LLM Distillation Services
Packages & Pricing.

LLM distillation services that shrink a big model's skill

LLM distillation services train a small model to copy how a large one handles a specific task. Omeiro uses outputs from models such as Claude and GPT-4o to build synthetic examples, then trains a compact 8B model that is cheaper to host.

Higher tiers evaluate the student against the teacher, quantise the build and set up serving.

LLM distillation: choosing a task a small model can learn

Distillation works best on narrow tasks with clear right answers. Classifying tickets, extracting fields from documents or answering questions inside a fixed domain suit a small model, while open-ended reasoning across many topics usually does not. Before ordering LLM distillation, define the task precisely, collect real inputs and decide what counts as a correct output. The tighter that definition, the more the synthetic examples can teach and the easier the comparison with the teacher becomes.

Data quality matters more than volume. The examples a teacher model produces contain mistakes, so a filtering step checks them before training, and a held-out set measures the student fairly. LLM distillation also changes your costs: you trade per-token fees for hosting and maintenance, so estimate your monthly request volume. Check the teacher provider's terms on training other models with its outputs, and tell us the latency target and hardware you plan to serve on.