Training and inference are the two distinct phases of a model's life. Training is the expensive, compute-heavy process of teaching a model patterns from large amounts of data. Inference is using that trained model afterward to produce outputs on new inputs. One builds the model; the other runs it.

The distinction matters for cost and strategy. Training a foundation model from scratch can cost millions and is rarely necessary for most businesses. Inference is the recurring cost you pay every time the model is used. Knowing which side a project sits on tells you whether the spend is a one-time investment or an ongoing operational line item.

At arosplatforms we steer most clients away from training their own large models. We rely on existing foundation models and adapt them with fine-tuning or retrieval, then focus our engineering on making inference fast and cost-effective, which is where the real day-to-day economics are.