LLM Development Services: Custom, Fine-Tuned and Private Large Language Models
LLM development is building, adapting and deploying large language models (AI models trained on large amounts of text to understand and generate language) for specific business tasks. It ranges from building applications on top of models like GPT, Claude and Gemini, to fine-tuning open-weight models such as Llama, Mistral and Qwen on company data, to deploying private LLMs inside your own cloud. PerfectionGeeks delivers all three, and benchmarks each option on your data before recommending one.
Use an API, Fine-Tune, or Build From Scratch? Choosing Your LLM Approach
Our recommendation for most enterprises: start at option 2, measure, and move to option 3 only when evaluation shows a gap that retrieval can't close. Fewer than 1 in 100 business use cases justify option 5. → Related: RAG development services
| Approach | What it means | Best when | Typical cost | Timeline |
|---|---|---|---|---|
| 1. Foundation model API + prompting | Use GPT, Claude or Gemini via API with engineered prompts | General tasks, fast start, moderate volume | $ | 2–4 weeks |
| 2. API + RAG | Add retrieval over your documents | Answers must use your current, private knowledge | $–$$ | 4–10 weeks |
| 3. Parameter-efficient fine-tuning (LoRA/QLoRA) | Train small adapter layers on an open-weight model | Consistent format, tone or domain language; lower inference cost; private hosting | $$ | 6–10 weeks |
| 4. Full fine-tuning / continued pre-training | Update all model weights on large domain corpora | Deep domain specialization (legal, medical, code, non-English) | $$$ | 10–20 weeks |
| 5. Train from scratch | Build a new model from random initialization | Unique language or data at massive scale; strategic IP | $$$$ (often millions) | 6–12+ months |
Why Parameter-Efficient Fine-Tuning Changed the Economics
Why Parameter-Efficient Fine-Tuning Changed the Economics
Our LLM Development Services
LLM Strategy & Model Selection
We benchmark 3–5 candidate models (proprietary and open-weight) on your real tasks for accuracy, latency, cost and privacy, and recommend the best fit. Related: AI consulting
LLM Fine-Tuning
Supervised fine-tuning, LoRA/QLoRA, instruction tuning and preference optimization (DPO/RLHF) for domain vocabulary, output formats, tone and task accuracy.
Custom LLM & Domain Model Development
Continued pre-training on large domain corpora (legal, medical, financial, multilingual) for organizations that need deep specialization.
Small Language Model (SLM) Development
Compact models (roughly 1B–14B parameters) distilled or fine-tuned for one task, running cheaply on modest GPUs, on edge devices or fully offline.
Private LLM Deployment
Self-hosted LLMs in your AWS, Azure or GCP account or on-prem, with vLLM or TGI serving, autoscaling, SSO and audit logging. No data leaves your environment. Related: Enterprise AI development
LLM Application Development
Production apps built on LLMs: knowledge assistants, document processing, copilots, chatbots and agents. Related: Generative AI development · AI agent development · AI chatbot development
LLM Evaluation & Red-Teaming
Custom benchmark sets, LLM-as-judge scoring, human evaluation, safety and jailbreak testing, and regression testing for every model version.
LLM Inference Optimization
Quantization (GPTQ, AWQ, GGUF), speculative decoding, batching, caching and model routing to cut latency and cost per query.
LLMOps
Prompt and model versioning, tracing, drift and quality monitoring, cost dashboards and retraining pipelines.
