“AI development” gets quoted anywhere from a few thousand dollars to well into six figures, and the spread isn’t random — it maps pretty directly to a handful of factors that most cost guides skip over. I’ve built production computer-vision systems (real-time object tracking with OpenCV, among others), so here’s what actually moves the number.
The first question: do you need a custom model at all?
A large share of “AI features” people ask for don’t require training a custom model — they can be built by calling an existing API (an LLM provider, a pretrained vision model) and wrapping it in your application logic. That’s a fraction of the cost of custom model development. The expensive path — collecting/labeling data, training, and validating a custom model — is only worth it when off-the-shelf models genuinely don’t solve your specific problem.
What drives cost when you do need something custom
- Data. If you don’t already have a labeled dataset, collecting and labeling one is often the single biggest line item — bigger than the modeling work itself.
- Accuracy requirements. A proof-of-concept that’s right 80% of the time is a very different (and cheaper) project than a production system that needs to be right 99%+ of the time.
- Real-time constraints. Computer vision running on live video in real time — which is what I built for a wheel-tracking project using OpenCV — has to be engineered for latency, not just accuracy. That’s a meaningfully harder problem than batch-processing static images.
- Integration into a real product. A model that works in a Jupyter notebook is not a shipped feature. Wrapping it in an API, handling failure cases gracefully, and integrating it into an existing application is often as much engineering effort as the model itself.
Rough bands
An API-wrapped AI feature bolted onto an existing app: often a few thousand dollars and a few weeks. A custom computer-vision or ML pipeline with real data-collection and training needs: typically five figures and a multi-month timeline. Enterprise-scale AI systems with ongoing retraining, monitoring, and infrastructure: this is where costs climb well beyond a one-time project fee, because it becomes an ongoing operational cost, not a one-off build.
The question worth asking before you budget anything
Before pricing out “AI development,” it’s worth getting a straight answer on whether AI is actually the right tool for the problem, or whether a simpler rules-based approach would get you 90% of the value for a fraction of the cost. A good AI developer should be willing to talk you out of a custom model when it isn’t warranted — that’s a better signal than someone eager to start training immediately.
Scoping an AI or computer-vision feature and want an honest read on complexity and cost? Get in touch.