Private AI
The four boundaries that make a deployment genuinely private, and the reference architecture.
Private AIOn-Premise AI
Full deployment in your own datacenter — including air-gapped environments with no external connectivity at all.
The problem
When the cloud is not an option
Some organizations do not get to choose. Classification requirements, export control, contractual obligations, or a regulator's position can rule out cloud entirely — not as a preference, but as a condition of doing business.
For others the driver is economics. At sustained high volume, per-token pricing stops being cheap and reserved GPU capacity you already own starts to look very different on a three-year view.
Either way, the model was never the hard part. The work is hardware sizing, cluster deployment, model serving, scheduling, and building an operational practice that keeps it running — usually inside a team that has never operated GPU infrastructure before.
Scope
What we deliver
- Hardware specification and sizing — Capacity modeled from your measured workload — concurrency, token throughput, model size, latency target — not from user count.
- GPU cluster deployment — Node configuration, interconnect, scheduling, quota, and multi-tenancy so more than one team can share the investment.
- Model serving stack — Inference engine deployment, continuous batching, quantization where it pays, autoscaling, and multi-model routing.
- Offline update process — Model and dependency updates that work without a package registry, staged and integrity-verified under your change control.
- Monitoring without external telemetry — Observability that functions with no outbound connection, because most vendor tooling assumes one.
- Runbooks and handover — The operational documentation your team needs to own this after we leave.
Detail
Air-gapped changes the lifecycle, not the capability
Model updates become releases
Weights are staged, integrity-verified, transferred under media control procedures, then validated against your evaluation set inside the enclave before promotion.
Dependencies cannot be resolved on demand
Every package, base image, and CUDA component has to be mirrored internally and version-pinned. This is the step most projects discover late.
Observability stays internal
No hosted APM, no vendor telemetry. Metrics, traces, and evaluation dashboards all run inside the boundary.
Iteration is slower by design
Expect the longest change cycle of any deployment model. Plan the roadmap around it rather than fighting it.
Questions
Frequently asked
How much hardware do we actually need?
Far less than most initial estimates, once the workload is measured rather than assumed. Quantization and continuous batching frequently bring requirements down by a large factor. We size it during the assessment against real concurrency and latency targets.
Can we start in the cloud and move on-premise later?
Yes, and it is often sensible — prove the use case on non-sensitive data, then move. Build behind an abstraction and keep the evaluation harness portable and the migration is real work but not a rewrite.
Who operates it afterwards?
Your team with runbooks and training, or us under a retainer. Both are legitimate. We will tell you honestly which fits your staffing rather than defaulting to the one that bills more.
Related
Edge AI
Inference at the point of use, for latency and disconnected operation.
Edge AIStart with an assessment, not a proposal.
A structured evaluation of your data, infrastructure, security constraints, and candidate use cases — delivered as a prioritized roadmap you own.
Direct response from an engineer. Typically within one business day.