Open-Weight vs Proprietary LLMs: Which to Build On
| Model family | Type | Strengths | Consider when |
|---|---|---|---|
| OpenAI GPT series | Proprietary API | Top-tier reasoning, tool use, multimodal | Best quality matters more than hosting control |
| Anthropic Claude | Proprietary API | Long context, careful instruction following, coding | Document-heavy and policy-sensitive tasks |
| Google Gemini | Proprietary API | Very long context, native multimodal | Video and image tasks; GCP-native stack |
| Meta Llama | Open-weight | Large ecosystem, strong fine-tuning support | Private deployment, custom fine-tunes |
| Mistral / Mixtral | Open-weight | Efficient, strong European-language support | Cost-efficient self-hosting, EU data residency |
| Qwen | Open-weight | Strong multilingual and coding performance, many sizes | Multilingual and Asian-language applications |
| Google Gemma / Microsoft Phi | Open-weight SLMs | Small, fast, cheap to run | On-device, edge, high-volume single tasks |
| DeepSeek | Open-weight | Strong reasoning at low cost | Reasoning-heavy self-hosted workloads (subject to your security review) |
How We Develop and Fine-Tune an LLM
| Step | What happens | Output |
|---|---|---|
| 1. Define the task & success metrics | Precise task definition, target accuracy, latency and cost per query | LLM requirements spec |
| 2. Baseline benchmarking | Test prompting and RAG on 3–5 models using your evaluation set | Benchmark report: is fine-tuning needed? |
| 3. Data collection & curation | Gather, de-duplicate, clean and label training examples; remove PII | Training dataset (typically 1,000–50,000 examples for fine-tuning) |
| 4. Model & method selection | Choose base model, size, PEFT vs full fine-tuning | Training plan |
| 5. Training | Fine-tune on managed GPUs with hyperparameter search and checkpointing | Trained model / adapters |
| 6. Evaluation | Task accuracy, faithfulness, safety, bias, regression vs baseline | Evaluation report |
| 7. Alignment & guardrails | Preference tuning, refusal behavior, output filters | Aligned, guarded model |
| 8. Optimization | Quantization, distillation, serving configuration | Production-optimized model |
| 9. Deployment | Private endpoint or managed hosting, autoscaling, monitoring | Live LLM API |
| 10. LLMOps | Monitor quality and drift, collect feedback, retrain periodically | Monthly model report |
How We Evaluate LLMs
A fine-tuned model only ships if it beats the prompted baseline on your test set.
Task-specific test sets
built from your real inputs, with expected outputs written by your experts
Automatic metrics:
exact match, F1, BLEU/ROUGE where relevant, JSON/schema validity, faithfulness to source
LLM-as-judge
scoring with calibrated rubrics, spot-checked by humans
Human evaluation
by domain reviewers for high-stakes outputs
Safety testing:
jailbreaks, prompt injection, toxic output, PII leakage
Operational metrics:
latency (time to first token, tokens per second), cost per 1,000 queries, throughput
API vs Self-Hosted LLM: Cost Comparison
We calculate your break-even point from your expected query volume, prompt length and latency needs before recommending either.
| Factor | Proprietary API | Self-hosted open-weight model |
|---|---|---|
| Upfront cost | None | GPU infrastructure setup + engineering |
| Cost structure | Pay per token | Pay per GPU-hour (fixed capacity) |
| Economics at low volume | Usually cheaper | Usually more expensive (idle GPUs) |
| Economics at high, steady volume | Can become expensive | Often cheaper per query |
| Data control | Vendor processes data (zero-retention terms available) | Full control |
| Model updates | Vendor-controlled | You control versions |
| Ops burden | Minimal | Requires LLMOps |
Enterprise LLM Use Cases
| Industry | LLM use case | Why custom or fine-tuned helps |
|---|---|---|
| Finance | Financial document analysis, earnings call summarization, compliance checking | Domain terminology, strict output formats |
| Healthcare | Clinical note structuring, medical coding support, patient communication drafts | Medical vocabulary, private deployment for PHI |
| Legal | Contract clause extraction, redlining, legal research | Precision on legal language, confidentiality |
| Insurance | Claims summarization, policy comparison, underwriting memos | Structured extraction at high volume |
| Manufacturing | Maintenance manual Q&A, SOP generation, defect report analysis | Technical jargon, offline plant deployment |
| Retail & eCommerce | Product content generation, search query understanding, review analysis | Brand voice, catalog-specific attributes |
| Customer service | Intent classification, reply drafting, quality scoring | Cheaper, faster SLMs at very high volume |
| Multilingual markets | Local-language assistants (Hindi, Arabic and more) | Better performance in under-represented languages |
How Much Does LLM Development Cost?
Building on an LLM API with RAG typically costs $15,000–$80,000; fine-tuning an open-weight LLM typically costs $30,000–$150,000+; training a model from scratch can cost millions. Cost depends on data preparation, model size, GPU compute, evaluation depth and deployment model.Hidden costs we plan for: data labelling, GPU compute for experiments, evaluation by domain experts, inference hosting and periodic retraining.
