LLM Fine-Tuning Services That Learn Your Language
Acquaint Softtech delivers LLM Fine-Tuning Services for Llama, Mistral, GPT, and Gemini, tailoring models to your data, brand voice, and output needs, backed by 13+ years of experience and 70+ in-house engineers.
- Every method: full, LoRA, QLoRA, instruction and preference tuning.
- Measured gains: evaluation before and after every run.
- Clear pricing: from $30/hour, dedicated $4,400/month.
- Proven expertise: 4.8/5 Clutch, ISO 27001, 70+ engineers.
What Are LLM Fine-Tuning Services for Business?
How LLM fine-tuning changes model behavior.
LLM fine-tuning services continue training a pre-trained large language model on your own examples, so it learns your vocabulary, tone, output format and decision rules. Instead of writing longer prompts, the knowledge goes into the model itself, giving more consistent answers and lower cost per call.
Can an LLM be fine-tuned? Yes. Most open models, such as Llama and Mistral, and several commercial models from OpenAI, Google and Amazon Bedrock support fine-tuning.
Signs your use case needs custom LLM fine-tuning.
Fine-tuning earns its place when a model keeps breaking your output format, sounds generic instead of on-brand, or misuses your domain terms. It also fits when a large model is too costly for a narrow, high-volume task, or when data must stay inside your network.
Why Invest in Custom LLM Fine-Tuning?
Generic models sound generic, miss your terminology and break your output formats. Custom LLM fine-tuning teaches the model your rules once, so every call behaves the same way.
Strict output formats
Reports, JSON and forms in your exact structure every time.
Brand voice and tone
Replies that sound like your company, not a generic assistant.
Domain vocabulary
Medical, legal or financial terms used correctly across every model output.
Faster classification
Smaller tuned models that label and extract text cheaper.
Lower cost per call
A tuned small model replacing a large one for a narrow task.
Private, on-premise AI
Open models tuned and hosted inside your own environment.
Which Fine-Tuning Method Fits Your Model?
The main types of fine-tuning LLMs are full fine-tuning, parameter-efficient tuning, instruction tuning and preference tuning. Most business projects use LoRA or QLoRA, because this fine-tuning method is faster and far cheaper than full training.
Parameter-Efficient Fine-Tuning
Supervised and Instruction Tuning
Preference Tuning
Small Model Distillation
Six Problems LLM Fine-Tuning Solves
Each problem below is a common gap in generic language models, and names who it was built for where we can. They show where LLM fine-tuning beats longer prompts.
Urgent signals buried in free text
The fix: a custom classification engine with human sign-off.
Output breaks your systems
The fix: supervised fine-tuning on graded examples.
Replies sound generic
The fix: tone and style tuning on approved replies.
Specialist terms used wrongly
The fix: domain-specific tuning plus RAG for facts.
Large model too costly at volume
The fix: a distilled small model for the narrow task.
Data cannot leave your network
The fix: an open model tuned and self-hosted.
Fine-Tuning vs RAG: Which Do You Need?
Fine-tuning changes how a model behaves; RAG changes what information it sees at answer time. Choosing between fine-tuning and RAG is the biggest cost decision in LLM customization.
Nine LLM Fine-Tuning Services We Deliver
Each of our LLM fine-tuning services has a defined scope and a clear deliverable. As an LLM fine-tuning services company, we start with one tuned model for one task and expand once gains are proven.
Fine-Tuning Consulting
Training Data Preparation
Supervised and Instruction Tuning
LoRA and QLoRA Tuning
Preference Tuning (RLHF and DPO)
Open-Source LLM Fine-Tuning
Managed Model Fine-Tuning
Evaluation and Safety Tuning
Deployment and LLMOps
Our LLM Fine-Tuning Pipeline, Data to Model
Every successful fine-tuning project moves through the same five stages, and skipping any of them is where tuning AI projects fail. You get an LLM fine-tuning pipeline you can rerun whenever your data changes.
Our rule: no training run ships unless it beats the baseline on your own evaluation suite.
What Is the Best LLM for Fine-Tuning?
