Own or Rent AI: When Your Data Cannot Leave the Building

Own or rent AI? For a team whose data cannot leave the building, the first question is not which costs less. It is whether the team can use AI at all, and what keeping the data in costs. There are three doors: send nothing, rent privately under a contract, or own the model. We priced the third and checked the rules behind the second, from sources read on 12 October 2026.

Who has to ask: records, closed networks and designs that cannot leave

A clinic summarising patient records and a law firm searching privileged documents both must control who can see those files. A defense lab on a closed network cannot reach a rented model, and a field crew with an unreliable connection cannot depend on one. A team guarding unreleased designs wants control: keep a local copy of a model and you keep that version, while a rented model is a fixed version that its maker eventually retires. A sustained workload can justify dedicated capacity.

Per-seat chat plans are not the comparison here. They are priced per person, they send every question out, and they buy a rented model, not a server. The comparison is between the three doors.

Model retirement, platform.claude.com.

Door one: send nothing, and give up AI on the restricted files

Door one is to keep AI away from the restricted work and use it on everything else. It avoids buying hardware, and the restricted files keep their current process.

Door two: rent privately, which HIPAA allows with the right written agreement

The US health-privacy rule, the Health Insurance Portability and Accountability Act (HIPAA), lets a covered entity disclose health information to a business associate that gives satisfactory assurance it will safeguard it. The assurance must be documented in a written contract or agreement that meets the rule, so a signed vendor contract alone does not establish compliance. The General Data Protection Regulation (GDPR) sets conditions for transferring personal data outside the European Economic Area. Neither rule bans hosted AI outright, so a contracted vendor can be a legitimate door.

Some cloud services offer regional routing or processing for supported models, and what that commitment covers differs by platform and model, so check which data and operations it includes. We priced none of these private deployments, so there is no number for this door. Your legal and security teams must check the service, the agreement and the data flows.

The limit of this door is the premises. If the records must stay on site, an off-site service still sends them beyond it, and the way to use AI on them is door three. The same goes for open models, whose files anyone may download, when they are rented from their makers: the data still goes to the maker.

HIPAA, US Code of Federal Regulations, 45 CFR 164.502 and 160.103 · GDPR transfers, European Commission · regional endpoints and processing, the vendors’ platform and pricing pages, read on 12 Oct 2026.

Door three: own, at $14,412 a month for a big-model server and $559 for a Mac Studio

Owning here means running a downloaded model on your own systems. Kimi K3, one of the largest open models we looked at, has 2.8 trillion parameters, the numbers a model learns in training, and its files take 1.56 terabytes. A server with eight B200 graphics processing units (GPUs) holds 1,440 gigabytes of GPU memory, so the files do not fit in it. That count is GPU memory only; spilling part of a model into the server’s host memory is outside this comparison.

A smaller model, GLM-5.3 at 753 billion parameters, takes about 467 gigabytes in the Unsloth 4-bit download, and should fit the same server. We found an 8-GPU B200 server listed at $384,228 in September 2026, though that was an out-of-stock listing and not a live quote. NVIDIA rates its own eight-GPU system at about 14.3 kilowatts at peak, which we use as the power draw. On our model, with the price spread over 3 years, power at full draw and an assumed 20 hours a month of admin time, it costs about $14,412 a month. That is an illustrative configuration, not the minimum cost of local AI, and it leaves out cooling, rack space and networking.

At desk scale, the Mac Studio with M5 Ultra starts at $5,499 with 96 gigabytes of memory. We estimate the 256-gigabyte version at about $9,500, because we could not read Apple’s price for it. A published review measured 41 tokens a second, counting reasoning tokens, for one user on a 256-gigabyte M5 Ultra with a mixed-precision GLM-5.3-Flash build, which Hugging Face lists at 321 billion parameters. Our purchase configuration may differ. Our estimate for owning it is $559 a month.

Apple lists a 512-gigabyte model for late October with no price, so we do not price it. Our post on how much memory each Mac needs for local AI covers the small models that suit it.

Model sizes, Hugging Face, Kimi K3 · GLM-5.3, Unsloth download, GLM-5.3-Flash · Server memory and power, NVIDIA DGX B200 · Server listings seen 21 Sep 2026, gpuperhour.com · Mac Studio launch, 25 Aug 2026, macworld.com · Speed test, macstories.net.

The capability trade-off: the best open model scores 46 against 58

Artificial Analysis, an independent testing firm, combines many tests into one score. Claude Opus 5.5 scores 58. GPT-6 Astra scores 53. The best open model, MiMo-V2.6-Pro, scores 46, which is 12 index points lower than Opus. These are the scores Artificial Analysis publishes for the versions it tested; we have not reproduced them on the local builds priced here.

GLM-5.3, the model we priced the server for, scores 45 and Kimi K3 44. GLM-5.3-Flash, the model that fits on a desk, scores 42. On LMArena, a public leaderboard where people vote between two answers, Claude Opus 5.5 scored 1507 and the best clearly open model, MiMo-V2.6-Pro, 1480, rank 26, in the snapshot of 8 October 2026.

Scores, artificialanalysis.ai · arena.ai, updated 8 Oct 2026.

Keep an owned server busy: on our assumptions it beats hourly GPU rental above about 37% of paid hours

Renting the same eight GPUs by the hour costs $53.52 an hour, or $39,070 a month around the clock. On our assumptions, owning at $14,412 costs less than that bill only if you would otherwise pay to rent the machine for more than about 37% of the hours. This is an illustrative comparison with hourly GPU rental, where off-site rental is permitted. It does not price private hosted AI, assume equal throughput or compare every operating cost.

GPU rental, lambda.ai/pricing, read on 12 Oct 2026 · Electricity, US Energy Information Administration, eia.gov · the arithmetic is under “For engineers” below.

Start from the data rule, then run a one-week test before you commit

If your rules allow a contract, rent privately. If the records must stay on site, or the work is offline, own. A steady heavy load can also justify a server, and control can tip a close call.

Give a rented flagship model and an open model the same approved tasks for a week. Run the restricted tasks only on site, against a pass mark you set first, on existing or trial hardware. Then put your own volume and data rules into the arithmetic above, and measure speed with your whole team using it before you commit.