Anatomy of a Modern Finetuning API
Source: https://benanderson.work/anatomy-finetuning-api Author: Ben Anderson Date: 2025-10-06
Summary
Technical breakdown of what a modern fine-tuning API looks like under the hood. Covers job management, data handling, training infrastructure, and evaluation. Companion to Fine-Tuning as a Service.
Key Claims
- Modern fine-tuning APIs abstract: data upload, training job management, checkpoint management, evaluation runs, model deployment.
- The hidden complexity: users submit data + hyperparameters; API handles distributed training, gradient checkpointing, failure recovery.
- Data handling: most APIs accept JSONL format, run deduplication and quality filtering automatically.
- Cost model: most APIs charge per token processed (both input training tokens and generated tokens during evaluation).
- Key differentiation: better APIs expose more of the training process (loss curves, gradient norms) for debugging; simpler APIs hide everything.
Concepts
- RL Infrastructure — fine-tuning APIs as the accessible layer of ML infrastructure
- Synthetic Data — synthetic data pipelines feed fine-tuning APIs