Deployment & economics

Should AI run on-premise or in the cloud?

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.

In plain terms

On-premise means running AI on infrastructure you own and control, while cloud means using shared infrastructure rented from a provider.

In the cloud, you call a vendor's model and pay per use. On-premise, you run a model on machines you control, so the data stays inside your network. Cloud is the default when the data is allowed to leave and you want to start quickly. On-premise is the answer when the rule is that the data stays put.

Do not choose on-premise because it sounds more serious. You take on the machines, the staffing, and the updates. Do not choose cloud because it is easier if a contract forbids that data from leaving. Decide the data rule with legal and security first. The architecture follows that sentence, not the other way around.

Think of it this way: 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 looks at cloud AI, but the rules say all processing must stay inside the country. They download a model and run it on government-managed servers.

A government office must keep citizen records inside the country. They download a model and run it on their own machines. A sister agency with public information calls a cloud model the same month and launches sooner. The difference was the rule about the records, not a preference for one architecture.

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 move on-premise to save money at the start. Servers, upkeep, and the people who run them cost more than cloud prices until your usage is large and steady.

RaftLabs prices the running cost before the build, so a feature people like does not become a loss. You get a number for a busy month, not only a demo. The related work on our side is Cloud migration.

This sits with the other deployment & economics terms on the glossary. What you pay to run AI, and the choices that change the bill. Worth reading next: API, Open vs Closed Models, and Inference Cost.

Common questions

It keeps the data in your building, which matters when the data cannot leave. It is not automatically safer. You still have to patch, control access, and watch it. A well-run cloud service can be safer than a server nobody owns. Security is the operating discipline, plus the question of where the data is allowed to go.
Yes, if you design the workflow so the model is a part you can swap. Keep your documents, tests, and rules in your systems either way. Moving later still costs a project. If you already know the data cannot leave, start where you will end. Switching under a deadline is the expensive version.

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