Applications of Foundation Models

Larger models usually cost more per token and respond more slowly. You must weigh whether the accuracy gain of a bigger model justifies the higher cost and latency for your use case.

Modality is the type of data a model handles: text, image, audio, or multimodal (combinations). You must pick a model whose modality matches your inputs and outputs.

If your users write in many languages, you need a model trained on those languages. A model strong only in English will produce poor results for other languages.

Bigger, more complex models tend to be more capable but cost more and run slower. Choosing the smallest model that meets your accuracy needs is often the best tradeoff.

If you will need fine-tuning or continued pre-training, you must pick a model and platform that supports those customization methods, or you will be locked into prompt-only adaptation.

The context window is the maximum input plus output length (in tokens) a model can process at once. A larger window lets you include more documents or longer conversations.