AI Foundations · Side by side
Inference vs Training
vs
Training is the process of building a model by learning from large datasets, compute-intensive and done relatively rarely. Inference is running the finished model to produce outputs for each request, what your app does at runtime, and where ongoing cost accrues.
Inference
Running a trained model
Training
Building the model from data
Side by side
| Inference | Training | |
|---|---|---|
| When | At runtime, per request | Up front, to build the model |
| Cost pattern | Ongoing, per-call | Large, one-time(ish) |
| Hardware | Optimised for low latency | Large GPU clusters |
| Who pays | App builders, per usage | Model providers, mostly |
The Verdict
If you're building on AI, you mostly pay for inference, every API call to a model is inference. Training matters when you build or fine-tune your own models. Most product teams optimise inference cost (model choice, routing, caching) rather than train from scratch.
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Frequently asked questions
Do I pay for training or inference?
As an app builder, almost always inference, each model call. Training costs sit with providers unless you fine-tune your own model.
How do I reduce inference cost?
Choose right-sized models, route per task (e.g. via OpenRouter), cache results, and trim prompts.
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