On-Premises AI
Definition
Operating AI models and infrastructure in your own or a dedicated, model-provider-independent environment — typically in your own data centre or in a sovereignly controlled hyperscaler setup. Generally requires open-weights models and your own MLOps infrastructure.
Noise — Signal
On-premises is often dismissed wholesale as "expensive and slow", or, in the opposite direction, glorified as "the only safe solution". Both miss the point. The question isn't cloud vs. on-premises but: which workloads, given their regulatory status, data sensitivity or volume profile, justify the effort of running your own stack? For high-frequency, latency-critical, privacy-sensitive inference, on-premises is often the economically and regulatorily superior option — for sporadic knowledge-work applications, rarely.
The right question
Not: "Should we go on-premises?" But: "Which of our AI workloads cross the thresholds at which on-premises pays — data sensitivity, regulatory status, volume, latency — and which open-weights models qualify in quality and licence for those workloads?"