do this.
When choosing the AI framework for a retail business, you’ll need to determine the stability of the network connection. Ethernet can also help with this. Luckily, AI POS systems have Ethernet ports.
Edge AI Bandwidth
Edge AI can still be used without an internet connection. It can continuously learn and execute tasks with low bandwidth because it relies on the local POS device. Aside from this, devices with a 5G connection help a retail business have low bandwidth.
Cloud AI Bandwidth
On the other hand, Cloud AI requires a continuous internet connection for its tasks. It relies on stable network connections to access its virtual compute resources. Without this, a retail store’s AI POS system won’t be able to train or process data.
Latency
An AI POS system should support a retail business’s operations in real-time. To do this, POS hardware should reduce latency as much as possible. When choosing between Edge AI and Cloud AI for a retail business, you’ll need to know its latency requirements.
Edge AI Latency
Edge AI relies on the POS system for its processes. Because it performs its workloads and data processing locally, it reduces latency. This also contributes to faster data transfer and responses.
Cloud AI Latency
On the other hand, Cloud AI’s dependence on remote servers for its data transfers and processes may increase latency. The average cloud platform latency is 100milliseconds. When delays happen, high-traffic retailers using Cloud AI POS systems are affected. Moreover, larger models and more complex tasks may be even more vulnerable to delays.
Helpful Hardware to Reduce Latency
A GPU is one of the essential hardware requirements to run next-gen AI POS software. It helps reduce latency for both infrastructures.
You’ll need to ensure the GPU is connected to a low-latency communication system and accelerators. The right combination of POS hardware for high-traffic retail businesses helps minimize latency.
However, you’ll need to determine the scale of data a retail business aims to process. This will help you identify the right specifications of the AI POS system’s GPU and other hardware.
Computing Power
Caption: AI cameras are used to identify menu items and make the customer checkout process quicker [Image: Bimi AI POS System]
AI in a POS system needs computing power to process data and support a business’s operations. The right CPU, GPU, and other hardware help these processes and even accelerate them. When choosing between Edge AI and Cloud AI, the system’s processing power should:
- Process large amounts of data
- Reduce time needed for complex calculations
- Manage algorithms
- Conduct deep learning
Edge AI Processing Power
Edge AI relies on the device’s hardware for its computing power. Because of this, it requires GPU, CPU, and other specialized hardware for its computing power. However, less-than-ideal hardware can limit Edge AI’s processes. Because of this, you’ll need to choose high-powered hardware for a retail business’s operations.
Cloud AI Computing Power
Cloud AI relies on its virtual resources for its computing power. Because its processes don’t depend on its hardware, it offers more computing power. As long as there is an internet connection, Cloud AI can do processes such as:
- Predictive analytics
- Business operations optimization (even across multiple locations)
- Training and deployment of complex AI models
- Quicker management of complex algorithms
- Execute chatbot functionalities
- Use natural language processing
- Automate model training engines
- Analyze images and videos
- Conduct real-time data analytics
Storage Capacity
AI POS systems need large amounts of storage. From training to executing tasks, more data enables a system’s AI to become smarter.
In a busy retail store, a POS system is used for transaction processing, inventory management, and other processes. An AI POS system that handles data from a business’s operations needs enough storage to function properly.
Edge AI Storage
The amount of data an Edge AI system can process depends on the POS device. You’ll need to check the RAM, memory, SSD, and other hardware to ensure the POS system has enough storage.
This is especially true if a retailer uses IoT to collect data from its cameras and similar sources. With the addition of IoT, the POS system’s Edge AI requires more storage.
The downside to an Edge AI framework is its limited storage capacity. While expandable memory and storage can be used, the system’s processing power still depends on storage. The good news is most AI POS systems can function with 8GB to 16GB of storage.
Cloud AI Storage
Cloud AI provides larger storage capacity than Edge AI because it isn’t constrained by local hardware. If a retail business uses local hardware for this, it is very expensive. It processes very large amounts of data for training and deep learning through its network. In fact, even Edge AI initially trains its models on cloud infrastructure.
