Private LLM Development

Your models, inside your boundary

Private LLM development services — on-premise, VPC and air-gapped LLM deployment, fine-tuning on private data, GPU sizing and security hardening — so prompts, data and weights never leave your control.

Talk to a private LLM engineer Why go private

Air-gapped

Option available

Llama / Mistral

& other open weights

Quantised

To cut hardware cost

Quick answerPrivate LLM development is building and deploying large language models that run entirely within your control — self-hosted on your infrastructure, in a private VPC, or air-gapped on-premise — so prompts, data and model weights never leave your boundary. It covers model selection, fine-tuning on private data, GPU sizing, security hardening and operations.

Why private

When a private LLM is the only option that clears governance

Data residency & sovereignty

Regulatory or contractual limits on where data and inference can run.

No third-party data movement

Prompts and outputs never leave your environment — a hard requirement in many sectors.

IP protection

Proprietary data and fine-tuned weights stay yours, stored where you choose.

Predictable cost at volume

Fixed infrastructure can beat per-token pricing for high, steady usage.

Offline / low-connectivity

Air-gapped or edge environments where a hosted API is not reachable.

Full control

Model version, update cadence and behaviour are yours to govern.

Private vs hosted

Private LLM compared with a hosted API

Private / self-hosted LLM compared with a hosted LLM API
Dimension	Hosted API	Private / self-hosted
Data leaves your boundary	Yes (to the provider)	No
Time to first result	Fast	Slower — infra to stand up
Cost model	Per token	Fixed infrastructure + ops
Best-in-class model access	Immediate	Open-weight models
Fit for regulated / air-gapped	Often blocked	Designed for it

How we deliver

A path to a supported private deployment

Select

Benchmark open models (Llama, Mistral and others) on your use case and hardware fit.

Size

GPU capacity to your latency and throughput targets; quantisation to reduce cost.

Deploy

Serving stack and private vector store, inside your VPC or on-prem.

Fine-tune

On your data, with access controls and audit — weights you own.

Operate

Monitoring, updates and LLMOps, or hand off a self-sufficient model.

Why Relevance Lab

Private LLMs, delivered and supported

Security-driven delivery

Built for financial services, healthcare, public sector and defence requirements.

Hardware realism

Right-sized to real usage, with quantisation to cut GPU cost.

Fine-tune on private data

Inside your environment, no external data movement, weights you own.

Platforms behind the team

RLCatalyst and Spectra accelerate build and run.

Audit & access control

Every prompt and response logged; least-privilege access.

Cloud or on-prem

Your AWS / Azure / GCP account, your data centre, or air-gapped.

350+

Data & AI specialists

150+

Data & AI projects delivered

170+

Certified engineers

30‑60‑90

Day roadmap to your first AI use case

Alliances & partners

AWS
DataStax
Salesforce
Snowflake

Related resource

See a private AI appliance in practice

“LLM in a Box” brings generative AI inside a Trusted Research Environment without data leaving the boundary.

Explore LLM Development Services

Resources

Private and secure LLM insights

Blog

LLM in a Box: generative AI inside the Trusted Research Environment

A pre-configured private AI appliance that brings LLMs to sensitive data without it leaving the environment.

Governed AI coding assistants with Research Gateway & Amazon Bedrock

Secure, self-service access to AI coding assistants with institutional control over data and usage cost.

Rethinking enterprise analytics with GraphRAG and context-aware AI

Why data engineering stalls on missing context — and how GraphRAG makes data relationships explicit for AI.

FAQ

Private LLM Development Services: frequently asked questions

Private LLM development is building and deploying large language models that run entirely within your control — self-hosted on your infrastructure, in a private VPC, or air-gapped on-premise — so prompts, data and model weights never leave your boundary. It covers model selection, fine-tuning on private data, GPU sizing, security hardening and operations.

Data residency and sovereignty requirements, contractual or regulatory limits on sending data to third parties, IP protection, predictable cost at high volume, and offline or low-connectivity environments. For many enterprises in the public sector, financial services, healthcare and defence, a private LLM is the only option that clears governance.

Open-weight models such as Llama and Mistral families, and other permissively licensed models, chosen on quality-for-task, context length, licensing and hardware fit. We benchmark candidates on your use case before committing.

GPU capacity sized to your latency and throughput targets (on-prem or in your cloud account), an inference serving stack, a vector store for retrieval, and monitoring. We right-size this to real usage rather than worst-case, and support quantisation to reduce hardware cost.

Yes — fine-tuning and adapter training run inside your environment on your data, with access controls, audit logging and no external data movement, producing model artefacts you own and store.

Content last reviewed: September 2026

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