Private AI Inference
Run selected models on organization-controlled hardware when local performance and model capability are sufficient.
On-Site & Self-Hosted
Some workloads are better kept under your control. We evaluate local infrastructure and self-hosted software as practical business options—not as a blanket replacement for cloud services.
Why consider it
A service may begin as the simplest option and become expensive as usage, storage, seats or API volume grows. In other cases, privacy, network availability or data movement makes a local system more appropriate.
What can be considered
Run selected models on organization-controlled hardware when local performance and model capability are sufficient.
Search, retrieval and AI-assisted access to internal documents without sending the entire knowledge base to a public service.
Keep scheduled processing, integrations and data transformation close to the systems they support.
Evaluate open-source or commercially licensed applications that can replace selected recurring SaaS costs.
Design local storage and supporting services where capacity economics, recovery requirements and data control justify it.
Keep workloads local while retaining cloud services for the parts that benefit from elasticity, external access or managed infrastructure.
Cost comparison
Hardware, power, backups, support, updates, licensing, staff time and replacement cycles all matter. A local system that saves subscription fees but creates an operational burden is not automatically a better system.
Where possible, we favour architectures that keep your data accessible, document dependencies and avoid unnecessary lock-in. The goal is to improve control without creating a fragile environment that only one person can maintain.
Evaluate a workload
We can compare your current subscription or cloud costs with realistic hardware, software and operating requirements before you commit to a migration.