1. The architecture choice that shapes everything else
Every AI video analytics deployment lives or dies on one architectural decision: where does the inference run? Cloud is simpler to deploy, harder to keep within bandwidth budget. Edge is faster and more compliant, with higher up-front capex. Most 2026 deployments end up hybrid — but the way you balance the two determines latency, cost, compliance, and whether the system survives a site outage.
2. The five trade-offs side by side
| Dimension | Edge AI | Cloud AI |
|---|---|---|
| Detection-to-actuation latency | 100–250 ms | 400–800 ms |
| Bandwidth per camera | Local only — alerts + thumbnails uplink | 2–8 Mbps continuous |
| Up-front capex | GPU appliance per cluster | Near-zero |
| Recurring opex | Software subscription only | Software + bandwidth + inference |
| Data residency / compliance | Raw video stays on-site | Needs region-resident inference endpoint |
| Multi-site rollup | Requires central dashboard tier | Native |
| Resilience to internet outage | Continues running | Degrades or stops |
3. When edge is the right call
- Any actuation use case where latency < 300 ms matters — fire-detection relays, boom barriers, fall-detection alarms, dock-edge proximity.
- Sites with poor or expensive bandwidth — remote warehouses, ports, mines, oil + gas.
- Air-gapped industrial sites with regulator-mandated local-only data.
- Dense camera counts (> 30 per site) where cloud bandwidth becomes a cost issue.
- India DPDP / GCC PDPL deployments where raw video must stay in-country.
4. When cloud is the right call
- Many small distributed sites (retail chain with 4–8 cameras per store).
- Use cases tolerant of 500 ms latency — footfall, sentiment, customer experience, queue management.
- Customers without IT capacity for on-site GPU hardware.
- Fast-iterating analytics where the value is centralised reporting, not real-time actuation.
5. The hybrid pattern most enterprises ship
In production VIZO361° deployments, the most common architecture is:
- Edge inference for fire / smoke detection, ANPR, intrusion, fall / dock-edge, PPE compliance — anything tied to relay actuation.
- Cloud aggregation for the multi-site dashboard, policy engine, incident tracker, and supervisor mobile app.
- Local cache + store-and-forward at every site so a 2-hour internet outage does not lose alert history.
- Model updates pushed centrally; rolled out per-site behind a feature flag.
This is the architecture VIZO361° ships by default. Customers can override toward pure-edge or pure-cloud per site as compliance / network constraints dictate.
6. The cost crossover
For a typical site with 8 cameras, cloud is cheaper across years 1–3. For a site with 30+ cameras, edge breaks even in year 1 and saves substantially from year 2 onward as bandwidth charges compound. Use the VIZO361° ROI calculator to plug in your camera count and current cost baseline.
FAQs
What is the difference between edge and cloud AI video analytics?
Edge AI video analytics runs the inference model on a GPU appliance physically located at the camera site. Cloud AI video analytics streams the video feed to a remote GPU pool for inference. Edge minimises bandwidth + latency and keeps raw video local; cloud centralises analytics + model updates and simplifies multi-site rollouts.
Which is faster — edge or cloud?
Edge wins on detection-to-actuation latency (typical 100–250 ms vs 400–800 ms in cloud). For high-stakes actuation — fire-detection relays, boom-barrier opening, fall alerts — edge is the default. Cloud is fast enough for footfall analytics, sentiment, customer-experience use cases.
Which costs less?
Depends on camera count and bandwidth cost. Edge has higher up-front capex (GPU appliance per cluster of 8–32 cameras) but no ongoing bandwidth charges. Cloud has zero on-site hardware but ongoing bandwidth and inference cost. Crossover is typically at 15–25 cameras per site over 3 years.
What about data residency, DPDP and GDPR?
Edge keeps raw video on-site, simplifying DPDP / GDPR / PDPL compliance. Cloud needs a region-resident inference endpoint (Mumbai for India, Bahrain or Dammam for GCC) plus a documented transfer mechanism. Both can comply; edge gives faster compliance sign-off.
Can you mix edge and cloud in one deployment?
Yes — hybrid is the default in 2026. Sites with reliable bandwidth (HQ, malls, urban warehouses) lean cloud; remote sites (mines, oil + gas, off-grid logistics) run edge. VIZO361° supports both under one platform and one policy engine.
Where does VIZO361° sit?
Hybrid by default. The standard deployment is an edge GPU appliance per camera cluster, with the policy + dashboard + multi-site rollup in the cloud. Customers choose pure-edge for air-gapped sites and pure-cloud for low camera-count distributed deployments.