AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
On-premise means running AI on infrastructure you own and control, while cloud means using shared infrastructure rented from a provider. Most teams should start in the cloud for speed. On-premise becomes worth its higher cost mainly when data residency, compliance, or scale demand it.
Cloud is renting a hotel room. On-premise is owning a house. The hotel is ready immediately, managed by someone else, and priced per night. The house is yours to configure, costs more upfront, and you handle the plumbing.
A government agency evaluates cloud AI but data sovereignty rules require all processing to stay within national borders. They deploy an open-weight model on government-managed infrastructure.
Start in the cloud. Move on-premise when a hard compliance requirement mandates it, when inference volume makes per-token pricing uneconomical at scale, or when the data cannot legally leave your infrastructure.
Do not go on-premise to save money at early scale. The infrastructure, DevOps, and model management overhead will cost more than cloud pricing until you reach significant, stable volume.
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