vs on-premise AI

On-premise privacy without the procurement.

On-premise AI is the right answer for a 200-lawyer firm with an IT budget.

Muet is the right answer for everyone smaller.

What on-premise means

A self-hosted deployment puts the model on hardware your firm owns or rents, behind your firewall. The firm controls the network, storage, access logs, and upgrade path, and no vendor sees your documents. The privacy posture is arguably the strongest available.

The catch is everything around the model: hardware procurement, network isolation, model deployment, RAG configuration, identity and access management, monitoring, backups, upgrades as better open-source weights ship each quarter, and someone in IT to keep it running.

What on-premise costs to deploy

Published guides for legal-firm on-premise deployments suggest:
  • Hardware. A workstation or small server capable of running a 70B-class model: roughly $5,000–$30,000 depending on whether you buy a Mac Studio cluster or a GPU server.
  • Setup. 8–12 weeks from procurement to production for a mid-size firm: 2–3 weeks for hardware and network, 2–3 weeks for model and RAG, 2–4 weeks of testing, 2 weeks for training and rollout.
  • Staffing. One Linux- and container-fluent administrator can manage the infrastructure for up to 200 users; smaller firms typically don’t have that person on staff.
  • Ongoing cost. Power, cooling, model updates, and security patches, while the open-source state of the art moves every few months and a year-old deployment falls well behind.

What Muet costs to deploy

  • Hardware: the Mac you already own.
  • Setup: the time it takes to drag the .dmg to Applications.
  • Staffing: none.
  • Ongoing cost: $390 per device per year, with automatic updates.
  • Time to first useful output: about ten minutes from download to first answered question.

The privacy comparison

Both keep documents off the public cloud. The difference is shape, not posture.
  • On-premise. Documents live on the firm’s servers, where multiple users can access them. The threat surface is the firm’s network, the IT staff with privileged access, and the audit trail of who looked at what.
  • Muet. Documents live on one user’s Mac, and nobody else has access, including us and the firm’s IT. The threat surface is the laptop, the device policy, and the user.

On-premise suits matter-team review, with twelve associates in the same data room. Muet suits one lawyer drafting a privileged memo on their own machine.

The two often run alongside each other

On-premise AI and Muet handle different shapes of work, and plenty of mid-size and large firms run both:
  • On-premise for matter teams collaborating across thousands of documents in a shared workspace, with the firm’s identity, audit, and review controls wrapped around it.
  • Muet on partner and associate laptops for the work that doesn’t belong in the shared workspace at all: pre-engagement pitches, internal investigations, partner-level HR matters, off-the-record work, personal correspondence with clients.

The firm gets on-premise collaboration and laptop-level privacy, and neither compromises the other.

Where each makes most sense as the only AI tool

  • On-premise alone.Firms processing >2M tokens/day of confidential text, government and defense work with hard air-gap requirements, or regulated facilities where every endpoint is centrally managed. Muet doesn’t try to replicate hardened-network controls.
  • Muet alone. Solos, 2–25 lawyer firms, in-house counsel teams, and any practice without a dedicated IT budget. They get the same privacy posture without the deployment cost.
  • Distributed teams where every lawyer wants the tool on their own machine, including the ones travelling or working from home. One licence per device, with no central deployment.

Try it

Pricing

$390per Mac, per year

A 30-day money-back guarantee and one licence per device, easy to expense. Requires Apple Silicon (M1 or newer) and macOS 15 or newer.