.svg){kind=logo}
Data stays put
Prompts, files and outputs stay on your network, under your retention rules.
Private LLM deployment
Private LLM: run language models inside your own boundary.
Deploy open or frontier models in your VPC, your data centre or an air-gapped site. Then add what a private model alone can’t give you: private context and control over every decision an agent makes.
- VPC
- On-prem
- Air-gapped
- Open-weight and frontier models
deployment.spec
Inside your boundary
Model
Open-weight, 70B class, fine-tuned
Hosting
On-prem data centre, in-country
GPUs
Sized to workload
Data boundary
No prompts or outputs leave the network
Access policy
Role-based, enforced at the gateway
Decision control
Context OS policies and decision traces run inside the same boundary as the model.
Illustrative spec. Every deployment is sized to your workloads.
Works with
.svg)
What is a private LLM?
A model you run, on infrastructure you control
A private LLM is a large language model deployed inside an organization’s own environment, such as its cloud account, data centre or an isolated network, so prompts, documents and outputs never pass through a shared public service.
Regulatory fit
Supports data residency rules such as India’s DPDP Act, the UAE PDPL and sector regulators.
Predictable cost
At steady, high volume, owned capacity can cost less than paying per token.
Your own tuning
Fine-tune on your terminology and documents without sharing them with a vendor.
Access control
Manage who can access models, prompts and sensitive documents with your own permissions and authentication policies.
Private LLM deployment options
Four levels of privacy, one set of controls
“Private” covers a range. Pick the level each workload needs. Context OS applies the same policies and decision traces across all of them.
| API with zero retention | VPC-hosted model | On-premise LLM | Air-gapped | |
|---|---|---|---|---|
| Where it runs | Provider’s cloud, under a no-retention agreement | Your cloud account, in your chosen region | Your data centre | An isolated network with no outside connection |
| Data leaves your network? | Yes, encrypted and not stored | No, stays in your account | No | No |
| Model choice | The provider’s frontier models | Frontier models through private cloud endpoints, or open-weight models | Open-weight models | Open-weight models |
| Setup effort | Low | Medium | High | Highest |
| Best for | Low-sensitivity tasks that need top quality | Most sensitive business data | Regulated and residency-bound workloads | Defence, critical infrastructure, classified data |
Choosing the model
Open-weight or frontier: the trade-offs
There is no single best private model. We match the model to each task and keep the option to change it later, because Context OS is model-agnostic.
| Open-weight models (self-hosted) | Frontier models in your cloud | |
|---|---|---|
| What it is | Families such as Llama, Mistral and Qwen, downloaded and run entirely on your hardware. | Frontier models offered inside major clouds, such as Claude on Amazon Bedrock or Google Cloud Vertex AI, called through private endpoints in your account and region. |
| Pros | Full control, works on-prem and air-gapped, fine-tunable, no per-token fees | Top-tier quality, no GPUs to run, enterprise data-handling terms |
| Cons | You own the GPUs and operations; quality can trail the top frontier models on hard reasoning | Needs a cloud connection, so not for air-gapped sites; region availability varies |
| Worth knowing | Check each model’s licence terms for commercial use | Usage is billed by the cloud provider |
Reference architecture
What a production private LLM stack includes
-
01
Inference serving
High-throughput serving with engines such as vLLM, with batching, quantization and autoscaling.
-
02
Model gateway
One entry point for every app: authentication, rate limits, routing between models, logging.
-
03
RAG on private data
Retrieval over your documents and systems, respecting each user’s existing permissions.
-
04
Fine-tuning
Parameter-efficient tuning on your terminology and tasks, done inside your boundary.
-
05
Evaluation
Task-specific evals for accuracy, safety and drift before and after every model change.
Why a private model isn’t enough
A private model keeps data in. It doesn’t make decisions safe.
Once a private LLM powers agents that approve, update and send things, the risk moves from where data sits to what the agent does with it. A private model doesn’t know your policies, your approval limits or which records a user may see. Context OS adds what it is missing.
Cost and sizing
What drives the cost of a private LLM
Cost depends far more on workload than on the model name. We size every deployment from your real usage.
01
Model size
Larger models need more GPU memory per instance. Quantization can cut this substantially.
02
Concurrency
How many users and agents run at peak sets the number of serving instances.
03
Context length
Long documents and long agent runs need more memory and more compute per request.
04
Availability
Redundancy, disaster recovery and multiple sites add capacity.
FAQ
Private LLMs, answered
Straight answers on private LLMs, deployment options, model choice and how decisions stay governed. Related: Sovereign AI.
A language model deployed inside your own cloud account, data centre or isolated network, so prompts, documents and outputs never go through a shared public service.
An on-premise LLM is one kind of private LLM, running in your own data centre. A private LLM can also run in your VPC or in an air-gapped network.
You can call Claude through cloud platforms such as Amazon Bedrock or Google Cloud Vertex AI from your own account and region. It isn’t self-hostable, so for on-prem or air-gapped sites we use open-weight models.
For many focused enterprise tasks, a well-chosen and tuned open-weight model performs well. For the hardest reasoning, frontier models often still lead. We run evals on your tasks before you choose.
Often not at first. Retrieval over your documents plus good context handles most needs. Fine-tuning helps for specialist language, formats or tasks.
It depends on whether hardware is already in place and how many systems need connecting.
We deploy and run the model stack, and Context OS adds private context, policy checks and decision traces, so agents built on your private LLM are safe to put into production.
Plan your private LLM deployment
In one session, we pick the deployment level, shortlist models for your tasks and size the infrastructure.