The best LLM for fine-tuning depends on your task, budget and privacy rules. We are model-agnostic and choose open or managed fine-tuning based on results on your test set.
Everything a Fine-Tuned LLM Needs to Ship
A model that improves on a handful of test prompts proves nothing on its own. Every LLM fine-tuning engagement ships with measured, repeatable results and everything needed to run them again.
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Fine-tune vs RAG decision
Compare fine-tuning, prompting, and RAG to select the approach best meeting goals, budget, and needs.
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Curated training dataset
Clean, label, and organize examples that teach the model your terminology, tone, and expected responses.
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Evaluation suite
Test model quality against baseline metrics, real user tasks, edge cases, and defined acceptance criteria.
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Model and method choice
Choose base models and training methods based on data quality, cost, performance, and deployment needs.
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Training runs and reports
Run controlled experiments, track settings, compare results, and document improvements, limitations, and recommended next steps.
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Safety checks
Check outputs for bias, privacy leaks, harmful responses, and policy violations before release to production.
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Deployment and monitoring
Deploy the tuned model securely, monitor quality and costs, and detect issues for timely improvements.
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Documentation and handover
Provide training details, evaluation results, deployment instructions, and guidance for ongoing maintenance and future updates.
Secure LLM Fine-Tuning on Your Data
Training data often holds customer records, contracts and internal knowledge. Secure LLM fine-tuning keeps you in control of the data, the training environment and the resulting model.
ISO 27001 certified
Information security management.
NDA before any data
Signed before training data is shared.
PII redaction
Personal data removed before training.
Train in your cloud
Your account, or self-hosted when data cannot leave.
Engineer Access
Encrypted data; access limited to engineers.
Safety and bias tests
Run on every version, with review.
Rollback ready
Previous model restored if a release regresses.
100% IP ownership
Datasets and models belong to you.
Domain-Specific LLMs for Your Industry
Every industry has its own vocabulary, formats and rules, which is where domain-specific LLMs pay off. Where we have delivered for a named client we mark it; elsewhere we list typical model customization use cases.
Recruitment and Matchmaking
Legal
Healthcare
Banking and FinTech
Insurance
eCommerce
SaaS and Support
Manufacturing
Public Sector and Research
LLM Fine-Tuning Led by Engineers Who Measure
Every LLM fine-tuning engagement has a named senior engineer accountable for measurable gains. Our AI work is led by our CIO, so every training decision rests on evidence, not guesswork.
Manish Patel
Chief Information Officer, Acquaint Softtech
Our Fine-Tuning Process in Six Steps
Our fine-tuning process shows measured results on your own data before full training. You know the gain from LLM fine-tuning before you pay for production rollout.
Define the Goal
Set goals, metrics, and why tuning beats RAG.
Curate the Data
Train with LoRA to full tuning, tracking runs.
Set the Baseline
Score the base model on your evaluation suite.
Fine-Tune
Train with LoRA to full tuning.
Evaluate and Harden
Compare against baseline and test safety.
Deploy and Monitor
Serve the model and retrain as data changes.
Custom Language AI from a Real Client
A custom classification engine trained on client language, with people owning every decision. The custom language AI story below is backed by a verified Clutch review.
Manual inspections are slow, subjective, and expensive. We engineered a high-velocity, AI-driven ecosystem for Good2Know that transforms grainy images into actionable intelligence. Scale your operations with the speed of AI and the security of an enterprise-grade architecture with Acquaint Softtech.
Minuttia helps brands win visibility across Google and AI search, and its analysts were spending days each week assembling reports from scattered data. Through AI development, Acquaint Softtech overhauled its analytics with automated ETL pipelines, machine learning models, dynamic dashboards, and scheduled reporting. Weekly reporting dropped from several days to a few hours, and campaign teams can now anticipate engagement drops before they happen.
Property Brokers serves provincial New Zealand from more than 80 locations and needed a faster way to turn market data into pricing confidence. Acquaint Softtech delivered a Python-powered property intelligence platform. It replaced scattered spreadsheets, sped up appraisal preparation, and gave regional managers a clear view of market trends.
