TuneLLM

TuneLLM reduces inference costs for AI workflows by automatically distilling large frontier models into smaller, fine-tuned models that run inside the user's own infrastructure. Instead of paying premium rates for generalist models like Claude or GPT for narrow, high-volume tasks such as translation, document parsing, or classification, users can proxy their existing API calls through TuneLLM. The system captures the data, trains a smaller model on the specific workflow, and provides a benchmark-gated switch to the distilled model, claiming a 10–20x reduction in cost while maintaining quality. The platform requires no ML engineering expertise and keeps all data and model weights within the user's private cloud or on-premise environment.

Unlike general AI API providers, TuneLLM focuses specifically on cost reduction for existing high-volume workflows by creating custom, smaller models that live in the user's own environment, eliminating the need for an internal ML team.