Design ML Cost Optimization

ML Cost Optimization as a FinOps Control Plane

Treat ML spend like a distributed system with feedback loops: observe GPU-seconds and token burn, attribute to teams, recommend rightsizing, and apply guarded actuators.

AWS, Google, and Azure ML estates routinely spend millions per month on GPUs without line-of-business attribution. This section (1/30) frames ml cost optimization as a finops control plane for interview depth.

Mechanism A

Unlabeled Kubernetes namespaces hide 70% of GPU waste—idle replicas, oversized inference pools, and training jobs that never checkpoint off spot.

Mechanism B

A cost control plane must separate observation (immutable ledger) from action (autoscale, spot shift, model tiering) so FinOps can audit every dollar moved.

Mechanism C

Executives want unit economics: cost per 1M inference requests and cost per training epoch, not aggregate EC2 bills.

Interview sound bite

"When ml cost optimization as a finops control plane misbehaves, I trace partial attribution before touching model accuracy."

Failure modes to volunteer

Shadow clusters without chargeback labels, inference autoscaler fighting HPA on CPU-only metrics, and spot training without checkpoint RPO.

Why interviewers care

ML Cost Optimization interviews reward crisp scope, explicit trade-offs, and failure stories—not generic microservice diagrams.

Interview checkpoint

Name one failure story for ML Cost Optimization as a FinOps Control Plane that proves you understand real outages, not happy-path diagrams.