Kandy scales e-commerce brands, and it wanted its partner stores to recommend products based on real shopper behavior instead of static catalog rules. Through custom software development, Acquaint Softtech built a machine learning recommendation engine with behavioral data pipelines and real-time recommendation APIs. Once deployed across several stores, it brought clear improvements in customer engagement, average order value, and repeat purchases.
What Clients Say About Our LLM Engineering
Rated 4.8/5 across 79 verified Clutch reviews of our AI, language and data work. Below, clients answer the questions buyers ask before choosing an LLM fine-tuning services provider.
Why Choose Our LLM Fine-Tuning Company
AI fine-tuning jobs are among the hardest roles to fill, and hiring takes months. Our LLM fine-tuning company gives you senior engineers within 48 hours, with offshore value and in-house accountability.
Measure first
A baseline before training, proof after every run.
Model-agnostic
Open or managed models, chosen on your results.
Proof you can check
Named clients and verified Clutch reviews from real customers.
Honest about RAG
We fine-tune only when it beats prompting or RAG.
Stable in-house team
24+ months average tenure, so your builders stay.
Your data, your model
Datasets, adapters and weights where licensed.
Fine-Tuning Tools and Models We Use
We are model-agnostic and choose fine-tuning tools per task, budget and privacy need. Every tool below is used by our engineers in production LLM fine-tuning.
Open models
Managed fine-tuning
GPU platforms
Training libraries
Efficient tuning
Experiment tracking
Evaluation
Serving
Containers
Data prep
Retrieval
Apps and integration
How Much Do LLM Fine-Tuning Services Cost?
LLM fine-tuning services start at $30 per hour part-time, and a dedicated fine-tuning engineer costs $4,400 per month. Fixed-scope fine-tuning projects start from $8,000, and the final cost depends on data preparation, model size and hosting.
Part-Time Fine-Tuning Engineer
- Up to 4 hours a day
- Monthly billing
- Pause with one week's notice
Dedicated Fine-Tuning Engineer
- Starts within 48 hours
- One-week trial
- Free replacement
Fixed-Scope Fine-Tuning Project
- Agreed accuracy targets
- Milestone-based payments
- Full documentation and handover
What drives fine-tuning cost
LLM Fine-Tuning Services FAQs
Clear answers to the questions teams ask most before fine-tuning a language model.
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What are LLM fine-tuning services?
LLM fine-tuning services continue training a pre-trained large language model on your own examples, so it learns your vocabulary, tone, format and rules.
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Can an LLM be fine-tuned?
Yes. Open models such as Llama and Mistral can be fully or partially fine-tuned, and OpenAI, Amazon Bedrock, Vertex AI and Together AI offer managed fine-tuning.
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What is fine-tuning in LLM vs RAG?
Fine-tuning changes how a model behaves, such as format and tone, while RAG gives it fresh information from your documents at answer time. Many systems use both.
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What are the different types of fine-tuning LLMs?
Full fine-tuning, parameter-efficient tuning such as LoRA and QLoRA, instruction tuning, and preference tuning such as RLHF and DPO.
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What is the best LLM for fine-tuning?
There is no single best model. Llama and Mistral suit private, self-hosted tuning, while OpenAI, Vertex AI and Bedrock suit managed tuning. We choose on your test results.
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How much does LLM fine-tuning cost?
Part-time help starts at $30 per hour, a dedicated fine-tuning engineer costs $4,400 per month and fixed-scope projects start from $8,000.
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How much data do we need to fine-tune an LLM?
Many parameter-efficient projects start with a few hundred to a few thousand high-quality examples. Quality matters more than volume.
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Who owns the fine-tuned model?
You do. We transfer 100% of the IP, including datasets, adapters, training code and, where the base model license allows, the tuned weights.
Related AI Development Services
Fine-tuning clients often pair their tuned model with RAG development, LLM development or MLOps. Explore the AI development services below from the same team.
AI Engineering
08AI Specialization
09- • Machine Learning Development Services
- • Computer Vision Development Services
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- • RAG Development Services
- • AI Agent Development Services
- • Agentic AI Development Services
- • AI Chatbot Development Services
- • Voice AI & Chatbot Development Services
- • Multi-Agent AI Services
India (Head Office)
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