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Retrofit AI vs new AI cameras

The question is rarely whether camera AI pays off. It is whether you should throw away a working camera estate to get it.

The short version

Side by side.

AI-camera vendors make a clean pitch: replace your cameras and intelligence comes built in. The unstated invoice is the rest of the project: hardware for every view, cabling and mounting labour, VMS changes, site-by-site rollout schedules measured in quarters, and intelligence that is fixed to whatever each camera model ships with.

Retrofitting puts the intelligence in an on-site inference layer instead of the glass. Your existing ONVIF/RTSP cameras become AI-enabled in days, capability upgrades are software, and the estate you already depreciated keeps earning. The TCO Comparison tool puts both paths side by side over a multi-year horizon with your camera count.

Upfront cost: Rip-and-replace with AI camerasNew cameras, cabling, and installation for every view; CamnitiveEdge appliance sized to the site; existing cameras stay
Time to live: Rip-and-replace with AI camerasQuarters: procurement, install, commissioning per site; CamnitiveDays: connect streams, write rules, validate views
Where intelligence lives: Rip-and-replace with AI camerasFixed in each camera model’s firmware; CamnitiveIn the edge layer: new capabilities arrive as software updates
Coverage economics: Rip-and-replace with AI camerasPer-camera hardware spend to add AI to a view; CamnitivePer-camera subscription on views you already own
Disruption: Rip-and-replace with AI camerasPhysical work at height across live operations; CamnitiveNo camera-side changes; network-level integration
Estate value: Rip-and-replace with AI camerasWorking cameras written off; CamnitiveSunk CCTV cost turned into measurable return

Rip-and-replace with AI cameras compared with Camnitive
What matters	Rip-and-replace with AI cameras	Camnitive
Upfront cost	New cameras, cabling, and installation for every view	Edge appliance sized to the site; existing cameras stay
Time to live	Quarters: procurement, install, commissioning per site	Days: connect streams, write rules, validate views
Where intelligence lives	Fixed in each camera model’s firmware	In the edge layer: new capabilities arrive as software updates
Coverage economics	Per-camera hardware spend to add AI to a view	Per-camera subscription on views you already own
Disruption	Physical work at height across live operations	No camera-side changes; network-level integration
Estate value	Working cameras written off	Sunk CCTV cost turned into measurable return

Put your own camera count behind it in the TCO comparison.

When to choose which

Both have a place.

Choose rip-and-replace with AI cameras when

Your cameras are genuinely end-of-life or coverage is fundamentally wrong
A greenfield site being built from scratch
A handful of views where an all-in-one AI camera is simplest

Choose Camnitive when

A working camera estate you would rather monetize than landfill
Multi-site rollouts where quarters of installation are unacceptable
Use cases that evolve: software-defined capability beats fixed firmware

Questions

What teams ask next.

Will our older cameras work?

Most fixed IP cameras that support ONVIF or RTSP work as they are, and the FloFrame sizer checks your camera makes and VMS in its first step. Each view is checked during pilot setup.

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