The most common mistake we see isn't choosing the wrong AI model. It's treating the choice as permanent — and making it once, for everything, on day one.
It isn't one choice. It's a trajectory. Most organizations should start public, and move private only for the workloads that earn it.
Start public
Frontier APIs — Claude, GPT, Gemini — are the fastest way to prove something works. Best raw capability, lowest upfront cost, nothing to host. For any non-sensitive workload, and for proving a use case before you invest, this is the right place to begin. Shipping beats theorizing.
Move to hybrid as you scale
As usage grows, some workloads develop reasons to move — and usually only some. Weigh five forces per workload:
- Data sensitivity and regulation. Is this PII, PHI, or privileged? Does HIPAA, GDPR, or a contract constrain it?
- Quality bar. Does it need the absolute frontier, or is a strong open model plenty?
- Cost at scale. Spiky and low-volume favors public. High, sustained volume favors self-hosting.
- Latency and availability. Does it need on-prem, edge, or offline operation?
- Control and lock-in. How much does long-term portability matter versus speed today?
The answer is rarely all-or-nothing. It's a gateway that classifies each request and routes it: sensitive or high-volume work runs private, everything else stays on the frontier. Redact at the boundary, log every call, and abstract the provider so switching models is a config change, not a rebuild.
Go private for the slice that warrants it
Some workloads earn a full private, self-hosted deployment — regulated data, sustained volume, strict latency or control. Rarely everything. Deliberately the part that justifies the added effort.
The point
You don't have to pick perfectly on day one. You have to pick deliberately, per workload, and keep the decision current as models, prices, and regulations move. That's the work — and it's most of the value.