Some organizations can't send their data to anyone's cloud — not for training, not for inference, not even for a moment. We built a coding agent for exactly that constraint: zero data egress. Here's what that actually takes.
"Can we use AI?" and "Can our data leave the building?" are often on a collision course. For a bank, a defense contractor, or a hospital, the second question has a fixed answer — no — and that seems to rule out modern AI, which for most people means an API call to a frontier lab. It doesn't. It just moves the whole system inside your walls.
See the difference: where does your data go?
Toggle between the two architectures. The only thing that changes is the most important thing — whether your data crosses the boundary.
What actually changes on-prem
Going on-prem isn't one decision; it's a chain of them. The three that matter most:
- Model — you trade the strongest frontier model for a strong open-weight model you can run yourself. The gap is smaller every month, and for a scoped task (like coding in one language on one codebase) it often disappears.
- Hardware — inference now runs on GPUs you own or rent inside your perimeter. Right-sizing this is where budgets are won or lost.
- Retrieval — your private data is indexed in a vector store that also lives inside the network, so the model can ground its answers without anything leaving.
The honest trade-offs
On-prem buys you privacy and control. It costs you some raw capability and some operational overhead. Here's the shape of it — the right choice depends on which bars you can't compromise.
Key takeaways
- On-prem AI keeps data, model, and retrieval all inside your network — zero egress.
- You trade a little raw model capability for privacy, control, and easier compliance.
- For scoped tasks, open-weight models on your own hardware are more than enough.
Need AI that never leaves your network?
We've shipped fully on-prem systems for security-sensitive clients. We can scope yours.