A custom machine learning model starts at $800 for a predictive analytics model (about 4 days delivery), $1,600 for a natural language processing (NLP) model (about 8 days), and $2,800 for a computer vision model (about 14 days). The gap between these reflects real differences in data complexity and training time, not arbitrary pricing tiers.
Why the three types cost different amounts
Predictive analytics models work from your existing structured data (numbers, categories, records) — the cheapest and fastest to build because the data is already in a usable shape. NLP models process unstructured text (documents, reviews, support tickets), which needs more preparation and typically more sophisticated modeling. Computer vision models process images or video, the most compute- and time-intensive of the three, especially for anything requiring high accuracy.
What's included at each tier
Every tier includes the full pipeline, not just a trained model file:
- Data cleaning and preparation
- Model training and evaluation
- Deployment into a usable form
- A final report explaining what the model does and how it performs
Is a custom model actually the right call?
Not every problem needs a custom-trained model — sometimes an existing AI API (like an LLM) can solve the same problem for far less. A custom model makes sense when the task is specific to your data (predicting your customers' behavior, recognizing your product's defects) rather than something general-purpose AI already handles well. A good technical partner will tell you when you don't need custom training, not just build one anyway.
Common questions
Do I need my own data to build a custom model?
Yes — a custom model is trained on your data specifically. If you don't have enough historical data yet, that's worth addressing before or alongside a model project, since model quality depends heavily on data quality and volume.
How accurate will the model be?
It depends on the problem and the data available — this is assessed and reported on as part of the evaluation phase, not promised upfront. The final report includes real performance numbers, not a marketing claim.
What's the difference between this and just using an LLM API?
An LLM API is general-purpose and fast to integrate but isn't trained on your specific data or problem. A custom model is trained specifically for your use case, which matters when the task depends on patterns unique to your business.
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