The decision
Rent, build, or own.
The comparison, stated plainly. Each path has real advantages; the question is what you are willing to give up.
| Dimension | Rent (hyperscaler) | Build (in-house) | Own (KinHelm) |
|---|---|---|---|
| Where your data lives | Vendor infrastructure, vendor jurisdiction | Yours | Yours: on-prem, private cloud, or air-gapped |
| Who sets policy | Vendor terms first, your configuration second | You, if you build the enforcement | You; WALDO enforces what you write |
| Operational intelligence | Accrues partly to the vendor | Accrues to you | Accrues to you |
| Time to value | Days | Years | Weeks: platform plus 600+ agents ship ready |
| Cost profile | Per-token forever, priced by the vendor | Millions up front, then a permanent team | Platform license plus your own infrastructure |
| Talent required | Low | A team the market cannot reliably supply | Your existing IT and security staff |
| Model ownership | None | Full, if you can train them | Full: models run and tune on your hardware |
| Governance & audit | Vendor attestations | Whatever you build | Built in: policy enforcement and audit trail |
| Exit risk | High: data, workflows, and habits locked in | None | None: self-hosted and exportable from day one |
Where renting wins
Frontier-scale general models and instant elasticity. If your workload needs the largest models on earth and your data can leave, renting is rational.
Where building wins
Total bespoke control with no license anywhere. If AI is your core product and you can staff a platform team indefinitely, building is rational.
Where owning wins
Everywhere the data cannot leave and the platform team does not exist: the majority of organizations that need working, governed AI without surrendering it.
Next step
Bring your constraints to the comparison.
A briefing maps the three paths against your data residency, budget, and staffing reality.