Where should your AI live?
Wrong answer: wherever the vendor prefers. Right answer: wherever your data classification says. The machine deploys to all three, inside your fence line, in sovereign cloud, or split down the middle.
What grade of data are you holding?
Pick what you have. Try to move it. The machine tells you where it's allowed to go, no sales call required to find out.
1 · What grade of data are you holding?
Pick at least one grade above, the machine will show you where it can go.
Illustrative. Your compliance officer outranks this widget, bring them to the assessment.
An honest comparison. No winner column.
Each mode is correct for a different data grade. None of them is correct for all of them.
| On-Premise AI | Hybrid AI | Cloud AI | |
|---|---|---|---|
| Data ceiling | Anything: ITAR, EAR, classified-adjacent, your worst NDA. | Restricted data stays local; unrestricted data is free to route out. | Public through regulated commercial (PII/HIPAA-adjacent, with controls). |
| Time-to-first-token | ~6 weeks: bare rack to first fine-tuned answer. | Days on the cloud side; same 6-week runway on the local side. | Same day, sign the agreement, call the API. |
| Fine-tuning depth | Full SFT, continued pretraining, LoRA/QLoRA, no compute ceiling but the one you buy. | Full depth on local models; managed fine-tuning on the cloud-side model. | Managed fine-tuning APIs: LoRA-class adapters, provider's ceiling. |
| Cost shape (capex/opex) | Capex-heavy, racks, GPUs, power. Amortizes below the cloud crossover point. | Blended, capex for the local tier, opex for the burst tier. | Pure opex, pay per token, scales up and down with usage. |
| Scaling | Bounded by the iron you own. Scale = buy more racks, on a lead time. | Local tier is bounded; cloud tier is elastic within your gate's rules. | Elastic, provision in minutes, ceiling is your budget, not your rack. |
| Compliance artifacts | Signed model cards, zero-egress logs, offline registry hashes. | Same local artifacts, plus provider attestations for whatever crosses the gate. | Provider SOC 2 / FedRAMP / IL4-5 attestations under a shared-responsibility model. |
| Who runs it day-2 | Your team, with our runbooks, drift monitoring, sneakernet updates. | Your team runs the routing gate; each side is operated to its own model. | The provider runs the infrastructure; your team runs prompts, evals, and spend. |
There is no row where one column is objectively best. That's the point, pick by data grade, not by vendor preference.
Three clients. Three answers.
Same machine, three different data grades. The deployment mode followed the data, not the other way around.
Defense electronics contractor
PCBSpot runs 100% on-premise, inside their own fence line. No external call has ever left the building, and their NFF rate dropped 73% in the first quarter, because the diagnosis model saw forty years of test data it was never allowed to see over an API.
See howAerospace prime, 70B fine-tune
The corpus was 80% unrestricted tech pubs and 20% ITAR test data. Training entirely on-prem penciled out to 11 days on owned iron. Splitting the run, restricted data trained locally, everything else burst to cloud GPUs, brought the cloud-side leg down to 61 hours.
See howSaaS platform, 40k monthly users
Nothing in the product touches export-controlled or CUI data, it's ordinary commercial usage logs and public documentation. Managed cloud AI serves all 40,000 users for $8.4k a month, scaling up during launch weeks and back down after, with no hardware to depreciate.
See howDecision FAQ
Stop guessing. Grade the data.
A 30-minute deployment assessment: your data grades, your compliance reality, a straight recommendation on on-prem, cloud, or hybrid, and what each one actually costs. Run by people who've shipped all three.
Typically responds within 4 hours