How to Train Custom Language Models: Fine-Tuning vs Training From Scratch (2026)

Training a custom language model offers teams the ability to control model behavior, avoid per-token API fees, and maintain full ownership of model weights. The process can vary significantly, ranging from prompt engineering, which requires no retraining, to fine-tuning pre-trained models with domain-specific data, and pre-training from scratch, which involves building a model using massive datasets. Each approach has its own set of requirements, costs, and timelines, and selecting the wrong path could lead to unnecessary expenditure or unmet objectives. Fine-tuning is often the most suitable method for most teams, offering a balance between leveraging existing models and customizing them for specific tasks, while pre-training from scratch is reserved for cases with unique language needs or regulatory demands. The guide emphasizes the importance of data quality in successful model training and suggests beginning with simpler models and scaling as needed. It also outlines the need for rigorous evaluation to ensure that fine-tuning enhances model performance effectively.