To support Cloud AI’s workloads, its POS hardware needs specialized GPUs and accelerators. The right hardware setup enables it to continuously process heavy workloads. Retail businesses that require data-heavy and complex models are better suited for Cloud AI infrastructures.
Data Privacy
Data privacy should be prioritized when selecting the infrastructure for a retail store. Efficient transaction processing is one of the benefits of POS systems. This involves ensuring customer data, especially card information, is protected. When an AI POS system transmits data, the infrastructure should be secure.
Edge AI Data Security
Edge AI has better security protocols than cloud AI. Because data is processed in the device or on the edge, this reduces the risk of security breaches. Edge AI’s local data processing also prevents cyberattacks.
Cloud AI Data Privacy
Data transmission in a cloud infrastructure is more susceptible to security concerns. When data leaves the AI POS system, it becomes vulnerable to cyberattacks and security breaches.
For example, hackers may infiltrate the cloud-based software. When this happens, they can access sensitive customer data. Retail stores need to follow Payment Card Industry Data Security Standards (PCI DSS). Security breaches may lead to fines and penalties that affect the retail business’s operations and credibility.
Initial and Operational Costs
An AI system needs hardware, power, and other requirements to function properly. You’ll need to understand the initial and ongoing costs to help retail stores find the right framework. AI POS systems automatically lower operational costs by increasing efficiency.
These initial costs include:
- Hardware (including accelerators)
- Modules
- Servers
- Training (especially for Edge AI)
- Better bandwidth framework (especially for Cloud AI)
On the other hand, you’ll need to factor in ongoing costs such as:
- Power consumption
- Maintainance
- Internet
- Cloud storage (for Cloud AI)
- Data transmission (for Cloud AI)
Edge AI and Cloud AI provide different benefits to a retail business. When comparing their operational costs, you’ll need to look at the retail business’s needs.
Edge AI Costs
Edge AI’s capabilities depend on the device. Because of this, retail businesses need to invest in its hardware. Because of this, it has higher initial costs than Cloud AI. GPUs, NPUs, and other components are designed for efficient energy consumption without sacrificing performance. AI modules and accelerators are also part of Edge AI’s initial costs.
Ongoing Costs for Edge AI
While retail businesses can expect more upfront expenses, Edge AI’s ongoing costs are lower. Power consumption is one of the ongoing costs of Edge AI POS systems. The good news is that NPUs ensure an Edge AI POS system consumes power efficiently.
Thanks to its local data transmission, there are no ongoing costs for this. Similarly, retail stores can expect minimal costs for bandwidth requirements.
However, device maintenance is needed to maximize the use of an Edge AI infrastructure. Retail businesses need to ensure their devices are well-maintained and fix any issues immediately.
Cloud AI Costs
Unlike Edge AI, Cloud AI requires less initial investment in hardware. But its cloud computing requires higher initial costs to ensure better bandwidth, including a stable internet connection.
Cloud AI integration needs dedicated engineers to set up the system. This is especially true for retail businesses that want to process very large amounts of data and have specific requirements.
Ongoing Costs for Cloud AI
Due to Cloud AI’s large-scale data processing, its ongoing costs mostly go to data transmission. Aside from this, cloud compute and storage costs are needed to support this. When a business needs to process more data, these costs scale as well. Typically, Cloud AI has higher ongoing costs than Edge AI.
System Reliability
Retail businesses need reliable POS systems to support their operations. You’ll need to ensure their infrastructure has minimal downtime and delays. Bandwidth and latency are factors you’ll need to factor in when choosing between Edge AI and Cloud AI.
In addition, software updates improve a system’s reliability. You’ll need to ensure these are easily deployed to a retail business’s AI POS system.
Edge AI’s Reliability
Thanks to Edge AI’s low latency and bandwidth requirements, it has more reliable performance than Cloud AI. Even without an internet connection, an Edge AI system can still perform its tasks and support a retail business. Edge AI ensures high-traffic retail businesses have a reliable AI POS system even in demanding work environments.
However, system updates tend to be more difficult with Edge AI. Because of its local framework, deployment of system updates needs to be done manually on each device. Retail businesses that use many Edge AI devices require more time and effort to update their system.
