LLM Fine-Tuning Services

LLM fine-tuning continues training an existing foundation model on your targeted data to permanently change its behaviour - its output format, tone, vocabulary, instruction-following patterns, and domain-specific responses. Unlike prompt engineering (which shapes one response at a time) or RAG (which supplies knowledge at query time), fine-tuning alters the model's weights. The result is a model that natively behaves the way you need rather than requiring extensive prompting to approximate it.

Follow the hierarchy: prompt engineering first (free, instant, reversible), RAG second (handles ~80% of enterprise use cases), fine-tuning third (only when the first two plateau). Fine-tune when you need: consistent output format that prompting can't reliably hold, domain-specific vocabulary and tone, cost/latency reduction at high volume (a fine-tuned smaller model replacing expensive API calls), or behaviour consistency that drifts between prompts. Don't fine-tune for facts - those belong in RAG. Don't fine-tune before exhausting prompting. And don't fine-tune when your target behaviour is still changing - lock the requirement first.

LoRA/QLoRA fine-tuning on a 7B-13B model costs $2,000-$30,000 in compute and data preparation - far less than the $100,000+ most CTOs assume. But the training cost is not the full cost. Budget 3-5x the training cost for 12 months of lifecycle management: adapter versioning, drift monitoring, retraining, and quarterly revalidation. A $10,000 fine-tune has $30,000-$50,000 in lifecycle cost over the first year. We scope the full cost - training plus lifecycle - before you commit.

Minimum 500 high-quality examples. Ideal range is 1,000-10,000 clean, representative input-output pairs. Quality matters enormously more than quantity - 500 carefully curated examples outperform 50,000 noisy ones. If you don't have sufficient labelled examples, we can generate synthetic training data using frontier models (GPT-4, Claude) with quality validation. The data curation phase is where most fine-tuning projects succeed or fail.

LoRA (Low-Rank Adaptation) trains small adapter layers that modify the base model's behaviour without retraining all parameters. It's 10-100x cheaper than full fine-tuning, achieves approximately 90% of the performance, and lets you deploy the adapter alongside (or swap between) different adapters on the same base model. QLoRA adds quantisation for even lower memory requirements. In 2026, LoRA and QLoRA are the only fine-tuning approaches most teams should consider - full fine-tuning is reserved for extreme domain shifts where adapter performance isn't sufficient.

Open models: Llama 3.1/3.3, Mistral, Gemma, Phi, Qwen, and others - deployable in your infrastructure with full weight ownership. Closed models: OpenAI and Anthropic offer fine-tuning APIs for their models with hosted serving. The choice depends on deployment requirements (self-hosted vs API), data residency (can training data reach external APIs?), and cost at scale (self-hosted inference is cheaper at high volume).

On the specific capability you train it for - yes, typically 20-40 point improvements on task consistency compared to prompting alone. On general capabilities - it may degrade them. Fine-tuning narrows the model's focus, which improves performance on your target task and can reduce performance on tasks outside the training distribution. This is why evaluation suites matter: measure the target improvement AND check that general capabilities haven't degraded below your acceptable threshold.

Your adapter may degrade silently. LoRA adapters are tied to the specific base model version they were trained on. When a provider updates the base model (which happens without notice on hosted APIs), the adapter's compatibility can shift. We build drift monitoring that detects this and triggers revalidation - the operational requirement that most fine-tuning guides omit and most teams discover six months in.

Yes - multi-task fine-tuning trains one adapter on several related tasks (classification, extraction, summarisation, generation). This is more efficient than separate adapters per task and often produces better results because the tasks reinforce each other. Alternatively, you can maintain separate adapters and route between them based on the input - the right approach when tasks are sufficiently different that one adapter can't serve all of them well.

Then we've saved you months and the cost of a training cycle that wouldn't have solved your problem. The readiness assessment evaluates your use case, tests whether prompt engineering or RAG delivers sufficient results, and only recommends fine-tuning when the evaluation data shows the first two approaches have plateaued. Approximately 60% of the teams who come to us asking for fine-tuning leave with a simpler, faster solution that works. We consider that a successful engagement.