You have a camera. You have a computer vision model. Now comes the most important architectural decision you will make: does the AI run on the device itself, or does the video get sent to a cloud server for processing? The answer has massive implications for latency, cost, privacy, and reliability.
What is Edge AI?
Edge AI means running artificial intelligence models directly on the device that captures the data — whether that is a camera, a robot, a phone, or a small embedded computer like the NVIDIA Jetson. The data never leaves the device. Inference happens locally, in milliseconds, using the device’s own processor and GPU.
What is Cloud AI?
Cloud AI means sending data to a remote server — typically on AWS, Google Cloud, or Azure — where powerful GPUs process it and return the results. The device captures the data, uploads it, waits for a response, and then acts on it.
The full comparison
| Factor | Edge AI (Jetson) | Cloud AI |
|---|---|---|
| Latency | 1–20ms (local) | 50–500ms (network round trip) |
| Internet required | ❌ No | ✅ Always |
| Data privacy | ✅ Data stays on device | ⚠️ Data sent to third-party servers |
| Cost at scale | Low — one-time hardware cost | High — pay per image/minute |
| Reliability | Works in remote/offline locations | Fails without internet connection |
| Processing power | Limited (but growing fast) | Virtually unlimited |
| Setup complexity | Moderate (model optimisation needed) | Low (API calls) |
| Best for | Real-time, offline, private, industrial | Batch processing, complex models, prototyping |
When Edge AI wins
1. Real-time control systems
If your vision system needs to trigger an action — stopping a conveyor belt, alerting a security guard, adjusting a valve — you cannot afford 200ms of cloud round-trip. A 20ms edge inference can save a product from being damaged. A 200ms cloud response cannot.
2. Remote or unreliable locations
Farms, construction sites, mines, ships, aircraft, and developing markets all have unreliable or expensive internet. Edge AI runs perfectly without connectivity — including at 35,000 feet or underground.
3. Privacy-sensitive applications
Medical imaging, facial recognition in secure facilities, and industrial IP all involve data that legally or commercially cannot be sent to a third-party cloud. Edge AI keeps everything local.
4. High-volume continuous streams
Sending 60fps 1080p video to a cloud API costs thousands of dollars per month at scale. Running the same inference on a £595 Jetson kit costs nothing after purchase.
When Cloud AI wins
- Prototyping and experimentation — get started in minutes with an API call, no hardware needed
- Very large models — running GPT-4V or Gemini Vision requires cloud-scale compute
- Batch processing — processing archives of images overnight where latency does not matter
- Variable workloads — cloud scales instantly; edge hardware is fixed capacity
The hybrid approach: edge + cloud
Many production systems use both. The Jetson handles real-time detection and control locally. Only interesting events — anomalies, alerts, flagged frames — are sent to the cloud for further analysis, storage, or reporting. This gives you the best of both worlds: zero latency for control, and cloud power for deep analysis.
import paho.mqtt.client as mqtt
def process_frame(frame, model):
results = model(frame)
for det in results[0].boxes:
if det.conf > 0.9 and det.cls == DEFECT_CLASS:
# Only send high-confidence defects to cloud for logging
send_to_cloud(frame, det)
trigger_local_alarm() # immediate local actionEdge AI performance in 2026
Edge AI hardware has improved dramatically. The NVIDIA Jetson Orin Nano delivers 67 INT8 TOPS of AI performance — enough to run YOLOv8 at 55+ FPS, multiple camera streams simultaneously, and even small language models locally. The gap between edge and cloud is closing fast.
The HemiHex Jetson Inspection Kit is production-ready edge AI hardware. Deploy your first model the same day it arrives. Shop now →