TECH NOTE / AI INFRASTRUCTURE
Running a model inside the company is not automatically safer, cheaper or more useful. It makes sense when control, latency, data ownership and workload justify the infrastructure.
Running AI locally can provide more control over data paths, the execution environment and system availability. It also introduces hardware, updates, monitoring, security and operational capacity that someone has to manage.
Cloud services remove much of that complexity and make powerful models easy to access. In exchange, the company depends on an external service and needs to design data handling carefully.
A local deployment becomes interesting when data or documents must stay inside a defined boundary, latency needs to be predictable, offline operation matters or the workload is stable enough to justify dedicated infrastructure.
If workloads are irregular, change frequently or need very large models, cloud can be more efficient. It is also useful during discovery, when we do not yet know which workload deserves dedicated infrastructure.
The architecture can also be hybrid; it does not need to fit one category permanently.
A useful decision starts with four questions: what data enters, what data leaves, what capability is required and what level of external dependency is acceptable.
Only then does it make sense to compare costs, hardware, APIs and models.
On-premise is an architectural choice driven by control, latency or dependency requirements.