LLM Finetuning Services

The clearest indicator is a measurable quality gap on a specific, well-defined task that persists after you have invested seriously in few-shot examples and retrieval augmentation. If your task requires consistent output formatting, domain-specific jargon comprehension, or behavior that few-shot prompting cannot reliably produce even with 10+ examples, fine-tuning is worth evaluating. Our diagnostic sprint (typically 1–2 weeks) establishes this baseline before you commit to the full investment.

Project investment ranges from $35,000 to $150,000 depending on data complexity, number of training runs, and serving infrastructure. Compute costs are separate — a typical LoRA fine-tuning run on a 7B–13B parameter model costs $500–$3,000 per run on cloud GPU infrastructure. End-to-end timeline from data audit to production deployment is typically 8 to 14 weeks. Most of that time is data work, not training.

We have fine-tuned Llama 3, Mistral, Phi-3, Gemma, and Qwen families using both full fine-tuning and LoRA/QLoRA adapters. For teams with data-residency or licensing requirements, open-weight models are often the only viable path. For teams that want API-based fine-tuning with less infrastructure overhead, we also work with OpenAI and Anthropic's fine-tuning APIs, though these have more constraints on what you can adapt.

For supervised fine-tuning on a narrow task, 500–3,000 high-quality examples is often sufficient with LoRA. More important than raw count is example quality and coverage of your failure modes. We often use synthetic data generation to augment sparse real-world datasets — generating diverse variations of your examples using a frontier model and then filtering for quality. This can substantially reduce the labeled data requirement, though the exact leverage depends on your domain and task.

Your training data and resulting model weights remain entirely under your control. Fine-tuning runs are executed either in your cloud account or in an isolated environment with no data retention after the engagement. We do not use client data to train shared models, we do not retain weights after handoff, and we provide data processing agreements if your domain is regulated (HIPAA, SOC 2, etc.).