Automate and optimize data center workload placement
Every data center has an orchestrator that runs workloads and a data center infrastructure management (DCIM) system that knows the power and cooling budget. Most still place workloads one at a time, without weighing that budget across the whole fleet. Let's decide where every workload runs so the fleet uses it well.
One solve places every workload across nodes, racks, and facilities
Timefold solves node assignment, GPU slicing, queued work admission, and preemption as one optimization problem. CPU, memory, storage, GPU type, rack and facility power, cooling, residency, tenant isolation, spread, and drain boundaries are enforced as hard constraints, so every plan can actually run. Within those rules, the model uses stranded power and idle compute before it reaches for new capacity, and only moves running workloads when the gain is worth it.
Where Timefold goes beyond default schedulers
Four places where default schedulers fall short. Timefold bakes each one into the model.
Power is a first-class constraint
Rack power, facility power, and cooling are hard limits, and the model fills the headroom inside them instead of stranding it.
GPUs packed without fragmentation
Workloads land on the right GPU generation, in supported slices, with as little unusable accelerator capacity left behind as possible.
Residency is a rule, not a review
Jurisdiction, EU General Data Protection Regulation (GDPR) transfer, EU AI Act, and sovereignty rules decide which facilities a workload may use before the plan is made.
Moves only when they pay off
Running workloads only move when the gain is worth it, and immovable ones never move.
Power, not compute, is now the binding constraint
New grid capacity takes years, while GPUs and requests arrive every week. The fastest capacity a data center can add is the capacity it already has but cannot use, because placement ignores measured usage, power, and cooling.
Data center electricity demand by 2030
Global consumption is expected to more than double, driven by AI.
To connect new capacity to the grid in many markets
While GPUs and new workloads arrive every week.
Of leased power capacity often goes unused
Paid for and provisioned, but never drawn.
Utilization on most enterprise-owned GPUs
Some of the most expensive hardware in the building, often half idle.
Constraints the model expresses today
When scheduling works, everything works.
Less waste. More control. Teams that trust the plan.