Why Per-Camera Quotes Undermine ROI | by Carter Maslan | Camio
Why Per-Camera Quotes Undermine ROI
2 min read · Oct 8, 2025
Leading with per-camera pricing often obscures the real conversation: understanding business objectives and constraints.
Our engagement model starts with a discovery process to define the problem, identify meaningful outcomes, and design a solution within cost parameters. Once we’ve established the scope and validated the ROI together, we apply per-camera pricing as a planning tool — not as the starting point.
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Two approaches to enterprise video intelligence:
| Traditional Infrastructure Sales | Camio's Outcome-Focused Approach | |
|---|---|---|
| Initial Step | Request per-camera quote | Discovery workshop with stakeholders -> solution design |
| Pricing Structure | Unit cost × camera count | Fixed price tied to defined business outcomes |
| Engagement Model | Transactional relationship | Collaborative partnership with dedicated team |
| Value Framework | Cost per lens | ROI per business objective |
| Planning Work | camera count * unit cost - unknown business impact | Joint optimization around constraints and goals |
| Accountability | Customer owns implementation risk | Shared responsibility for results |
| Ongoing Relationship | Reactive support | Proactive optimization and iteration |
Why this matters for enterprise buyers:
Per-camera pricing can be appropriate for simple object detection and evidence recording purchases. But when solving operational challenges — whether that’s cutting costs, improving compliance, or optimizing operations — the unit cost per lens is just one variable in a much larger equation.
Paying $65 to spend $293:
One enterprise deployed a $65 per camera per month video system — that also required near datacenter-level GPU infrastructure on-premise— only to drown in false alarms. The vendor’s AI couldn’t distinguish staff from visitors, generating 36% false positives across hundreds of cameras. The result? At just $25/hour fully loaded, labor costs ballooned to $228 per camera per month — over triple the cost of the analytics— just to handle false alerts.
Camio solved the problem with its architecture focused on outcomes — fast intervention without adding headcount — not on a checklist of features.
Instead of brute-forcing the analysis of every video frame with the same detection models, Camio uses a multi-stage filtering pipeline that applies more expensive state-of-the-art AI models only when context demands it (e.g. when people are in restricted areas after hours). This reduces both the false positives and the costs— proving that smarter architecture, not just smarter AI models, drives ROI.
We’ve found that enterprises benefit most when we:
- Understand cost constraints upfront
- Design solutions that deliver measurable impact within those parameters
- Take accountability for outcomes, not just deployment
- Continue optimizing performance after go-live
This approach requires more discovery work initially, but it ensures the investment drives the business results that matter — and that the costs correspond to the actual value delivered rather than just infrastructure deployed.