For a chain with many locations, the core difference is where the video lives and who controls it. Cloud video surveillance (VSaaS) manages or stores video in the vendor's data centers: you don't need servers at every site and updates arrive automatically, but you depend on the internet and the vendor, and video, images or metadata leave your infrastructure. On-premise video surveillance keeps video on your own servers, with no cloud dependency, in exchange for running your own data center. If your priority is controlling the data, reusing the cameras you already have and applying AI across the whole chain without sending video out, on-premise AI is usually the better choice; if you have few sites and a small IT team, the cloud may suit you better.
What is cloud, hybrid and on-premise video surveillance?
- Cloud (VSaaS): as Verkada itself explains, cloud video storage uses remote data centers to record and retain footage instead of on-site servers or NVRs, reducing local infrastructure.
- Hybrid: video is stored on the camera or an edge device, and management happens in the cloud. Verkada describes its platform as cloud-managed, with storage on each device and metadata uploaded to the cloud for quick search.
- On-premise: video and its analysis live on servers, NVRs or a VMS inside the company, in your corporate data center or facilities.
What are the real advantages of the cloud?
To be clear: the cloud solves real problems. It removes servers and recorders from every store, centralizes administration in a web console and makes remote access easy. Verkada, for example, states that its cameras include onboard storage with up to 365 days of continuous recording in standard quality, depending on the model, and that they can be managed without local servers or NVRs. For a company with few locations and a small IT team, that simplicity matters a lot.
What happens if a store loses internet?
This is the key question for a chain with stores in areas with unreliable connectivity. In hybrid models the camera can keep recording locally during an outage and sync automatically once the connection returns. But, as a Verkada review published by ButterflyMX notes, remote viewing, AI search, alerts and cloud administration require an internet connection.
With on-premise, analysis doesn't depend on an outside service: it depends on your own network between stores and your data center. To be honest, a store still needs connectivity to your corporate monitoring center for central analysis, but that network and those servers are yours, and a cloud provider's outage doesn't stop your operation.
Cloud vs on-premise: point-by-point comparison
| Criterion | Cloud / hybrid (VSaaS) | On-premise |
|---|---|---|
| Where video lives | Vendor's data centers (or camera + cloud) | Your servers, your data center |
| Outside dependency | Internet and vendor service for management, alerts and search | No cloud dependency |
| Existing cameras | Depends on the vendor; some models are built around their own cameras | Multi-brand cameras typically integrated via ONVIF/RTSP |
| Infrastructure | Minimal on site | Servers (with GPUs for AI) and a team to run them |
| Cost model | Recurring subscription | Server investment plus licenses |
| Updates | Automatic, handled by the vendor | Planned by your team or integrator |
| Control and auditing | Under the vendor's policies | Under your policies and your logs |
What fits a chain with hundreds or thousands of stores?
There is no universal answer. Before deciding, work through these with your security, loss-prevention and IT teams:
- Where must the video stay? Consider your internal policies and the applicable personal-data protection framework; every company must comply with the one that applies to it.
- How many camera brands do you run? Chains that grew in stages may have equipment from several manufacturers. Replacing all of it to adopt a platform costs time and money.
- What upload bandwidth do your stores have? Streaming continuous video from hundreds of sites to an outside service is not the same as sending only events or metadata.
- Do you have a data center and an IT team? On-premise means running servers; if you don't want to, the cloud or a turnkey provider are alternatives.
- What do you want the AI to do? Detecting blocked cameras, searching archived video, alerting on your own rules or following a person across cameras are different needs.
Where does Axentra OmniSight fit?
OmniSight is the option for teams that conclude video should stay in-house but still want modern AI across the whole chain:
- Video stays inside the company: installed on-premise on GPU servers in your data center. Video never leaves the organization and it does not depend on cloud services.
- Works with what you already have: IP cameras via ONVIF and RTSP and brands such as Hikvision, Dahua, Axis, Hanwha, Uniview, Bosch, Avigilon, Verkada and Cisco, plus VMS such as Milestone and Genetec. It unifies cameras from different brands in one VMS, without replacing equipment.
- The whole chain, 24/7: analyzes cameras in real time and alerts the monitoring-center operator.
- Camera health: automatically detects cameras that are blocked, turned or out of service across every location.
- Natural-language rules: for example, "alert me if you see an accident", plus natural-language search across archived video.
- Investigations: tracks people and vehicles across cameras and generates their route; reads license plates, fleet numbers and vehicle lettering, useful at distribution centers.
- People in charge: every face-recognition match against watch lists is reviewed by a human operator, and its use must follow the applicable legal framework.
What we won't hide: OmniSight requires GPU servers in your data center. If you'd rather not build that infrastructure on your own, Axentra also delivers turnkey projects (server supply, integration, installation, maintenance and support), but the model remains on-premise, not SaaS.
Licensing is based on floating licenses per processed camera, per year.
How can you test it without risk?
OmniSight offers a free 10-day pilot with up to 10 of your cameras. It's a direct way to compare against what a cloud vendor offers you today: same stores, same cameras.
Want to assess whether on-premise AI makes sense for your chain? Let's talk about your case.