Predictive analytics and AI
Build or buy a predictive model?
The short answer
Buy when the prediction is a common one (churn, lifetime value, lead scoring, demand) and you have no data science team. Build when the problem is specific to your business, the data is unusual, or the model is core to your product. Building means paying for people first and compute second.
Key takeaways
- Buy for common predictions without a data science team; build for unusual or core problems.
- Most tabular predictions train fine on CPUs; GPUs matter for deep learning and language models.
- Ask any vendor to test on your data and show accuracy on data the model has not seen.
On this page 2 sections
| Buy a platform | Build your own | |
|---|---|---|
| People | Analyst or ops lead | Data scientist or ML engineer |
| Time to first model | Days to weeks | Weeks to months |
| Flexibility | Common use cases | Anything you can define |
| Maintenance | Vendor retrains and monitors | You monitor drift and retrain |
| Compute | Included | Your cloud or GPU rental |
Compute, if you build
Most tabular predictions (churn, scoring, forecasting) train fine on ordinary CPUs. GPUs matter for deep learning, large text or image models, and fine-tuning language models. Renting GPUs by the hour from a cloud such as RunPod avoids buying hardware for occasional training runs.
If a model has to run around the clock (serving predictions continuously, for example), renting a GPU server by the month from a host such as Database Mart can work out simpler than hourly billing. Compare the monthly cost against your expected hours of use.
Questions to ask any vendor
- Can we test on our own data before signing?
- How is accuracy measured, and on data the model has not seen?
- How are the scores delivered to our CRM or marketing tools?
- How often is the model retrained, and who is alerted if accuracy drops?
- Where is our data processed and stored?
Next step