AI Infrastructure Control Plane

Economic and operational control for AI infrastructure.

InferenceKey is the layer that governs how your AI runs and how much it costs.

Teams with inference workloads have identified between 20% and 60% inefficiency in misallocated GPU capacity.
Not a model.Not a dashboard.It's control infrastructure.
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The problem doesn't start when capacity is lacking.
It starts when capacity is misallocated.

One team requests 2 GPUs.

Another team requests 2 more.

They get assigned.

Weeks later:

— They're underutilised.

— Or simply idle.

— But the cost is still there.

Each team reserves out of fear.

Nobody wants to run out of capacity.

The result.

Fragmented GPUs.
Idle capacity.
Spend that grows without usage to back it.

In real environments, this pattern often hides between 20% and 60% underutilised capacity.

When AI goes to production, the mess becomes lost budget.

From static allocation to shared capacity.

GPUs shouldn't belong to teams.
They should be available to whoever needs them.

No more fear-based reservations.
No more idle resources.

The key isn't buying more.
It's sharing better.

With InferenceKey:

  • You visualise workloads in real time.
  • You detect saturation and idleness.
  • You define concurrency rules.
  • You assign capacity based on real demand.

GPUs are no longer "assigned".
They become governed.

Every job has context.
Every GPU has a purpose.

Teams that operate this way typically recover between 20% and 60% efficiency, depending on their initial level of fragmentation.

You don't run inference.
You share it with rules.

Real-time visibility of your AI infrastructure.

See how workloads move.
Where capacity is consumed.
Which resources are actually active.

No assumptions.
Just clear data.

Every moving dot is compute in use.

Visibility is worthless if it doesn't reduce spend.

With InferenceKey you can:

  • See cost per workload.
  • Segment spend by team.
  • Compare real usage vs allocated capacity.
  • Identify which projects are draining budget.
  • Simulate scenarios before scaling.

You stop approving GPUs "just in case".
You approve only what makes sense.

Cost stops being a surprise.
It becomes a decision.

20–60%
Potential savings when reallocating capacity correctly

*Teams with fragmented infrastructure

Understand exactly where the money goes.

From global view to scenario comparison, InferenceKey turns AI usage into an informed economic decision.

Improvement percentages depend on usage patterns and initial GPU fragmentation level.

If your AI infrastructure is growing, there's efficiency you can recover.

We'll analyse it with you.

20 – 60 % underutilised capacity in fragmented environments
No disruptive changes No upfront commitment

Just a clear assessment of what you already have.

Request technical assessment