Computer vision lets software "see": counting products on a shelf, spotting defects on a production line, reading number plates or recognising documents. When businesses ask "how much will it cost?", the honest answer is "it depends" — but it depends on a predictable set of factors.
1. Can you use an existing model?
The biggest cost driver is whether a ready-made service can do the job. Text extraction (OCR), general object detection and face blurring are widely available through cloud APIs. Integrating them is a normal development project. Recognising your specific parts, defects or products usually needs a custom-trained model, which costs more.
2. Data collection and labelling
Custom models need examples — often thousands of labelled images. Costs include:
- Capturing images in real conditions (lighting, angles, backgrounds)
- Labelling them accurately, sometimes by domain experts
- Handling rare cases, such as unusual defects
Data work is frequently the largest single part of a custom vision project.
3. Accuracy requirements
Going from "good" to "near perfect" accuracy is expensive. Decide early what accuracy is acceptable and what happens when the model is unsure — for example, sending uncertain cases to a person for review.
4. Where the model runs
| Deployment | Cost considerations |
|---|---|
| Cloud API | Low setup, pay per image — can grow with volume |
| Own cloud server | Server and GPU costs, more control |
| Edge device / camera | Hardware cost, works offline, low latency |
| Mobile app | Model optimisation for phones |
5. Integration with your systems
A model on its own does nothing. It needs an app, dashboard or integration with your ERP, warehouse or quality system. This software work is often similar in size to the AI work.
6. Ongoing costs
Budget for hosting, monitoring accuracy over time, and retraining when products, packaging or conditions change.
How to keep costs under control
- Start with a proof of concept on a small, well-defined task.
- Test existing APIs before training anything custom.
- Design a human-in-the-loop process for uncertain results.
- Plan integration and reporting from the start.
Have a vision idea? Tell us about it and we will help you scope a sensible first phase, or hire a Python developer.