Which base models do you fine-tune?

Gemma 3, Qwen 3.8, Llama 4 and Mistral for open-weight deployments, chosen by benchmark results and licence terms. Hosted fine-tuning on GPT or Claude models where data policy allows and the economics favour it.

What does LLM fine-tuning cost?

A scoped fine-tuning engagement with data preparation, training, evaluation and deployment runs from $15,000 over 6 to 10 weeks, depending on data readiness and the serving setup. GPU cost for training is passed through at cost.

Do we own the weights?

Yes. Adapters, merged weights, the training data pipeline and the evaluation set are yours on final payment, with no licence that restricts you.

LLM fine-tuning services

LLM fine-tuning services that are validated on a held-out set before they are deployed on your customers

Domain fine-tuning with QLoRA and DPO alignment, scored against a frozen benchmark agreed in scoping, served on vLLM inside your environment. We also tell you when fine-tuning is the wrong tool, which is more often than most vendors admit.

Book a free scoping call See the work

91%: Citation accuracy after fine-tuning
0: Hallucinated citations after DPO
60%: Lower inference cost than the hosted API
400K: Documents in 1 training set

Why this shape

What this looks like when the system has to survive production

Fine-tuning fixes tone, format, domain vocabulary and jurisdiction-specific behaviour. It does not fix missing or stale facts; retrieval does that. The first thing we do on a fine-tuning engagement is test which problem you have, because training a model to be confident about facts it does not have is how hallucination gets worse.

When fine-tuning is the right call, the loop is fixed: versioned labelled data, QLoRA adapters on an open-weight base, DPO alignment so a correct answer always scores above a confident wrong one, held-out evaluation on ROUGE, BERTScore and task-specific metrics, then serving on vLLM on your hardware or in your cloud account with drift monitoring and a retraining path.

What you get

Delivered as a working production system, in your cloud account

Data preparation and labelling

Cleaning, deduplication, label schema and a frozen held-out set, built with your subject expert so the benchmark reflects real work.

QLoRA and LoRA training

Parameter-efficient adapters on Gemma 3, Qwen 3.8, Llama 4 or Mistral, chosen by your benchmark results and licence requirements.

DPO alignment

Preference pairs that reward the behaviour you need, such as citing the source or refusing outside scope.

Held-out evaluation

ROUGE, BERTScore, exact-match and human preference scoring reported against the base model and the hosted API you are replacing.

Serving on vLLM

Deployed on client-owned GPUs or your cloud account, with quantisation where latency and cost justify it.

Drift and retraining

Monitoring against a rolling baseline and a retraining pipeline, so the model improves instead of going stale.

Architecture

The fine-tuning loop we ship

Versioned data feeds training. Nothing reaches the registry without clearing the held-out evaluation. Serving is drift-monitored and feeds the next retraining cycle.

When this is the wrong tool

If the model lacks facts, retrieval is cheaper and safer than fine-tuning. If the task is tabular prediction, a gradient boosted model beats any LLM on accuracy, cost and auditability. We run that test before recommending either.

Proof

Delivered systems closest to this work

Every figure comes from a system running in production for a real client.

LegalTechUSA and UK

Domain-specific legal model that stopped hallucinating citations

1. Problem. A major international law firm was running general-purpose models that hallucinated citations and produced inconsistent clause analysis across 11 jurisdictions. Every wrong answer was a liability risk in client-facing work.

2. Solution. We fine-tuned Gemma 3 27B on 400,000 of the firm's own legal documents using QLoRA on GCP Vertex AI. DPO alignment sharpened precision for time-pressured lawyers. A custom evaluation pipeline covering ROUGE-L, BERTScore and citation accuracy ran before any model reached production.

Gemma 3 27BQLoRADPOGCP Vertex AI

3. Impact

91%: Citation accuracy on legal queries
0: Hallucinated citations after alignment
400K: Proprietary documents in the training set
60%: Lower inference cost vs proprietary APIs

Regulated EnterpriseEU

A private LLM deployment where no request ever leaves the customer environment

1. Problem. A regulated enterprise wanted a modern language model over its internal documents, under a contractual ban on sending that content to any third-party processor. Even zero-retention API terms did not clear the bar: nothing could leave the environment at all.

2. Solution. A fully private deployment inside the customer VPC: open-weight models on client-owned GPU nodes, self-hosted vector store, orchestration and observability, with egress to external model APIs blocked at the network layer. The audit answer to "where did this data go" is the same as "where does it live".

Open weightsvLLMSelf-hosted VPCQdrant

The client's name, sector and jurisdiction are under NDA. The residency figure is the measurement they cleared us to publish.

3. Impact

100%: Data residency inside the client VPC
0: Requests leaving the environment
Open: Weights, on client-owned hardware
Full: Audit trail held internally

Retail and E-CommerceUSA

2 compounding problems solved in one platform: conversion and inventory

1. Problem. Conversion was flat because the experience was identical for every segment. Inventory costs kept climbing because demand planning was reactive and manual. 2 problems feeding each other, neither one solved by the existing tools.

2. Solution. A dual-layer platform. The personalization layer generates real-time recommendations from live behavioral signals and improves through reinforcement learning. The supply chain layer predicts demand shifts and adjusts inventory planning before overstock or stockout hits. Both run through AWS Bedrock under 100ms.

AWS BedrockReinforcement learningSnowflakeSageMaker

3. Impact

14%: Increase in e-commerce conversion rate
31%: Improvement in inventory planning accuracy
62%: Improvement in demand forecasting accuracy
<100ms: Recommendation response time

Questions

LLM fine-tuning services: what buyers ask first

private LLM deploymentPrivate LLM deployment where the audit answer to "where did this data go" is "nowhere"RAG development companyA RAG development company that measures retrieval before it touches generation enterprise LLM developmentEnterprise LLM development that ships the product around the model Full service page

A few thousand high-quality examples usually beat hundreds of thousands of noisy ones for QLoRA on a domain task. The legal model used 400,000 documents because the firm had them clean and labelled; most clients start with far fewer.

Your AI system should work, not just demo well

Book a free 30-minute scoping call. You leave it with a recommended approach, a realistic timeline and a cost range, and you talk to the engineer who would design the system.

Book a free scoping call Send project details

Written scoping summaryyours after the call

1Recommended approachRAG with hybrid retrieval, or classical ML, or plain software. Including the option of not building.
2What the system ownsThe steps it resolves alone, and the steps that stay with a person.
3First metric and baselineThe number it has to beat, measured on your own data before any build.
4Timeline and cost rangePhased, with the first scored result inside 14 days.

Written by the engineer who would build itFree, no obligation, yours to take to any vendor

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