Private & Self-Hosted AI Infrastructure
All the capability of hosted AI, none of the data leaving your building.
Request a consultationFor some businesses the question is not whether to use AI but whether they are allowed to. Healthcare records, legal files, financial data, and proprietary designs often cannot be sent to a third-party API — by regulation, by contract, or by common sense. The answer is to bring the model to the data instead of the other way around.
Open-weight language models have become capable enough to run real workloads on a single well-specified server or a small cluster. We design and build that infrastructure: the hardware sizing, the model serving layer, the GPU or Apple Silicon scheduling, the monitoring, the security boundary, and the operations to keep it healthy. We run this kind of infrastructure ourselves, on our own clusters, and we deploy it the same way for clients — declaratively, from version control, so every change is reviewable and reversible.
The result is an AI platform that your automation, knowledge assistants, and applications can call the same way they would call a cloud API, with none of the data leaving your control and predictable monthly costs instead of per-token bills.
This is for you if
- You are in healthcare, legal, finance, defense, or another field where data residency is non-negotiable.
- Your per-token API bill has grown to the point where owning the hardware would be cheaper.
- You need AI capability in an environment with limited or no internet access.
- You already have GPUs or capable hardware sitting underused.
Private AI Infrastructure: what's included
Architecture and hardware sizing
Which models fit your workload, what hardware serves them at the latency you need, and what it costs — GPU servers, Apple Silicon nodes, or a private cloud instance, compared honestly.
Model serving and orchestration
Production model serving with load balancing, health checks, and automatic failover across nodes, deployed on Kubernetes or a lighter-weight stack when that is the right fit.
Private cloud AI
AWS, Azure, or GCP deployments inside your own accounts and networks — private endpoints, no public exposure, and infrastructure defined as code so it can be rebuilt on demand.
Security and access control
Network isolation, authentication in front of every model endpoint, audit logging of every request, and encryption at rest and in transit. The boundary is designed, not assumed.
Monitoring and cost visibility
Dashboards for throughput, latency, token usage by team or application, and hardware health, so you can see exactly what the platform is doing and what it costs.
Managed operations
Ongoing patching, model updates, capacity planning, and on-call support, so your team gets a platform rather than a project.
How an engagement works
Workload profile
We measure what you actually need to run — request volume, context sizes, latency tolerance — because that, not the model name, determines the hardware.
Design and cost model
A reference architecture with a three-year cost comparison against hosted APIs, so the decision is financial as well as technical.
Build from code
Every component is defined in version control and deployed from it. Nothing is configured by hand, so the environment is reproducible and auditable.
Handover or operate
We document and train your team to run it, or we run it for you under a managed services agreement. Either way you own the infrastructure and the code.
What you walk away with
- A private model endpoint your applications call like any cloud API.
- A sized, costed hardware specification with a comparison against hosted alternatives.
- Infrastructure defined entirely in code, with monitoring and alerting from day one.
- Documented operations and a team that can run it, or a managed agreement so they do not have to.
Frequently asked questions
Are open-weight models good enough for real work?
For most business tasks — summarization, extraction, classification, drafting, question answering over documents — yes, and the gap closes every few months. For the hardest reasoning tasks a hosted frontier model may still be better, and we design systems that can route those specific requests out when it is safe to do so.
How much hardware does this take?
Less than most people expect. A single server with a modern GPU, or a small set of Apple Silicon machines, handles a mid-size business's internal assistant and automation workloads. We size it from your measured workload, not a vendor's guess.
Can this run in our existing cloud account?
Yes. Private cloud deployment inside your own AWS, Azure, or GCP account keeps the data under your control without buying hardware, and is often the right first step.
Who keeps it running?
Your choice. We hand over full documentation and training so your team can operate it, or we operate it for you with monitoring, patching, and support under a monthly agreement.
What about model updates?
New models are tested against your workload on a staging endpoint before promotion. Because the deployment is defined in code, a rollout — or a rollback — is a single reviewed change.
Related services
Talk to us about Private AI Infrastructure
Start with a free 30-minute discovery call. We'll tell you honestly whether this is the right fit.
Request a consultation