MACYOU: AI DEPLOYMENT
ON APPLE SILICON
MacYou is a self-service platform we built and run: you pick a chip, memory and an AI stack, and about five minutes later a private inference machine is live — reachable through an OpenAI-compatible API, on dedicated Apple Silicon that nobody else shares. It is live at macyou.co.
THE PROBLEM IT SOLVES
Teams that want private AI face a bad menu: rent shared GPU cloud where price scales with every token, wait for enterprise procurement, or self-host — which means buying hardware, tuning an inference stack, and owning the ops. Meanwhile the data-control question ("where do our prompts actually go?") kills half the projects before they start.
MacYou's answer: dedicated Apple-Silicon machines as the inference substrate. Modern Apple chips run open models efficiently on unified memory, draw little power, and — critically — are yours for the rental period: dedicated hardware, not a shared queue. Your data does not leave your machine.
WHAT IT DOES
- Pick the machine — chip and memory sized to the model you want to run.
- Pick the AI stack — a pre-configured inference setup instead of a bare OS.
- Deploy in about five minutes — from click to a live endpoint.
- OpenAI-API drop-in — existing code keeps working; change the base URL, keep the SDK.
HOW IT'S BUILT
| Layer | Choice | Why |
|---|---|---|
| Frontend / app | Next.js 16 on Vercel | One codebase for marketing, dashboard and API routes; deploy previews for every change. |
| Data | Prisma + Postgres | Typed schema shared across the app; boring and reliable where billing state lives. |
| Payments | Stripe | Subscription lifecycle, proration and invoicing without building a billing engine. |
| Auth | Auth.js with 2FA | People rent whole machines — account takeover has to be expensive. |
| Inference fleet | Dedicated Apple-Silicon hardware | Unified memory runs large open models; one tenant per machine. |
DECISIONS THAT MATTERED
- OpenAI compatibility over a custom API. The switching cost for users is one environment variable, not a rewrite. Adoption lives or dies on this.
- Dedicated over shared. Multi-tenant GPU slicing is cheaper to operate but reintroduces the noisy-neighbour and data-isolation questions the product exists to remove.
- Pre-configured stacks over raw machines. The five-minute promise only holds if the platform owns the inference setup end to end.
- Boring web stack. The novelty budget went to the hardware layer; the web layer is deliberately conventional so it never becomes the bottleneck.
WHERE THIS SHOWS UP IN OUR OTHER WORK
Running MacYou is why our AI-agent work can offer fully local inference as a real deployment target: when a client's data cannot leave their infrastructure, we already operate the hardware platform that makes on-premise practical — it is not a slide in a deck.
Want a private AI deployment, or an agent built on top of one? Tell us the constraint.
VISIT MACYOU → START A PROJECT →