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. 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.
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: you now own the model lifecycle, the GPU capacity planning, and the upgrade cadence. The right choice depends on which bars you can’t compromise.
With compliance teams, the winning argument was never “the AI is smarter.” It was “no data ever crosses the boundary, and here is the network diagram and audit log to prove it.” On-prem turns AI from a policy fight into an architecture review.
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.