Published on August 8, 2025
As the world becomes more interconnected, the decision of where to run AI agents—on the edge or in the cloud—has become a critical one. Both options have their merits, and in many cases, businesses face the challenge of determining which is the best fit for their needs. Each environment offers distinct advantages in terms of latency, performance, scalability, and cost.
At Vortex IQ, we’ve conducted an in-depth benchmarking study to compare the performance, cost, and efficiency of running agents on the edge versus the cloud. This blog will explore the key findings of this benchmark and provide insights into which solution is best suited for different use cases.
Before diving into the comparison, let’s first define what edge and cloud computing are and how they relate to running AI agents.
When it comes to running AI agents, edge computing enables faster decision-making by reducing latency, while cloud computing offers greater scalability, storage, and processing power.
For our benchmarking study, we ran AI agents in both edge and cloud environments under identical conditions to measure their performance. Here are the key parameters we tested:
Reliability and Availability: What happens when connectivity issues occur? How does each environment handle failures, downtime, or disruptions?
One of the most significant advantages of edge computing is low latency. Because the data is processed locally, there is no need for time-consuming communication between the user device and distant cloud servers. This is especially important for applications where milliseconds matter, such as:
Benchmark Results:
Conclusion: Edge computing significantly reduces latency and is ideal for applications requiring real-time responses.
While edge computing is superior for low-latency applications, the cloud shines when it comes to high throughput. Cloud environments offer virtually unlimited computational resources, making them more suitable for heavy data processing tasks and large-scale automation.
For example, cloud computing is ideal for applications like:
Conclusion: Cloud computing is better suited for high-throughput tasks and situations that require processing large datasets or handling complex workflows.
As the demand for agent tasks increases, cloud environments shine in their ability to scale seamlessly. Cloud services can dynamically allocate resources based on demand, which makes scaling up or down virtually effortless.
In contrast, edge devices are typically limited by their hardware capacity. While edge computing can be more efficient for specific tasks, it becomes challenging to scale beyond the capacity of the local device.
Conclusion: Cloud computing offers unparalleled scalability, making it ideal for large-scale operations where task volumes are unpredictable or rapidly growing.
Cost is an important consideration when deciding between edge and cloud solutions. While edge computing requires an upfront investment in hardware, the ongoing costs are generally lower than cloud computing, especially when dealing with smaller volumes of data or tasks.
On the other hand, cloud computing operates on a pay-as-you-go model, which can lead to higher long-term operational costs, especially at scale.
Conclusion: Edge computing is more cost-efficient for smaller-scale operations or when processing is focused on low-latency, high-speed tasks. Cloud is more suitable for larger-scale, high-throughput applications but comes with higher operational costs.
While edge computing offers independence from the internet and is resilient in cases where connectivity is poor, cloud environments have their own advantages when it comes to reliability and availability. Cloud platforms are often hosted across multiple data centers with built-in redundancies, making them more resilient in cases of failures or outages.
Conclusion: For applications where uptime and availability are critical, cloud computing offers more reliability, especially for businesses with global operations.
When deciding whether to run AI agents on the edge or in the cloud, it’s important to understand your specific needs and use cases. Here’s a quick breakdown of when each solution is most appropriate:
In many cases, a hybrid approach that leverages both edge and cloud solutions might be the most effective. By running critical, low-latency tasks on the edge and leveraging the cloud for high-throughput, data-intensive operations, you can maximise both performance and cost efficiency.
Both edge and cloud computing have their own strengths and weaknesses when it comes to running AI agents. By benchmarking their performance in real-world scenarios, we’ve seen that edge offers unbeatable low-latency performance, while the cloud excels in scalability, throughput, and reliability.
As your business scales and automation needs grow, the key takeaway is that the architecture of your system should evolve to take advantage of the best of both worlds. By understanding the strengths of edge and cloud computing, you can make the right choice to meet your business’s unique demands.
The future of e-commerce optimisation—and beyond—is bright with Vortex IQ. As we continue to develop our Agentic Framework and expand into new sectors, we’re excited to bring the power of AI-powered insights and automation to businesses around the world. Join us on this journey as we build a future where data not only informs decisions but drives them, making businesses smarter, more efficient, and ready for whatever comes next.