Where inference runs
On-premise (edge) analytics
On a server or appliance at the site, usually GPU-accelerated
Cloud video analytics
In the provider's data centers
Comparison
On-premise vs cloud video analytics: where should AI run?
Short answer
On-premise video analytics runs AI models on servers or appliances at the site, so video never needs to leave the local network. Cloud video analytics uploads video or clips to a provider that runs the models remotely. On-premise favors bandwidth, privacy and resilience; cloud favors minimal on-site hardware for small, well-connected sites.
Side by side
On-premise (edge) analytics vs Cloud video analytics
| Dimension | On-premise (edge) analytics | Cloud video analytics |
|---|---|---|
| Where inference runs | On a server or appliance at the site, usually GPU-accelerated | In the provider's data centers |
| Bandwidth | Low; only events and metadata need to leave the site | High; video or frames must be uploaded for analysis |
| Alert latency | Local; does not depend on the internet round trip | Depends on upload speed and the round trip to the cloud |
| Privacy and data residency | Video stays inside the site network | Video is transmitted to and processed by a third party |
| Internet outage | On-site detection and local alerting continue; remote notifications depend on the link | Analysis stops or degrades until the link returns |
| On-site hardware | Requires a node with GPU capacity sized to cameras and detections | Minimal; cameras plus, for some services, a gateway or bridge |
| Scaling to more cameras | Add node capacity per site | Add cloud capacity and upload bandwidth per site |
| Multi-site oversight | Needs a central layer that collects events from each site | Central by design |
Bandwidth
On-premise (edge) analytics
Low; only events and metadata need to leave the site
Cloud video analytics
High; video or frames must be uploaded for analysis
Alert latency
On-premise (edge) analytics
Local; does not depend on the internet round trip
Cloud video analytics
Depends on upload speed and the round trip to the cloud
Privacy and data residency
On-premise (edge) analytics
Video stays inside the site network
Cloud video analytics
Video is transmitted to and processed by a third party
Internet outage
On-premise (edge) analytics
On-site detection and local alerting continue; remote notifications depend on the link
Cloud video analytics
Analysis stops or degrades until the link returns
On-site hardware
On-premise (edge) analytics
Requires a node with GPU capacity sized to cameras and detections
Cloud video analytics
Minimal; cameras plus, for some services, a gateway or bridge
Scaling to more cameras
On-premise (edge) analytics
Add node capacity per site
Cloud video analytics
Add cloud capacity and upload bandwidth per site
Multi-site oversight
On-premise (edge) analytics
Needs a central layer that collects events from each site
Cloud video analytics
Central by design
Decision guide
Which should you choose?
Choose on-premise analytics if…
- You want analytics on many cameras per site
- Upload bandwidth is limited, costly or unreliable
- Video must not leave the site
- Detection must keep running during internet outages
Choose cloud analytics if…
- Sites have only a few cameras and strong, reliable connectivity
- You cannot place or maintain any compute hardware on site
- Only short clips or snapshots need occasional analysis
OrionAlerts
Where OrionAlerts fits
OrionAlerts runs AI video analytics on premises. Inference happens on the on-site Nebula OS node with GPU acceleration, sized per deployment to the channel count and the detections running, and analytics use the same decoded video that the node records.
For multi-site estates, Orion Cloud adds the central layer, synchronizing health status, alerts, configuration and operational metadata so teams get one command view without uploading raw video. Alerts reach the dashboard, mobile app and email.
FAQ
On-prem vs cloud video analytics: common questions
How much bandwidth does cloud video analytics need?
It depends on how much video is sent. Continuous analysis needs each camera's stream uploaded, which adds up quickly across cameras and sites. Services that analyze only motion-triggered clips or snapshots use far less, but can miss events between uploads. On-premise analytics sends only events and metadata off site.
Which costs less, on-premise or cloud video analytics?
It depends on scale. Cloud analytics avoids buying on-site compute but adds subscription and upload bandwidth costs that grow with every camera. On-premise analytics needs a node with enough GPU capacity at each site, but its running costs do not depend on uploading video. Compare the total cost for your camera count and sites.
Can on-premise and cloud approaches be combined?
Yes. A common hybrid design runs detection on site and sends only events, health and metadata to a central cloud layer for multi-site oversight. OrionAlerts works this way: each site runs its own Nebula OS node, and Orion Cloud provides one command view of health, alerts, assets and maintenance without uploading raw video.
Other comparisons
Talk through your architecture
We will look at your sites, cameras and connectivity and show how Nebula OS would be deployed.