AI can save businesses money. But building it isn’t free.
And the cost of an AI project goes far beyond model API fees.
Data preparation, infrastructure, integrations, engineering time, testing, security, monitoring, and ongoing maintenance can easily become a bigger part of the budget than the model itself.
The range is also huge.
Adding an AI-powered feature to an existing application is very different from building a production-ready AI system from scratch.
So what actually determines the price?
Some of the biggest factors include:
model choice and API usage
data quality and preparation
integration complexity
infrastructure and compute
security and compliance requirements
development and testing
monitoring and maintenance
expected usage and scale
We recently broke down these costs in detail, including typical price ranges and the less obvious expenses businesses often overlook when estimating an AI project.
If you're planning to integrate AI into an existing product or build something AI-first, here's the full breakdown:
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