| Project | Scope | Typical cost | Timeline |
|---|---|---|---|
| LLM feasibility & benchmarking | Test 3–5 models on your tasks; recommendation | $5,000–$15,000 | 2–4 weeks |
| LLM app on API (+ RAG) | Production app with retrieval, guardrails, integration | $15,000–$80,000 | 4–10 weeks |
| PEFT fine-tuning (LoRA/QLoRA) | Dataset curation, fine-tuning, evaluation, deployment | $30,000–$80,000 | 6–10 weeks |
| Private LLM platform | Self-hosted model(s), serving, SSO, monitoring, fine-tunes | $60,000–$150,000+ | 10–16 weeks |
| Domain LLM (continued pre-training) | Large corpus training, alignment, full evaluation | $150,000+ (compute-dependent) | 4–6+ months |
LLM Development Challenges, and How We Solve Them
| Challenge | Solution |
|---|---|
| Not enough quality training data | Synthetic data generation, distillation from larger models, active learning |
| Hallucinations | RAG grounding, faithfulness evaluation, constrained output formats |
| High inference cost | Quantization, SLMs, routing, caching, batch serving |
| Data privacy | Private deployment, PII scrubbing in training data, access controls |
| Bias & safety | Bias evaluation, preference tuning, output filters, red-teaming |
| Model drift & obsolescence | LLMOps monitoring, adapter-based updates, model-agnostic architecture |
| Regulatory compliance | Documentation, model cards and audit logs aligned to the EU AI Act and NIST AI RMF |
LLM Development Tech Stack
| Layer | Tools |
|---|---|
| Training | PyTorch, Hugging Face Transformers, PEFT, TRL, DeepSpeed, Axolotl, Unsloth |
| Data | Spark, Pandas, Label Studio, Argilla, synthetic data pipelines |
| Evaluation | lm-evaluation-harness, Ragas, DeepEval, LangSmith, custom LLM-as-judge |
| Serving | vLLM, Hugging Face TGI, NVIDIA Triton / TensorRT-LLM, Ollama (edge) |
| Orchestration | LangChain, LangGraph, LlamaIndex |
| Cloud | AWS Bedrock & SageMaker, Azure OpenAI & Azure ML, Google Vertex AI |
| MLOps | MLflow, Weights & Biases, Kubernetes, Terraform |
Why Choose PerfectionGeeks for LLM Development
Benchmark-first
We prove whether fine-tuning beats prompting or RAG on your data before you pay for training.
Cost-aware engineering
PEFT, SLMs, quantization and routing keep training and inference budgets in check.
Private by design
Self-hosted deployment so sensitive data never leaves your environment.
Model-agnostic
Proprietary and open-weight, with no lock-in.
Full-stack delivery
From dataset to production app, including integration and LLMOps.
Find Out If You Need a Custom LLM
Share your use case and sample data. We'll benchmark the options and tell you whether prompting, RAG, fine-tuning or a private LLM gives the best accuracy per dollar.
Client Testimonials
PerfectionGeeks provided excellent service from start to finish. Their team was professional, easy to work with, and delivered exactly what we needed on time. We’re very pleased with the outcome and would gladly recommend them to others.
Thanaboon Mingkaew
CEO, SMART IOT
We partnered with PerfectionGeeks for a custom accounting automation software, and the experience was excellent. Their team delivered a high-quality solution that streamlined our operations and improved efficiency. Professional, skilled, and reliable — we’re already working with them on another project.
Saarthak Gupta
Founder, Ceos Accounting Services
As COO of TRP Construction Management, I’m proud of our growth from 300 to 16,000+ SKUs and 4,000+ vendors since 2022. Thanks to Shrey Bhardwaj and Perfection Geeks for delivering scalable, future-ready technology solutions that transformed our vision into a seamless pan-India platform. Highly recommended technology partner.
Mayank Pathak
COO, TRP Construction Management
PerfectionGeeks Technologies transformed our vision into reality with innovative technology solutions. Their professionalism, timely delivery, transparent communication, and commitment to quality made the entire collaboration smooth, reliable, and highly satisfying.
Armaan
Cofounder, Kzminer
I highly recommend PerfectionGeeks Technologies for mobile and web development services. Their expertise, dedication, creative ideas, and customer-focused approach helped our business achieve impressive digital growth successfully.
Chetna
Founder CEO , Klicked
Choosing PerfectionGeeks Technologies was the best decision for our company. Their skilled developers created a user-friendly platform, provided constant assistance, and ensured exceptional performance beyond our expectations every step.
Mark
CTO, IMSMART
Working with PerfectionGeeks Technologies was an excellent experience. Their team delivered our project on time with outstanding quality, smooth communication, innovative solutions, and professional support throughout the entire development process.
Naveen
Founder Director , Way2Kart
PerfectionGeeks Technologies delivered a smooth, professional experience with excellent communication and technical expertise. Their team understood project requirements clearly, provided timely updates, and ensured high-quality results that exceeded expectations.
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CTO, Sethu Fishries
PerfectionGeeks Technologies impressed with their innovative solutions, responsive support, and commitment to quality. The team handled every detail professionally, delivered on time, and created a seamless experience from start to finish.
Mervin
Cofounder & CTO, Clover Infinity