- Learns your exact vocabulary
- Domain trained
- Runs in your own cloud
- Private GPU
- No per-token API inflation
- Fixed cost
- Starting price
- From ₹1L
When does a business need custom LLM fine-tuning?
Custom LLM fine-tuning trains open-source models (such as Llama, Mistral, or Qwen) on your proprietary datasets, tone of voice, and specialized workflows. WebMarv curates training datasets, conducts parameter-efficient fine-tuning (LoRA), and deploys self-hosted models for maximum privacy and low latency.
- Part of
- AI Software Engineering
- Best for
- Fintech, healthcare, and defense organizations with strict on-premise data privacy rules
- Starts from
- ₹1L
- First step
- Free audit
The friction
Sound familiar? Custom LLM Fine-Tuning & Model Serving fixes this.
Commercial AI APIs (GPT-4) become prohibitively expensive at high daily query volumes.
Strict data privacy regulations forbid sending customer data to third-party US cloud providers.
Prompt engineering alone fails to consistently enforce complex formatting or proprietary logic.
What's included
What Custom LLM Fine-Tuning & Model Serving covers. Pick one piece or the whole set.
Included in the work
- Training dataset preparation, deduplication, and synthetic data augmentation
- Parameter-Efficient Fine-Tuning (PEFT / QLoRA) on open-source foundation models
- Automated benchmark evaluation against target accuracy, latency, and safety criteria
- Quantized model serving using vLLM and TensorRT-LLM for sub-100ms response latency
- Self-hosted cloud GPU infrastructure deployment (AWS, RunPod, or private on-premise servers)
- Continuous active learning and model re-training pipelines on newly validated outputs
How it works
Four steps. No guesswork.
- 01
Free scoping
We map your operations, users and goals before any design.
- 02
Plan and quote
You get the scope, tech stack, timeline and price in writing.
- 03
Build in releases
You review working software every release, not only at the end.
- 04
Launch and support
We deploy, monitor and keep improving it after go-live.
Is it a fit?
Right for you. Or not. We will say which.
A good fit if you are
- Fintech, healthcare, and defense organizations with strict on-premise data privacy rules
- High-volume SaaS platforms looking to replace expensive OpenAI API bills with owned models
- Companies creating proprietary AI features that represent core defensible intellectual property
Pricing
What does it cost?
From ₹1L
Every solution is scoped around your business, complexity and the outcomes you need. We quote after a free audit.
SCOPE & PRICING DETERMINANTS
- 01Number of screens, features and user roles
- 02Integrations with payments, CRM or other systems
- 03How fast you need to launch
Our promises
What you can count on.
Free audit first
We look at your current setup before we quote. There is no obligation to go ahead.
Price in writing
Scope, price and what you get are written down before any work starts.
You own the work
The code and content we build for you are yours. Ownership is in the agreement.
Straight answers
If an existing tool or a smaller fix will do, we tell you. Even when it is not a WebMarv project.
Short releases
You see working software as it is built, so nothing is a surprise at launch.
Identify your biggest revenue leak in 30 minutes.
FAQ
Custom LLM Fine-Tuning & Model Serving questions
Not answered here? Ask us directly.
What is the difference between RAG and Fine-Tuning?
RAG provides the model with external facts and documents to reference. Fine-tuning teaches the model a specific style, tone, format, or specialized task. Often, combining both yields the highest accuracy.
Can a fine-tuned open-source model match GPT-4?
For narrow, specialized domain tasks (such as medical summary extraction, SQL generation, or legal contract classification), a well-fine-tuned 8B or 70B model often outperforms general frontier models at a fraction of the cost.
Can the model run completely offline or on our own private servers?
Yes. We deploy self-hosted instances on your own cloud GPUs or on-premise hardware, ensuring zero external network calls and total data sovereignty.
How much does running a self-hosted fine-tuned model cost?
Running a dedicated open-source model on cloud GPUs typically costs a fixed monthly server fee, which is dramatically cheaper than per-token commercial API pricing for high-volume applications.
How much does WebMarv charge for custom LLM fine tuning services?
WebMarv's custom LLM fine tuning services work is part of Software Engineering, which starts from ₹1L. The final price depends on scope and is agreed in writing after a free audit.
How do I get started with custom LLM fine tuning services?
Book a free audit. We review your current setup, then send a written plan and quote. There is no obligation to go ahead.