Most teams reach for a public LLM API first because it is the fastest way to a working prototype. But for the customers we build for, the deciding question is rarely quality. It is where the data is allowed to go. A prompt that leaves the perimeter is, for many railway and defence workloads, a compliance event on its own.
Here is how the two approaches actually compare on the axes that matter once you move past the demo.
| Dimension | On-prem private LLM | Public LLM API |
|---|---|---|
| Data residency | Stays inside your network; nothing leaves the perimeter | Prompts and context transit a third-party cloud |
| Latency | Predictable on local hardware; no round-trip to a vendor | Depends on the vendor and your uplink |
| Upfront cost | Higher: you provision and own the GPUs | Near-zero to start |
| Cost at scale | Flattens; you pay for hardware, not per token | Grows with every request |
| Model freshness | You choose when to upgrade weights | Vendor ships improvements continuously |
| Operational burden | You run inference, patching, and fleet health | Vendor handles the infrastructure |
How we think about the choice
There is no universal winner, only a workload. When the data is sensitive, the site is air-gapped, or the request volume is high and steady, a private model on hardware you control usually wins on both compliance and long-run cost. When you are exploring, volumes are low, and the data is non-sensitive, a public API gets you moving faster.
Where we can help is the harder middle: standing up a private LLM that meets RDSO realities without pretending the operational cost is zero. If you are weighing this trade-off for a regulated deployment, talk to us.