Inference vs Training
Training is the process of building or adjusting a model's parameters from data; inference is the process of running a trained model to produce an output for a specific input, in production.
What It Is
Training happens once, or periodically, and is compute-intensive by nature, it’s how a model learns its parameters from a large dataset.
Inference happens every time a user or agent makes a request, and needs to be fast and cost-efficient at scale, since it recurs continuously rather than as a one-time event.
The distinction matters operationally: training is a project with a defined endpoint; inference is an ongoing, per-transaction cost.
In B2B Commerce Context
In B2B commerce, the cost and latency conversation with an AI vendor is almost always about inference, since every quote, catalog search, or agent action triggers an inference call. Training cost is typically the vendor’s concern unless a client is fine-tuning a model on its own data.
A practical example
When You Need It
- You're evaluating cost estimates from an AI vendor and need to know what you're actually paying for.
- You're deciding between fine-tuning (a training cost) and RAG (mostly an inference-time cost).
- You're diagnosing whether a slow AI feature is a model-size problem or a retrieval/data problem.
- You're forecasting AI infrastructure costs as transaction volume grows.
Model inference cost, not training cost, is usually the number that matters at scale.
What It Is Not
- Training and inference are not interchangeable terms, and conflating them leads to misjudging cost and performance.
- Training data is not the same as the context fed to a model at inference time, the latter is what RAG supplies per query.
- Inference is not free just because training already happened. At high transaction volume, inference cost usually dwarfs training cost over time.
Comparison
| Attribute | Training | Inference |
|---|---|---|
| Frequency | Once, or periodically | Every request, continuously |
| Compute pattern | Large, intensive, batch-oriented | Smaller, per-request, needs to be fast |
| Cost driver | One-time or periodic project cost | Scales directly with transaction volume |
| Who typically bears the cost | Often the model vendor, unless fine-tuning your own | The business running the AI feature |
See also
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