When should I fine-tune a model instead of using RAG?

Fine-tuning is best when you need the model to learn a specific behavior, tone, format, or reasoning pattern — things that are hard to specify in a prompt. RAG is better for factual knowledge that changes frequently. For most production systems, we recommend starting with RAG for knowledge grounding and adding fine-tuning when you need consistent style, domain-specific reasoning, or cost optimization through smaller models.

How much training data do I need for fine-tuning?

For basic fine-tuning, 50-100 high-quality examples can show meaningful improvement. For production-grade results, we recommend 500-5,000 examples depending on task complexity. If you have limited real data, we generate synthetic training examples using larger models (GPT-4o, Claude) and then filter for quality. The key is data quality — 500 excellent examples outperform 5,000 mediocre ones.

How much does fine-tuning cost?

Fine-tuning costs depend on the base model and training data volume. Fine-tuning GPT-4o-mini costs roughly $3 per million training tokens. Fine-tuning open-source models (Llama, Mistral) requires GPU compute — typically $50-500 per training run depending on model size and data volume. The real ROI comes from production savings: a fine-tuned small model can replace expensive GPT-4o calls at 5-10x lower cost per inference.

Should I fine-tune an open-source model or a proprietary one?

Open-source models (Llama 3, Mistral) give you full ownership, on-premise deployment, and no per-token API costs — ideal for high-volume, privacy-sensitive, or compliance-heavy use cases. Proprietary fine-tuning (GPT-4o-mini) is faster to set up and requires less infrastructure management. We benchmark both approaches on your specific tasks and let the results guide the decision.

How do you evaluate whether fine-tuning actually improved the model?

We run comprehensive A/B evaluations comparing the fine-tuned model against the base model and GPT-4o on a held-out test set. We measure task-specific accuracy, response quality (via LLM-as-judge), latency, cost per inference, and safety. We also test for regression on general capabilities to ensure the model did not lose important abilities during fine-tuning. All results are documented in a detailed evaluation report.

Can you deploy fine-tuned models on our own infrastructure?

Yes. For open-source models, we deploy on your GPU infrastructure using vLLM, TGI, or TensorRT-LLM for optimized inference. We handle model quantization (GPTQ, AWQ) to reduce memory requirements, set up auto-scaling, and configure monitoring. This gives you complete data privacy — no data ever leaves your network. We support AWS, GCP, Azure, and on-premise GPU clusters.

How often does a fine-tuned model need to be retrained?

Retraining frequency depends on how quickly your domain changes. For stable domains (legal, medical), annual retraining is often sufficient. For fast-moving domains (customer support with frequent product changes), quarterly or monthly retraining may be needed. We build automated retraining pipelines that trigger when model performance drops below defined thresholds, ensuring your agent stays accurate over time.

Fine-Tuned LLM Agent

Fine-Tuned LLM Agent Development Services

Build AI agents powered by domain-specific fine-tuned models that outperform general-purpose LLMs on your tasks. We handle data preparation, training, RLHF/DPO alignment, evaluation, and deployment — delivering smaller, faster, cheaper models with superior accuracy.

Schedule a Call

INR 2000

Per Hour

50+

AI Agents Built

4.9/5

Client Rating

<2 Weeks

MVP Delivery

Fine-Tuned LLM Agent Capabilities

Custom-trained models that deliver domain expertise at a fraction of the cost

Domain-Specific Fine-Tuning

Fine-tune GPT-4o-mini, Llama 3, Mistral, and other models on your domain data. From medical terminology and legal language to financial analysis and technical support — your model speaks your industry's language natively.

Training Data Preparation & Curation

We handle the hardest part of fine-tuning — data preparation. We collect, clean, format, deduplicate, and quality-score your training examples. We also generate synthetic training data using larger models to bootstrap datasets when real examples are limited.

RLHF & DPO Alignment

Align your model's behavior using Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimization (DPO). Teach the model your preferred tone, safety boundaries, and response style through preference-based training.

Model Evaluation & Benchmarking

Rigorous evaluation against base models using domain-specific benchmarks. We measure accuracy, faithfulness, latency, cost per inference, and safety across hundreds of test cases — ensuring your fine-tuned model demonstrably outperforms alternatives.

Cost Optimization

A fine-tuned smaller model often outperforms a larger general-purpose model on your specific tasks — at 5-10x lower cost. We benchmark fine-tuned GPT-4o-mini against GPT-4o, and fine-tuned Llama against both, to find the optimal cost-performance sweet spot.

On-Premise Deployment Options

Deploy fine-tuned open-source models (Llama, Mistral) on your own infrastructure for complete data privacy. We handle model optimization (quantization, vLLM), GPU provisioning, and inference server setup for self-hosted deployment.

