Choose edge when
The robot must react on the spot, the plant or warehouse connection is unreliable, or images must not leave the premises.
Guide
Edge AI vs. cloud: when computer vision should run on the robot
Short answer
Computer vision should run on the robot (Edge AI) when the robot must decide instantly, when the connection is not stable, or when video must not leave the plant. The cloud fits better for training models, analyzing historical data and managing many devices. In practice, a hybrid setup is common: inference on the robot, training and monitoring in the cloud.
Updated October 11, 2026
What Edge AI is
Edge AI means running AI models on the device that produces the data, instead of sending that data to a remote server. On a robot, the camera captures the image and an onboard computer, such as an NVIDIA Jetson module, runs the vision model and passes the result straight to the control system.
Model training is normally done off the robot. What runs on the robot is inference: applying the already trained model to each new image.
Edge AI vs. cloud: comparison
| Criterion | Edge AI (on the robot) | Cloud |
|---|---|---|
| Latency | Low: the image is processed onboard, with no trip over the network. | Higher: adds the network round-trip time and depends on network conditions. |
| Connectivity dependence | Works even if the connection drops or is intermittent. | Needs a stable connection; without it, vision stops. |
| Data privacy and bandwidth | Video is processed locally and only results or selected samples leave. | Images or video must be sent out, which uses bandwidth and exposes data outside the plant. |
| Hardware cost | Each robot needs its own inference computer, which raises the cost per unit. | The robot needs less onboard compute; cost shifts to cloud service usage. |
| Model updates | Each new version must be rolled out to every device. | Updated in one place; all devices use the new version. |
| Scale | Each robot adds its own compute; load is not concentrated on one server. | Scales with shared resources, but network traffic and service usage grow. |
When to choose edge, cloud or hybrid
Choose cloud when
The task is not sensitive to response time, you have a stable connection and want centralized analysis, or you need to train and review models with large amounts of data.
Choose hybrid when
You want immediate decisions on the robot and also a central view: the robot infers locally and sends results or samples to the cloud to retrain, monitor and update models.
Typical applications on robots
Edge vision is mostly used in these tasks.
Warehouse picking and sorting
The robot identifies and separates products with local vision.
Production-line quality inspection
Defect detection directly on the equipment that inspects.
Reading labels and codes
Part counting and label and code reading with Edge AI.
These tasks apply to different kinds of robots: AMRs (autonomous mobile robots), robotic arms and cobots. To run models onboard, inference computers such as NVIDIA Jetson modules are used.
How AUGEVI approaches it
At AUGEVI we build the eyes and brain of your robot: computer vision models that run on the robot itself, on NVIDIA Jetson, not in the cloud. They work on AMRs, arms and cobots.
The process has four steps: capturing data from your real operation, training and validating the model, optimized deployment on Jetson, and monitoring with adjustments from new data.
Keep reading
Frequently asked questions
What people ask us before getting started
What is Edge AI in robotics?+
It means running AI models, such as vision models, on the robot itself instead of on a remote server. The robot processes what it sees with an onboard computer and decides without depending on the network.
Does on-robot vision work without internet?+
Yes, inference runs onboard, so the robot can keep seeing and deciding even if the connection drops. What does need a network is sending results to external systems or downloading new model versions.
Can Edge AI be combined with the cloud?+
Yes, it is a widely used hybrid setup. The robot runs inference locally and the cloud is used to train, monitor and distribute updated models.
Is Edge AI more expensive than the cloud?+
It depends on the operation. Edge AI needs an inference computer on every robot, while the cloud shifts cost to service usage and data traffic. Compare both against the number of devices and the video volume in your case.
What hardware is used for Edge AI on robots?+
Compact inference computers that fit inside the robot, such as NVIDIA Jetson modules. At AUGEVI we deploy optimized models on Jetson.
Where should your robot's vision run?
20 minutes. We review your case and tell you whether Edge AI, cloud or a hybrid setup is the right path.