Private AI infrastructure: features, cost and control
Compare deployment boundaries, model control and who operates the stack. HyperLake manages open-source stacks in your own cloud account.
Relevant capabilities
- Customer-controlled cloud account and region
- Managed data engines, model serving and agent workspaces
- Expert support included in the subscription
How should you compare the options?
- Define where data, model endpoints, logs and backups must reside; private AI is a deployment boundary, not a single product label.
- Assign operating responsibility for models, data engines, updates and incident response.
- Compare capacity, utilization, support and platform charges at the same workload and service level.
What should you bring?
- Cloud account, region and data-boundary requirements
- Representative workloads, models, throughput and latency targets
- Security, integration and operating-ownership requirements
What does a practical first workflow look like?
- Document the deployment boundary and workload requirements.
- Review the supported stack and separate confirmed requirements from open questions, including on-premises support.
- Pilot a representative workload with agreed quality and performance checks.
- Agree operational ownership, support and total-cost assumptions before moving production work.
What should you check before expanding?
- Verify which account and region hold data and compute.
- Measure workload quality, latency and utilization.
- Reconcile direct cloud charges, platform subscription and operating effort.
How should you evaluate the cost?
Your cloud compute is billed at 0% HyperLake markup, plus the HyperLake subscription. Compare equivalent capacity, utilization and operating effort; total savings depend on the workload.
What needs checking before switching?
Check supported cloud accounts, data boundaries, model requirements and migration scope with the team. On-premises availability must be confirmed separately.