LLM Fine-Tuning Services
We fine-tune open-source and hosted LLMs so they speak your domain’s language, follow your formats, and perform your tasks reliably — with the data and evaluation work that actually makes it worth the cost.
Fine-tuning is powerful when prompting and retrieval hit a ceiling: you need a consistent tone, a structured output format, a specialised task, or lower latency and cost at scale. Done carelessly it burns budget and produces a model that’s worse than the base.
We treat fine-tuning as a data and evaluation problem first, compute second. That means curating and labelling the right dataset, choosing the right method (parameter-efficient LoRA/QLoRA vs full fine-tuning), and proving the gain against a held-out benchmark before you commit to production.
What's included
- Use-case assessment: is fine-tuning the right tool vs RAG or prompting?
- Training data curation, cleaning, labelling, and synthetic data generation
- LoRA / QLoRA parameter-efficient tuning and full fine-tuning
- Instruction tuning, preference optimisation (DPO), and domain adaptation
- Rigorous evaluation against task-specific benchmarks
- Quantisation, serving, and cost-efficient deployment
How we approach it
- Define success metrics and build an evaluation set before training
- Establish a strong prompting/RAG baseline to beat
- Run efficient LoRA/QLoRA experiments before scaling compute
- Validate on held-out data, then package for serving with monitoring
What you get
- A fine-tuned model that measurably beats the baseline on your tasks
- A reusable training and evaluation pipeline
- A serving setup (hosted API or self-hosted) with cost/latency benchmarks
- A retraining playbook for when your data changes
Technologies we use
Frequently asked questions
How much does LLM fine-tuning cost?
The GPU hours are usually the smallest line item — data preparation, experimentation, and evaluation dominate. We size a realistic budget up front and start with parameter-efficient methods (LoRA/QLoRA) to keep costs low. See our breakdown of LLM fine-tuning costs for the full picture.
Do we need our own GPUs?
Not necessarily. We can use hosted fine-tuning APIs, rent cloud GPUs on demand, or set up self-hosted training — whichever fits your budget, data-privacy needs, and scale.
How much data do we need?
Less than most people expect for LoRA-style adaptation — often a few hundred to a few thousand high-quality examples. Quality and consistency matter far more than raw volume, and we help you build or augment the dataset.
Ready to talk llm fine-tuning?
Tell us about your project and we'll respond within 24 hours with a clear next step.