Cloud AI’s Reliability
The main drawback for Cloud AI infrastructure is its dependence on a stable internet connection. The initial investment in better bandwidth and network connection can minimize disruptions. However, there is no guarantee that the infrastructure itself can fully avoid these challenges.
On the other hand, system updates are easier to deploy in Cloud AI. These updates can be instantly deployed on its cloud infrastructure, even across many devices.
Scalability
When choosing the right infrastructure, you’ll need to think of your customer’s business goals. Moreover, a retail business’s AI POS system should support its current and future processes. You’ll also need to determine how an infrastructure affects the AI POS system’s scalability.
Edge AI’s Scability
Similar to Edge AI’s computing power, its scalability depends on its hardware. Because of this, investing in powerful components ensures the framework keeps up with a business’s goals.
Hardware upgrades may be necessary to improve the system’s features. Luckily, IoT platforms are new advancements that improve the scalability of Edge AI systems.
Cloud AI’s Scalability
Cloud AI scales better than Edge AI. Its ability to process larger datasets enables it to refine an AI POS system’s features and processes. Integrations are also easier to deploy because of its cloud framework.
Aside from refining processes, Cloud AI also improves the system’s AI workloads. It can easily accommodate more data analysis and other complex tasks. Even if a retail business’s operations change dramatically, its infrastructure can easily adapt.
How to Choose Between Edge AI and Cloud AI
Caption: Bimi uses RK3576 to power its AI POS system with Edge AI [Image: Android RK3576 for Bimi AI POS System]
The right infrastructure for a retail business should have reliable performance and support its processes. Similarly, an AI POS system’s hardware should work together to support the AI workloads.
You’ll need to determine how much computing power a retail business needs. This includes understanding its daily processes and the extent of AI tasks it requires. Lastly, initial and ongoing costs also contribute to your decision.
Edge AI in a Retail Environment
Edge AI is a good choice for retail stores that use powerful hardware such as AI cameras and smart scanners. The combination of AI-enabled hardware and AI-assisted data analytics enables businesses to automate checkout and inventory management.
This application of Edge AI can be seen with Bimi’s AI POS systems. Let’s use a Bimi AI POS system using an RK3576 chip as an example. This CPU enables the system to run on an Edge AI infrastructure.
It can perform advanced processes such as AI food recognition that uses an AI camera to correctly identify an item. Even without an internet connection, the AI POS system can speed up the checkout process using Edge AI.
Cloud AI in a Retail Business
Caption: AI POS systems can help improve a business’s customer interactions [Image: Nory Agentic AI]
Cloud AI is a good choice for retail businesses that prioritize intensive data analytics and personalized customer service. Because Cloud AI processes large datasets, it is a good choice for:
- Predictive analytics
- Sentiment analysis
- AI model optimization
- Workflow automation
- Supply chain optimization
Moreover, its chatbot capabilities are more powerful than Edge AI. It offers more personalized and natural customer interactions. This includes tailoring product recommendations based on a customer’s preferences.
Building AI POS Systems Designed for the Future
The choice between Edge AI and Cloud AI depends on a retail business’s processes and requirements. Those that prioritize reliability are a better fit for an Edge AI system. On the other hand, retail stores that need to process large datasets and scale should choose Cloud AI. As a POS reseller, you can find the right infrastructure for a retail business now that you know their differences.
If you’re looking for an Edge AI POS system, Bimi is your answer. Bimi uses Edge AI technology to enable its AI POS systems to make a retail business’s processes more efficient. From food recognition to built-in AI cameras, its powerful hardware supports high-traffic retail stores. For more information, contact a Bimi expert today.
Frequently Asked Questions (FAQs)
1. Should retail businesses that need reliable data transmission choose Edge AI?
Yes, retail businesses that need reliable data transmission should choose Edge AI. Edge AI’s data transmission tasks are performed within the device or on the network’s edge. Because of this, the infrastructure can still transmit data even without an internet connection.
