LLM Fine-Tuning Services

Q: When should we fine-tune instead of just prompting or using RAG?

Prompt first — it's free and fast. Use RAG when the gap is missing or changing facts. Fine-tune when you need consistent behaviour or format, a model that genuinely understands your domain's language, or a smaller, cheaper model to match a large one on a specific task. Often the answer is a combination. We assess which lever fits before recommending training — and the most valuable thing we sometimes do is tell you not to fine-tune yet.

Q: How much does LLM fine-tuning cost?

Cost depends mainly on data preparation and the method, not the compute. A focused LoRA fine-tune on a well-defined task typically runs $15,000–$40,000; larger programmes — full fine-tuning, multiple tasks, continued pre-training, and deployment — run $40,000–$120,000+. The training run is often a small part; the data work dominates. We usually start with a fixed-price PoC (typically $12,000–$20,000) to prove the gain first, and every engagement opens with a free 30-minute discovery call.

Q: How long does fine-tuning take?

A production fine-tuning project typically takes 8–12 weeks: roughly 2 weeks discovery and approach selection, 3–4 weeks data preparation, 2–3 weeks training and evaluation, then deployment. A scoped PoC runs in about 6 weeks. The biggest variable is training-data readiness — the training run itself is usually fast; preparing good data is the work.

Q: How much training data do we need?

Less than most people expect. For LoRA/QLoRA on a focused task, a few hundred to a few thousand high-quality examples is often enough — quality and consistency matter far more than quantity. We assess what you have and, where you're short, bootstrap examples with a larger model and your review. We won't let the lack of a massive dataset block the project.

Q: How much can fine-tuning save on inference cost?

When you replace a premium frontier-API model with a fine-tuned smaller open model on a specific task, inference cost typically drops substantially — often by a half to two-thirds — and because the saving repeats on every call, at production volume it compounds into the main business case. We model the break-even (upfront fine-tuning cost vs. ongoing saving) on your real volume so the decision is made on numbers, not a brochure figure.

Q: How do you prove the fine-tuned model is actually better?

We benchmark it against your current baseline — your existing model or prompt — on a held-out test set the model never trained on, and report the delta in accuracy and cost. If it doesn't beat the baseline, it doesn't ship. We define the success metric with you before training, so 'better' is a measured number, not an assumption, and you can see exactly what you're deploying.

Q: Can fine-tuning add our latest data or fix hallucinations?

This is the most common misconception. Fine-tuning teaches behaviour and patterns — it's the wrong tool for facts that change, which go stale in the weights, and only a partial fix for hallucinations. For current information and factual grounding, RAG (retrieving and citing source data at query time) is the stronger tool. Most robust systems fine-tune for how the model behaves and use RAG for what it needs to know, and we design that split.

Q: Which models can you fine-tune, and can you do non-English?

Open models like Llama, Mistral, Qwen, and Gemma (which we fine-tune and self-host), and hosted models with fine-tuning APIs (e.g. OpenAI, Google). We pick the base model on your accuracy target, deployment needs, and whether data must stay in your environment — which usually points to an open model you host. We also fine-tune for the specific languages your task needs, including multilingual models, and test quality per language rather than assuming it transfers — domain fine-tuning is often especially valuable in non-English settings where general models are weaker.

Q: Can we keep our data and the model in-house, and do we own it?

Yes to both — it's a core reason to fine-tune an open model. We can run the entire process inside your infrastructure or VPC, so training data never leaves your environment, and deliver a model you host yourself. And ownership is complete: the fine-tuned weights (or adapters), the curated training data, the code, and the documentation are yours unconditionally — a lasting asset that improves as you add data. We sign NDAs before any discovery conversation and never reuse your data for anyone else.

Q: How do you avoid overfitting or the model forgetting general skills?

With validation monitoring and early stopping to prevent overfitting, parameter-efficient methods (LoRA) and careful learning rates to preserve the base model's general ability, and broad testing — on unseen data and on capabilities outside the target task — to catch both before deployment. Often the fix is restraint, not more training.

LLM Fine-Tuning Services

Fine-tuning is the lever teams reach for too early and avoid too late. We start by telling you whether you even need it — then, when the evidence says yes, we adapt an open model to your task so a smaller, cheaper model matches a frontier giant on your data. LoRA, QLoRA, and full fine-tuning, built on a curated dataset you own, benchmarked against your current baseline, and served on your own infrastructure. Lower cost per call, accuracy past the prompting ceiling, no vendor lock-in.

