Garnet vs. rolling your own private AI: a commissioned build compared

Garnet vs. rolling your own

Both keep your data in your building. The difference is who does the work, and who you call when it breaks.

**Where the work lands.** One row per dimension of the job. The *load* column is how much of that dimension falls on you if you roll your own, on a 0–100 scale: higher means more of it is yours. These are our own estimates, to compare the parts with each other. Ownership is a tie: both paths keep your data in your building.
Dimension	Roll your own	Load on you (0–100)	A Garnet build
The engineering	You or your team, weeks to months of real work	92	yesGarnet, start to finish
Time to running	Nights and weekends, then upkeep forever	80	yesInstalled and tuned, ready to use
Security hardening	A genuinely sealed offline boundary is easy to get subtly wrong	88	yesThe boundary, done right
Hardware	You research, size, source, and assemble it	68	yesSpecified and sourced for your work
When it breaks	You debug it alone when it stops answering	90	yesYou call one accountable firm
Ownership of your data	Yours, and it stays in your building	8 tied	tieYours, and it stays in your building.

Roll your own

Everything you'd wire together.

The parts exist and they are good. Putting them together, sealing it off and keeping it running is the job.

Garnet vs. rolling your own private AI

If confidential work can’t go to the cloud, you have two options: commission a Garnet, or build your own private AI from the parts that exist. Both keep your data in your building. The difference is the engineering time, getting the security right, choosing the hardware, and who you call when it stops working. A Garnet arrives finished and we stand behind it; building your own means all of that is yours.

01 / The engineering time

Someone has to do the integration, and it runs into months.

Every part of a private AI exists, and most of it is free. The work is fitting it together: picking AI models that suit your hardware, making them handle real use, connecting them to your documents, and keeping it all working through every update. A good engineer can do it. The question is whether you want them on it for months.

Commissioning a Garnet hands that work to people who have already done it. You are buying the finished system, not the parts.

02 / The security gaps

The boundary is the product, and it gives way quietly.

A private AI only stays private while every way out is closed. Several parts of the system want to reach the internet by default, and when one does, nothing visibly breaks: it just stops being private.

This is what most home-built systems get wrong, and it is hard to spot, because a leak looks exactly like working software.

03 / The hardware calls

Buying the wrong machine is an expensive, slow mistake.

Memory decides which AI models a machine can run at all, and the wrong choice can mean months waiting for parts. Power and heat matter in an office too, and they are usually discovered after delivery.

With a Garnet the hardware is ours. We choose it, provide it and maintain it under a managed lease, so a wrong call is ours to fix.

04 / Who answers

Who you call at two in the morning.

A home-built system is supported by whoever built it, for as long as they work for you. The question is what happens when that person is on leave, or leaves.

A commissioned build comes with a name to call: an audit retainer for architecture under continuous watch, cluster operations if you want the model layer run for you, or ad-hoc engineering at $600 an hour with a 24-hour response, available by the hour. You can also take the machine alone and run it yourself; it is yours to operate.

The honest summary

Build it yourself if the work is the point.

If you have the engineers, the time and the appetite, building your own is a fair choice and you will learn a lot. Commission a Garnet if you want the result rather than the project, if the material is too sensitive to learn on, or if you need someone else to be accountable for it.