unclouded.ai
Deployment

Your AI does not have to live in one place

Different data deserves different treatment. We help organizations build AI environments where processing location is a deliberate architectural decision rather than a default nobody ever chose.

On-premises AI

AI processing runs on systems inside your organization. Nothing crosses the boundary.

Best suited for:

  • Sensitive information
  • Internal-only data
  • Strict organizational policies
  • Intellectual property
  • Maximum infrastructure control

We design, configure, integrate, update and manage the software stack.

Managed private processing

Your workloads run on dedicated or controlled infrastructure we operate, with data retention, processing, security and access requirements defined as part of the engagement.

This gives many of the advantages of private AI without requiring your staff to run specialized AI systems — usually the difference between a project that happens and one that stays on a list.

Cloud AI

Sometimes the cloud is the right answer. It can provide:

  • Temporary compute capacity
  • Access to particularly capable models
  • Faster processing
  • Less infrastructure to manage
  • Better economics for occasional work

When it fits, we design and manage those integrations as part of the whole system.

Hybrid AI

Some workloads stay local. Others do not need to.

A hybrid architecture lets each category of information follow the policy that actually applies to it, instead of forcing one rule across everything. In practice that is what most organizations need, because most organizations hold several kinds of data at once.

One organization can use every one of these approaches at the same time.

The architecture is what enforces the difference. Once "this collection is local, that one is not" is written into the system rather than into a policy document, it stops depending on everyone remembering.

a worked hybrid example

Local only

  • Sensitive personnel documents
  • The internal search database

policy boundary

Managed private processing

  • Public meeting recordings
  • A large one-time indexing job

policy boundary

Commercial AI permitted

  • Public website content
  • Rented GPU capacity for a burst

Five workloads, three placements, one organization. This is the normal case, not the complicated one.

Choosing

Which one fits which workload

A rough guide, not a rule. The real answer comes from the data, the turnaround time and what your policies already say.

Deployment fit by workload characteristic. Where two placements both work, the cheaper one usually wins.
If the workload… Usually belongs Because
Touches personnel, legal or medical records On-premises The policy question is settled before the technical one is asked.
Involves proprietary source code On-premises The code is the asset. Sending it out is the thing being avoided.
Is a one-time backlog of public records Managed private Heavy for a few weeks, then never again. Buying hardware for it is waste.
Runs continuously but lightly On-premises Existing hardware absorbs it, and recurring cloud cost never stops.
Needs a model too large to host Cloud, if permitted Some capability genuinely is not available on hardware you can own.
Processes already-published material Whatever is cheapest There is nothing to protect. Optimise for cost and speed instead.

You do not need an AI strategy. You need a problem worth solving.

Show us the repetitive work, the archive nobody can search, the recordings piling up, or the process that looks automatable. We will work backward from there.

Start a Conversation
Hardware

Hardware without the hardware sales pitch

If dedicated hardware is required, we help determine what the workload needs. You purchase it directly. We do not need to sell you a larger GPU to improve our margin, and that is the only reason a sizing recommendation is worth anything.

Recommendations are based on:

  • Required throughput
  • Model requirements
  • Memory requirements
  • Concurrent workloads
  • Power and cooling
  • Reliability expectations
  • Existing infrastructure
  • Expected growth
  • Budget

Sometimes the right answer is a substantial GPU server. Sometimes it is a workstation. Sometimes it is hardware you already own and are not using between six in the evening and eight in the morning.

Design the environment before buying anything

Sometimes the right answer is a GPU server. Sometimes it is a workstation. Sometimes it is hardware you already own. We will tell you which.

Plan a workload Design Your AI Environment