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Fine-Tuning Is Usually the Wrong First Move

The instinct to fine-tune is often a reflex, not a decision. Most of the time a sharper prompt or retrieval (RAG) solves the problem faster and cheaper — and the single most valuable thing we do on a fine-tuning engagement is sometimes tell you not to fine-tune yet.

Exhaust the cheap levers first

If a clearer prompt, a few good examples, or retrieval over your documents gets you there, that's hours of work instead of weeks — and nothing to retrain when things change. We always push prompting and RAG to their real ceiling before recommending training, because fine-tuning a problem a prompt could fix is wasted budget and a model you now have to maintain.

But avoiding it can be just as expensive

The opposite mistake is just as common: paying premium API rates forever for a high-volume task a fine-tuned small model would handle for a fraction, or accepting an accuracy ceiling prompting can't break. Refusing to fine-tune when the evidence says you should is a slow, recurring cost — not a saving.

Knowing which you're in is the skill

The real expertise isn't running a training script — it's diagnosing whether your problem is a prompt problem, a knowledge problem, or a behaviour problem, because each has a different fix. That diagnosis is the first thing we do, before any GPU is booked, so you spend on the lever that actually moves your metric.

It's 90% a Data Problem, Not a Model Problem

Teams obsess over which base model and which technique. But the result is decided almost entirely by the quality of the examples you train on — fine-tuning is a data-curation project with a training step at the end, and a model trained on messy data faithfully learns the mess.

The model learns exactly what you show it

A fine-tuned model is a mirror of its training set — including its inconsistencies, mislabels, and bad habits. Garbage examples don't average out; they get baked in. That's why a vendor who skips straight to training is dangerous: the work that determines the outcome happens before the GPU ever spins up.

Quality and consistency beat volume

You don't need millions of examples — for LoRA, hundreds to low thousands of clean, consistent ones usually beat a huge noisy pile. A few hundred examples that all demonstrate the behaviour the same way teach more than ten thousand that contradict each other. Curation, not collection, is the job.

We can bootstrap the data you're missing

Short on examples? We use a larger model to draft training data that humans then review and correct, plus augmentation for the rare cases — a far faster path to a usable set than hand-writing everything. You're rarely as far from enough data as you think.

The dataset is the asset that compounds

Your curated training set outlives any single base model: when a better open model ships, you re-tune on the same data and inherit the gains. We treat that dataset as a proprietary asset you own and grow — the part of the work with lasting value.

If You Didn't Beat Your Baseline, You Didn't Improve Anything

A fine-tuned model that nobody compared to what you already had is a leap of faith, not an improvement. The only way to know fine-tuning worked is to measure it against your current model on a held-out set — and surprisingly often, that measurement is never taken.

Benchmark against what you run today

Before training anything, we establish how your current setup — the base model with your best prompt, or your existing solution — actually performs on a held-out test set. That baseline is the number the fine-tuned model has to beat. Without it, "it seems better" is the best anyone can honestly say, and that's not good enough to ship on.

A held-out set the model never saw

Accuracy measured on data the model trained on is meaningless — it memorised those. We evaluate on examples held back from training, so the number reflects how it behaves on inputs it'll actually meet in production, not how well it recited its homework.

We show you the delta, good or bad

You get the before-and-after on the same test set, including the cases where it didn't help. Sometimes the honest answer is that fine-tuning gained little and a prompt change would do — and we'd rather tell you that than hand you a model with no evidence it's worth deploying.

"How much better did it get?" is the test

The fastest way to judge a fine-tuning vendor is to ask exactly that. A serious partner answers with a number on your data; one who can't quote the improvement either didn't measure it or doesn't want you to — and either way you're buying blind.

Fine-Tuning Changes Behaviour; RAG Changes Knowledge

The three levers — prompting, RAG, and fine-tuning — solve genuinely different problems, and most "should we fine-tune?" debates are really a mix-up about which problem you have. Get the distinction right and the answer is usually obvious.

Prompting changes what you ask

The cheapest lever, and the one to try first. If the model can do the task but needs clearer instructions or a few examples, prompt engineering gets you there with no training and no data to curate. A surprising share of "we need fine-tuning" turns out to be "we need a better prompt".

RAG changes what the model knows

When the gap is missing or changing facts — your documents, your latest data — retrieval supplies them at query time. It's the right tool for knowledge that updates, where baking facts into weights would be expensive and stale within weeks. Don't fine-tune facts that change.

