01VPC
Single-tenant inference
Dedicated GPU instances serving your models behind a private endpoint.
LLM.co · Private AI & Custom AI DevelopmentPrivate AI in your own cloud
Dedicated GPUs inside your own AWS, Azure or GCP account. Your keys, your region, your network rules. The fastest way to start private AI without buying hardware.
FIG. — Supercomputer cabinets, LLNLLLM.co
A private cloud deployment gives you most of the control of on-premises with none of the procurement. Models run on GPU instances in an account you own, inside a VPC you control, with traffic that never touches a model vendor.
We deliver it as infrastructure code, so the whole environment can be reviewed, reproduced in another region or moved on-premises later.
| Clouds | AWS, Microsoft Azure, Google Cloud |
|---|---|
| Network | Private subnets and endpoints; no public model URLs |
| Encryption | Your KMS keys at rest; TLS in transit |
| Delivery | Terraform and containers in your repository |
| Tenancy | Single tenant, in your account |
What we build
What this looks like in practice.
02VPC
Regional deployments
Separate stacks pinned to the regions your data residency rules require.
03VPC
Burst capacity
Autoscaling GPU pools for batch jobs, with a hard budget ceiling.
04VPC
Path to on-prem
The same containers and gateway, ready to move to your hardware when volume justifies it.
What private cloud AI means
Private cloud AI runs open-weight models on dedicated GPU instances inside a cloud account your organization owns. The VPC, the IAM roles, the encryption keys and the logs are all yours. Requests travel from your applications to a private endpoint in that account and never reach a model vendor.
That is a different arrangement from the managed model services sold by the same cloud providers. Those services run models on infrastructure the provider operates, and you call them over a shared API. In a private cloud deployment you choose the model, pin its version and control every network path to it.
How a private LLM runs in AWS, Azure or Google Cloud
The architecture is the same on all three providers.
Each provider names its GPU instance families differently and offers them in different regions. We confirm which instances carry the GPUs your models need, such as NVIDIA H100, H200 or L40S, in the regions your residency rules allow.
- A landing zone with private subnets, private endpoints and no public model URLs
- GPU instances sized to your models, reserved or on demand, with cost alerts
- vLLM serving behind a gateway that exposes one OpenAI-compatible API with keys, quotas and routing
- Storage encrypted with your KMS keys, TLS in transit, and logs shipped to your SIEM
- Terraform and container images kept in your repository and reviewed by your team
When private cloud is the right starting point
Private cloud AI suits organizations that want private AI running without a hardware purchase. It fits pilots where usage is still unknown, batch workloads that spike, and teams that already run most of their systems in one cloud. It also handles data residency well, since each stack can be pinned to a specific region.
Costs scale with GPU hours. Once usage becomes steady and broad, owned hardware often costs less over its life. Because the system ships as containers and infrastructure code, moving it on-premises later is a deployment change. The application, models and gateway stay the same.
Security in your own cloud account
In your own account you keep the controls your cloud security team already uses: IAM policies, security groups, private endpoints, key management and the provider's audit logging. We add the AI-specific layer on top. Users sign in through SSO, the gateway enforces roles and document permissions, and every prompt, retrieved passage, answer and action is logged with the user and the model version.
The cloud provider still operates the physical hardware under its shared responsibility model. That is the main difference from on-premises, and for many security teams it is a familiar, already-approved arrangement.
What you receive
LLM.co delivers private cloud AI as part of custom AI development, so the environment arrives with a working application on top of it.
- Terraform modules and container images in your repository
- Application code, prompts and evaluation sets
- Cost dashboards plus GPU, latency and quality monitoring
- Architecture and data-flow documentation for your security review
- Runbooks for scaling, model upgrades and adding regions
How it works
Four steps, each one reviewed.
01
Landing zone
VPC, subnets, private endpoints and IAM roles set up to your security baseline.
02
GPU capacity
Instance types chosen and reserved for your models, with cost alerts.
03
Deploy as code
Terraform and container images, reviewed by your team and kept in your repository.
04
Lock down
No public endpoints, encrypted storage with your keys, logs to your SIEM.
Questions
Common questions.
How is private cloud AI different from our cloud provider's AI service?
A provider's managed AI service runs models on infrastructure the provider operates, behind a shared API. Private cloud AI runs open-weight models on dedicated GPU instances inside your own account. The GPUs, weights, keys and logs sit under your IAM policies and network rules, and you decide when the model changes.
Can we run a private LLM in AWS?
Yes. We deploy open-weight models on GPU instances inside your AWS account, in private subnets with no public endpoints, encrypted with your KMS keys. Everything is defined in Terraform in your repository, so your team can review it, reproduce it in another region or tear it down.
Can we run a private LLM in Azure or Google Cloud?
Yes. The same architecture runs in Microsoft Azure and Google Cloud, using each provider's GPU instances, private networking and key management. We check GPU availability in your preferred regions during sizing, since instance types and capacity vary by region and provider.
How much does private cloud AI cost?
Cost is driven by the GPU instances you run, how many hours they run and whether capacity is reserved. Model size, concurrent users and batch volumes set the instance count. We model expected usage, set cost alerts and budget ceilings, and compare the total against on-premises hardware so you can see the break-even point.
Is our data used to train anyone's model?
No. Prompts, documents and outputs stay in your account and are never sent to a model vendor. Open-weight models run as files on your storage. If we fine-tune a model for you, that training runs in your account and the resulting weights belong to you.
Do we need GPU quota from our cloud provider?
Usually, yes. Most accounts start with low or zero quota for large GPU instances, and increases need a request to the provider. We identify the instance types and quantities early so your team can file the request while the rest of the build proceeds.
Can we move on-premises later?
Yes. The stack ships as containers and infrastructure code with the same gateway in every environment, so the system can move to your own GPU servers with little change. The applications, models and evaluation sets carry over as they are.
Which regions can private cloud AI run in?
Any region where your cloud provider offers the GPU instances your models need. Separate stacks can be pinned to different regions to meet data residency rules, with prompts, indexes, logs and weights kept in each region. We check availability during sizing.
Related
More private ai.
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