Inference is the process of running a trained AI model to produce an actual output — answering a question, generating an image, writing code — as opposed to training, which is the earlier, one-time (or occasional) process of teaching the model in the first place. Every single time someone sends a message to an AI chatbot or an app calls an AI API, that’s an inference request.

The distinction matters commercially more than it might first appear. Training a model is a large but largely one-off cost. Inference, by contrast, happens continuously, every time the model is used — so for a company building a product on top of someone else’s foundation model via API, inference cost is an ongoing, per-use expense that scales directly with how many people use the product and how much text they send and receive each time.

For a founder doing basic unit economics, inference cost is a genuine line item to plan for, not an afterthought — a feature that calls an AI model on every user action can look free to build and then turn out to have real per-user cost at scale, the same way cloud hosting costs do, just billed per token instead of per server-hour.

🇵🇭 Philippine Example

A general, honest note rather than a specific invented company: Philippine startups building AI-powered features typically pay foundation-model providers directly for inference by usage (per token processed), which means inference cost shows up as a real, variable line item in their unit economics rather than a fixed cost — a pattern true of AI-using startups broadly, not something unique to a single named Philippine company that could be verified here.

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