Fine-tuning changes how the model behaves

When you need a consistent output format, a domain's language and tone, or a specific skill baked in — or a smaller model to match a bigger one's quality — fine-tuning teaches behaviour that prompting can't reliably reach. It changes the model itself, not just its inputs, which is exactly why it needs data and proof.

Usually the answer is a combination

The strongest systems fine-tune for behaviour and cost, add RAG for fresh facts, and reach both through good prompting. We design the mix for your task rather than selling you the one lever we happen to lead with — and a focused PoC proves the combination before you commit to a full build.

The Win Is a Smaller Model That Does the Big Model's Job

The headline payoff of fine-tuning isn't a slightly smarter model — it's a small, cheap one that matches a frontier giant on your specific task. Because the saving repeats on every single call, at production volume it's the economics, not the accuracy, that usually justify the project.

Match frontier quality at a fraction of the cost

A general-purpose giant is paying — in money and latency — for a breadth you don't need on one narrow task. A smaller open model fine-tuned for that task can match it where it counts, and every inference is dramatically cheaper. Multiply that by production volume and the per-call gap becomes the whole business case.

Past the ceiling prompting keeps hitting

When a model fundamentally misreads your domain's language, format, or edge cases, prompting and RAG plateau — you can feel the accuracy refusing to climb past a certain point. Fine-tuning teaches the model your world directly, which is how you clear a ceiling no amount of prompt rewriting will move.

Reliable behaviour downstream code can trust

A model that returns the right format most of the time still breaks the system consuming it. Instruction tuning makes the correct behaviour consistent, so you can drop the brittle re-parsing and retries that were quietly failing in production — reliability is often a bigger win than raw accuracy.

A model you own and run yourself

A fine-tuned open model runs on your infrastructure, inside your perimeter — strong domain-specific AI without sending sensitive data to a third-party API. The model and its training data are yours to keep, re-tune, and improve as your data grows, instead of renting capability by the call forever.

Weights Aren't a Solution — Deployment and Drift Are

A folder of fine-tuned weights is not a working system. The value only shows up once the model is served efficiently, integrated, monitored, and re-tuned as your data shifts — and a vendor who hands over weights and walks away has handed you the hard 80% of the job.

Serving it efficiently is its own discipline

A fine-tuned model has to be quantised, optimised, and served so it actually hits the latency and cost targets that justified building it. A model that's accurate but too slow or expensive to run in production isn't a win — getting it to run economically is engineering work, not an afterthought.

The world drifts away from your training set

Your data, formats, and use cases change, and a static fine-tuned model slowly falls out of step. We track its performance against your benchmark over time so you see quality slipping before it becomes a problem — not after a downstream system starts failing on it.

Re-tuning built in, not bolted on

Because your curated dataset is an owned asset, refreshing the model is a re-train on more data, not a rebuild — and when a stronger base model ships, you inherit its gains on the same data. We set up that loop so the model keeps improving instead of decaying after handover.

You own the model, data, and pipeline

The weights, the training data, the serving setup, and the evaluation harness are all yours — running on your infrastructure, free of lock-in. That ownership is what turns a one-off project into a compounding capability you control for years.

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Fine-Tuning Technologies
We Work With

We fine-tune on a proven stack — open and frontier models, training and PEFT frameworks, experiment tracking, optimised serving runtimes, and cloud or on-prem infrastructure — selecting the right combination for your task, accuracy target, and cost requirements.

Flexible LLM Fine-Tuning Engagement Models

Fixed-Price POC

Validate an AI agent use case with a low-risk, fixed-scope engagement designed to prove value, feasibility, and ROI before committing to a full build.

4–6 week delivery timeline
Defined scope & success criteria
Low commitment, fixed budget
Executive-ready ROI assessment

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Dedicated AI Team

A cross-functional AI agent team embedded into your environment — working within your processes, security requirements, and communication tools.

AI, Data & MLOps specialists
Named delivery lead
Works within your NDA & security policies
Scalable team composition

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Project-Based

End-to-end delivery of a defined AI agent capability with fixed scope, timeline, and commercial terms. Full knowledge transfer and documentation included.

Fixed scope & pricing
Defined milestones & deliverables
Dedicated project management
Knowledge transfer & documentation

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Model for Your Domain?

Most teams start with one high-value task — where a general model is too expensive, too inconsistent, or just not accurate enough. We fine-tune, benchmark against your baseline, and prove the gain in 6 weeks. No open-ended contracts. No ambiguous scope.

Frequently Asked Questions