Edge AI is a better choice for retail businesses that want to avoid system interruptions or downtime. Edge AI POS systems have Ethernet ports and 5G networks for continuous data transmissions. This is one of the advantages Edge AI has over Cloud AI, which relies on a stable network connection.
2. Which AI infrastructure should you choose if you need greater computing power?
Cloud AI has greater computing power than Edge AI. First, it is able to train models using larger amounts of data. Its cloud platform enables it to do so. Without being limited by the system’s hardware, Cloud AI can take on more complex workloads. A retail business that needs simultaneous AI processes benefits from Cloud AI.
On the other hand, Edge AI’s computing power depends on the POS device’s hardware. Even with powerful components, its hardware limits its processing capacity. If a retail business needs to manage complex algorithms, Cloud AI is a better choice.
3. Should you choose Edge AI if you want lower power consumption?
Yes, Edge AI consumes less power than Cloud AI. It uses NPUs and other specially designed components for energy efficiency. These components ensure an AI POS system won’t overheat while being used in a retail store.
While Edge AI has better energy efficiency, its hardware is built to ensure the AI POS system functions smoothly. The right combination of components in an Edge AI system ensures a retail business’s operations won’t slow down.
4. Which AI infrastructure has less risk of security breaches?
Edge AI has less risk of security breaches because of its local processing. When data is processed within the device, it can’t be intercepted during transmission. This ensures sensitive information, including credit card information, cannot be stolen easily.
When the risk of cyberattacks is reduced, a retail business ensures its PCI Compliance. Thanks to this, it can avoid expensive fines and penalties from security breaches.
5. Which AI infrastructure is a better choice for retail businesses that prioritize faster checkout?
Edge AI is a better choice for retail businesses that prioritize faster checkout. It has lower latency and provides faster real-time responses.
More importantly, Edge AI commonly uses IoT for item recognition capabilities. This enables AI cameras to scan and identify items during a customer’s checkout. This reduces waiting times and increases efficiency.
6. Does Cloud AI have lower ongoing costs than Edge AI?
No, Cloud AI has higher ongoing costs than Edge AI. While Cloud AI can process larger amounts of data than Edge AI, this causes it to have higher ongoing costs. Retail businesses need to factor in a reliable internet connection as well.
As a retail business’s data transmission needs increase, its costs follow suit. Complex AI workloads require a business to spend more on:
- Data transmission
- Cloud compute
- Storage
7. Should you choose Cloud AI for smoother system updates?
Yes, you should choose Cloud AI for smoother system updates. Cloud AI instantly deploys system updates into the AI POS system. This ensures security and POS features are automatically reflected. Retail businesses with multiple locations will have an easier time maintaining their AI POS systems.
On the other hand, Edge AI systems require businesses to manually update their devices. This requires a lot of time and effort, especially for high-traffic retail businesses.
8. Should a retail business choose Cloud AI if it prioritizes personalized customer service?
Yes, retail businesses should choose Cloud AI for personalized customer service. Its ability to process large amounts of data enables it to analyze more customer information. This helps it refine customer profiles so it can tailor its interactions with each customer. Cloud AI is also better at providing more personalized product recommendations.
Similarly, retail businesses that rely on chatbots should consider Cloud AI. Its chatbots can converse more naturally and adapt to the customer’s preferences.
9. Does Cloud AI provide more scalability than Edge AI?
Yes, Cloud AI provides more scalability than Edge AI. Additional features and models are easier to deploy to Cloud AI POS systems using cloud platforms. Retail businesses that need to quickly and constantly adapt to their fast-paced operations benefit from this.
Unlike Edge AI, you can instantly deploy APIs and other changes to Cloud AI. Its scalability isn’t limited because of its hardware. Because of this, retail stores can easily improve their AI POS system using Cloud AI.
10. Is Edge AI a better choice for low latency?
Yes, Edge AI is a better choice for an infrastructure with low latency. Even very small delays in data transmission can affect a retail business’s operations. Since Edge AI processes data on the edge of a network, delays are minimized.
An Edge AI POS system’s components help reduce latency. Its GPU ensures it can handle heavy workloads and support business operations without slowing down.
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