Our Fine-Tuned LLM Development Process

A proven 5-step process from data audit to production fine-tuned model deployment

Discovery & Architecture

We analyze your use case, audit available training data, benchmark base models on your tasks, and recommend the optimal base model (GPT-4o-mini, Llama 3, Mistral) and training strategy — supervised fine-tuning, RLHF, or DPO.

Prompt Engineering & Prototyping

Before fine-tuning, we establish a strong baseline with prompt engineering. This defines the target behavior, generates synthetic training data from larger models, and gives you a working prototype while we prepare the training pipeline.

Integration & Development

Prepare and curate the training dataset, run fine-tuning jobs with hyperparameter optimization, implement RLHF/DPO alignment if needed, and integrate the fine-tuned model into your agent application with proper error handling and fallbacks.

Testing & Evaluation

Comprehensive evaluation comparing the fine-tuned model against the base model and GPT-4o across your domain benchmarks. We test accuracy, latency, cost per inference, edge cases, safety, and regression on general capabilities to ensure production readiness.

Deployment & Monitoring

Deploy the fine-tuned model with inference optimization (quantization, batching, caching). Set up monitoring for model drift, accuracy degradation, and cost tracking. Continuous improvement through regular retraining on new data. 30-day post-launch support.

Fine-Tuned LLM Agent Pricing

Transparent pricing for model fine-tuning and agent development

Starter Agent

INR 50,000starting

Basic fine-tuning with existing training data

Fine-tuned GPT-4o-mini or Llama 3
Up to 1,000 training examples
Data formatting & quality scoring
Base model benchmark comparison
Single-task optimization
Source code handover
14-day post-launch support

Get Started

What Our Clients Say

"We fine-tuned a Llama 3 model on our 50,000 customer support conversations. It now resolves 88% of tickets automatically with domain-specific accuracy that GPT-4o couldn't match out of the box. The cost per inference dropped by 80% compared to using GPT-4o, and we own the model entirely."

VP of Engineering

Engineering at SaaS Platform (500K Users)

"Edesy fine-tuned GPT-4o-mini on our medical documentation data with DPO alignment for safety. The model generates clinical notes that are 94% accurate versus 71% with the base model, while following our specific formatting requirements perfectly. It saves our physicians 45 minutes per day."

Chief Medical Officer

Medical at HealthTech Startup

"Our fine-tuned classification model processes insurance claims 15x faster than our rule-based system with 97% accuracy. Edesy handled everything from data preparation to on-premise deployment. The model runs on our own GPU cluster, so no data ever leaves our infrastructure."

Head of Claims

Operations at Insurance Enterprise

Fine-Tuned LLM Agent

Fine-Tuned LLM Agent Development Services

Schedule a Call

INR 2000

Per Hour

50+

AI Agents Built

4.9/5

Client Rating

<2 Weeks

MVP Delivery

Trusted by businesses worldwide

ShopifyAmazonStripeSlackNotionVercel

Fine-Tuned LLM Agent Capabilities

Custom-trained models that deliver domain expertise at a fraction of the cost

Domain-Specific Fine-Tuning

Training Data Preparation & Curation

RLHF & DPO Alignment

Model Evaluation & Benchmarking

Cost Optimization

On-Premise Deployment Options

35%

Avg Accuracy Improvement Over Base

70%

Cost Reduction vs Large Models

100+

Training Datasets Prepared

45+

Fine-Tuned Models Deployed

Our Fine-Tuned LLM Development Process

A proven 5-step process from data audit to production fine-tuned model deployment

Discovery & Architecture

Prompt Engineering & Prototyping

Integration & Development

Testing & Evaluation

Deployment & Monitoring

Fine-Tuned LLM Agent Pricing

Transparent pricing for model fine-tuning and agent development

Starter Agent

INR 50,000starting

Basic fine-tuning with existing training data

Fine-tuned GPT-4o-mini or Llama 3
Up to 1,000 training examples
Data formatting & quality scoring
Base model benchmark comparison
Single-task optimization
Source code handover
14-day post-launch support

Get Started

What Our Clients Say

VP of Engineering

Engineering at SaaS Platform (500K Users)

Chief Medical Officer

Medical at HealthTech Startup

Head of Claims

Operations at Insurance Enterprise

Frequently Asked Questions

Explore More

Resources to help you evaluate and implement

OpenAI GPT Agent LangChain Agent RAG Agent Multi-Agent System

Ready to Fine-Tune a Model for Your Domain?

Get a free consultation and detailed project estimate. Our fine-tuning specialists will assess your data, benchmark base models, and recommend the optimal training strategy.

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