On-Premise AI Deployment: Key Considerations for Data-Sensitive Teams
On-premise AI deployment for data-sensitive teams: when it is worth it, hardware sizing, versioning and audit trail, security review, and who owns updates.
This is the decision-and-requirements guide to on-premise AI deployment: when it is worth it, and the checklist your security review will ask for anyway. For infrastructure options and the architecture, see the self-hosting guide; a separate guide compares what each "private inference" pattern actually protects.
When on-premise AI deployment is worth it
- The data can't leave, full stop. Contracts, policies, or regulation name your infrastructure as the boundary.
- The network is the boundary. Air-gapped or segmented environments where egress isn't yours to grant. Plan for how the model gets in: see weight staging below.
- Auditability is contractual. You must show where data went, and "a vendor's assurance" isn't the accepted answer.
- Predictable cost at committed volume. Fixed infrastructure beats variable per-token at scale, sometimes.
When it isn't: data policies that accept provider terms with retention controls (cheaper, less ops), bursty low volume, or teams with no one to own a server.
What actually runs on-prem
Not "AI", a specific workload. The easiest first candidate in automations is the decision step: classification, routing, gating, rating. It's small (a 0.8B decision model like Saina Helm runs on a CPU VM; sizing details), stateless per request, and easy to audit (options in; probabilities and one answer out). Generation workloads (drafting, summarizing) are heavier and better tested second.
The requirements checklist
Hardware sizing. Decide by workload: decisions-only → CPU VM; generation or throughput → GPU. Size for peak concurrency, not average. For Helm that means replica count: one server process handles one request at a time (others get 503 and retry), so peak concurrent requests ≈ replicas behind a load balancer.
Containerization. Run the model as a container (Docker/Podman) so the runtime is reproducible and the security team gets something scannable. Pin a version tag (Helm: ghcr.io/run-saina/saina-server:0.1.2) and, better, its image digest rather than a floating tag.
Weight staging for air-gapped networks. Helm's container carries no weights; on first start it downloads the pinned revision (~1.7 GB) from Hugging Face. Where egress is denied, pre-stage the weights on an internal store, mount them read-only into the container, and set SAINA_CHECKPOINT to that local path. Do this before go-live, not during it.
Model versioning and audit trail. Pin the exact weights revision (Helm: SAINA_REVISION, a Hub commit; v1.0 current). The Helm server doesn't write request logs, so the audit trail is recorded by the calling workflow: request time, question IDs, options hash, outcome (selection and reason), model revision, server version. Avoid logging request content unless you've decided you need it; it makes the log itself sensitive. Keep the option-set definitions in version control alongside the workflow, so "what model decided this, with what rules, on October 3rd" has a direct answer.
Security review inputs they will ask for:
- Data flow diagram: what enters, what's stored (ideally nothing), what egresses (ideally nothing).
- Authentication and authorization on the endpoint; key rotation procedure.
- TLS everywhere; no plain HTTP between hops.
- Container image provenance and scan results.
- Model and weights licence terms, reviewed for your use (read the model card and repository licence files).
- Cross-origin settings: Helm's server allows
https://saina.run(its browser playground) by default; setSAINA_CORS_ORIGINSto an empty string unless you use it. - Logging and retention: what's recorded, for how long, who can read it.
- Failure behavior: what happens to in-flight data on crash/restart.
Who owns updates. Name it now: who applies OS patches, container updates, model upgrades; what the test ritual is before each (labeled set re-run); what the rollback is. On-prem's real cost is this rota, not the VM.
Support model. What breaks at 2am, who notices (monitoring), and who fixes it. If the answer is "nobody", reconsider hosted.
Helm as the worked pattern
The deployment shape this guide describes, concretely: Saina Helm on an internal VM in a pinned container (tag 0.1.2, digest pinned), API-key auth, TLS at the reverse proxy, CORS origins empty, weights pre-staged where egress is denied, decision mode with thresholds, and the calling workflow logging per-question reasons. All model execution inside your network; no external calls at inference time. Weight updates are a deliberate revision bump with a test-set re-run, not a silent provider change.
Decision checklist
- The boundary requirement is written down and cites a real constraint.
- Workload chosen; decision steps first.
- Sizing for peak concurrency (replicas); CPU-first if decisions-only.
- Weights pre-staged if the network is air-gapped.
- Versioning + audit logging specified before the security review.
- Licence terms and CORS settings reviewed.
- Update ownership named; test ritual and rollback defined.
- 2am answer exists (monitoring + owner) or the decision is reconsidered.