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Qatalystic
Applied AI

Inference placement & cost optimisation

Deciding per workload whether it belongs on owned hardware, on cloud accelerators or on a serverless provider — and making the switch a configuration change.

3–6 weeksTypical duration

The problem this solves

Your inference costs are growing and every workload runs wherever it was first deployed.

What you receive

Artefacts you can hold, and that you can accept or refuse — never a list of activities.

  • A per-workload profile covering latency requirement, privacy constraint and load shape
  • The placement recommendation per workload, with the cost and latency of each option calculated
  • A routing layer that makes placement a configuration change rather than a migration
  • The measured result of the first three moves

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