AI video surveillance for cameras you already own is usually priced per camera stream (or per site), billed monthly or annually as software — not per new camera bought, because good platforms run on your existing ONVIF/RTSP feeds. Your real cost is driven by four things: how many camera streams you analyze, how many AI capabilities run on each stream, where the processing happens (on-prem GPU vs. cloud), and how long you retain searchable footage. There's no single sticker price, but you can estimate and compare honestly once you know those drivers — and the ROI comes from catching incidents in seconds instead of reviewing footage for hours.
What actually drives the cost?
Forget the myth that AI surveillance means ripping out cameras and buying "AI cameras." The heavy cost of a rip-and-replace is exactly what you avoid when the software ingests standard IP feeds. Here's what moves the number instead:
- Number of camera streams analyzed. Most platforms price per stream. Ten cameras and a thousand cameras are very different bills. You can also phase in — start with your highest-risk cameras.
- Capabilities running per stream. Detecting objects, vehicles, and weapons; anomalous-behavior detection; cross-camera tracking; license-plate recognition (ALPR); and plain-language search each consume compute. A platform that runs every capability in parallel on every camera continuously costs more to compute than one that runs a single model — but it also replaces several point tools.
- Where inference runs. On-prem GPU servers are a capital expense you own; cloud inference is an operating expense that scales with usage. Sensitive or air-gapped sites often require on-prem, which shifts cost from monthly fees to hardware.
- Retention and search depth. Keeping footage searchable (not just recorded) for 30, 60, or 90+ days drives storage. The longer the searchable window, the more you pay — but the more useful plain-language search becomes.
- Integration and rollout. Connecting to your VMS or C5/C4 command center, operator training, and multi-site deployment are one-time or phased costs.
Is AI video analytics worth it?
The honest answer: it's worth it when your problem is too many cameras and too few eyes. A human can watch a handful of feeds attentively; a wall of 200 monitors is effectively unwatched. If your incidents are being discovered after the fact — or found only by an operator scrubbing hours of recordings — the economics favor AI quickly.
The value isn't "AI is cool." It's that a missed incident, a slow response, or an all-night footage review has a real cost — in loss, in liability, in overtime. AI shifts you from after-the-fact review to alert in seconds.
Where the return typically shows up:
- Operator leverage. One operator can effectively oversee far more cameras when the system flags what matters and stays quiet otherwise.
- Investigation time. Plain-language search ("find a blue truck near the OXXO between 6–7am yesterday") turns an all-day tape review into a minutes-long query.
- Faster response. Weapon or anomaly detection that alerts in seconds can change the outcome of an incident, not just document it.
- Fewer point tools. One platform doing detection, tracking, ALPR, and search can replace several separate subscriptions.
- No new hardware, faster payback. Running on existing cameras removes the biggest line item, so a pilot can prove value before you scale.
How do I estimate my own cost?
You don't need a quote to sketch the math. Work through this:
- Count the streams you'd actually analyze first — not every camera you own, just the high-risk ones for a pilot.
- Decide the capabilities you need on those streams (e.g., weapon detection + ALPR + search).
- Choose deployment — on-prem (capex, control, air-gap) or cloud (opex, elastic).
- Set a searchable retention window you can defend operationally.
- Add one-time integration and training.
Then weigh that against what a single prevented loss, a faster response, or hundreds of recovered investigation-hours is worth to your operation. For most command centers and multi-site operators, that comparison isn't close.
What should I watch out for?
- Per-camera hardware lock-in. If a vendor requires proprietary cameras or firmware, you're back to rip-and-replace pricing. Insist on standard ONVIF/RTSP/HTTP MJPEG support.
- "AI" that's really one model. Ask what runs on every camera, continuously, versus what runs only on demand.
- Alert fatigue. More detections aren't better if operators drown. The system should surface the few things that matter.
- Human-in-the-loop. AI should flag and search; people should decide. That's not a limitation — it's how you keep the system defensible.
Where Axentra Omnisight fits
Axentra Omnisight is built for exactly this cost model: it runs on the cameras you already have — any standard IP camera via ONVIF, RTSP, or HTTP MJPEG — with no proprietary firmware and no vendor lock-in. It runs every capability in parallel on every camera, continuously: object/vehicle/weapon detection, anomalous-behavior detection, cross-camera tracking, license-plate recognition, and plain-language search over live and recorded footage. It watches every camera at once and alerts operators in seconds, and it's deployed in metropolitan C5 command centers, national retail chains, multi-site logistics, and industrial operations.
Because it layers on top of what you own, you can start with your highest-risk cameras, prove the value, and scale — instead of financing a hardware replacement first. Human-in-the-loop by design: Omnisight finds and flags; your operators decide.
Want a straight estimate for your camera count and use case? Talk to us — we'll size a pilot to your real footprint.