On-prem or cloud? It’s a foundational AI infrastructure decision, and the right answer depends on your data, compliance needs, and control requirements — not a default. Here’s the trade-off, and how dgm helps you decide. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry.)
The trade-offs
Cloud AI:
- Faster to start, easy to scale, lower upfront cost.
- But your data goes to a provider’s environment, and you depend on their cloud.
On-prem (or private) AI:
- Maximum data control and residency — your data stays in your environment.
- Better for strict compliance and highly sensitive workloads.
- But more infrastructure to manage and a higher upfront commitment.
How to choose
The decision hinges on a few factors:
- Data sensitivity — how sensitive is the data the AI will touch?
- Compliance — do your regulatory obligations demand data stay in your environment?
- Infrastructure — what do you already run?
- Control — how much control do you need over where data and models live?
If sensitivity and compliance are high, lean on-prem/private; if not, cloud or private-cloud is usually faster and simpler. Many businesses land on a hybrid or private-cloud middle ground.
Don’t make it a one-way door
The deployment choice shouldn’t lock you into one vendor’s cloud. A model-agnostic, flexible platform keeps options open, so you can adjust as needs change rather than being trapped (see how to avoid AI vendor lock-in).
How dgm helps
dgm helps you choose based on your data sensitivity and compliance needs in its $399 assessment, then implements accordingly on a platform that keeps your data under your control — and avoids locking you into one vendor’s cloud — as part of the $3,999/month implementation. If you’d rather explore the platform yourself first, go straight to osFoundry; if you want help choosing and implementing, that’s where dgm comes in.