Workload-level cost visibility must help customers act without turning logs onboarding into a configuration project.
ML platform engineers need evidence for retention decisions while the ingestion path must remain low-touch and tenant-safe.
Who you’d be doing this for
“Finance asked what drove last week's jump, and I had to pull people into a guessing session.”
Tiago Gentile · ML Platform Engineer
Runs GPU training workloads on Kubernetes and owns the telemetry budget and incident evidence for the cluster.
What is at stake
Pilot customers cannot connect a managed logs increase to the Kubernetes workload that caused it. You have to weigh useful attribution against a low-touch, tenant-safe telemetry path.
Why it isn’t already fixed
Every obvious fix costs something else. That’s the part you’d have to decide.
- self-service cost insight vs. low-touch onboarding
- workload attribution vs. ingestion reliability
- useful dimensions vs. tenant-safe access
- pilot learning vs. query complexity
Why Crusoe
Managed logs for GPU and Kubernetes workloads must give customers a clear path from high-volume telemetry to an operational decision.
Written with these in mind
Not your kind of problem? 76 more at Crusoe, or browse every organization.
This is the setup. The work is inside.
Running it puts you in the room: the full situation and its constraints, stakeholders who push back in their own words, and the decisions that are yours to make. What you produce becomes a Day One Plan — work you can show someone instead of describing.