Kubernetes spend, traced to the team that owns it.
Kubernetes bills roll up by cluster, not by team or workload. Without attribution, idle spend hides in plain sight and finance gets a number with no story behind it.
A single line item for a shared cluster tells you what you spent but nothing about which team or service spent it.
Spend is attributed to the workload and the team that owns it, across both Kubernetes and GenAI usage, so every dollar has an owner.
Requests set generously months ago keep reserving capacity nobody uses, and nothing surfaces it.
Randoli flags idle workloads automatically and recommends rightsized requests based on what each workload actually consumed.
Token and inference costs compound fast, and a small increase in usage becomes a runaway bill before anyone spots it in the general cloud spend.
Token and inference cost is tracked as its own signal and attributed to the workload and team behind it, rather than buried in a general cloud bill.
Reviewing the monthly invoice means learning about a spike weeks after the spending happened.
Alerts on both Kubernetes and token spend route through the same alerting integrations your team already uses for everything else.
Someone exports usage data, maps it to teams in a spreadsheet, and rebuilds the same report every billing cycle.
Chargeback and allocation data is available through the same API as the rest of your Randoli data, so existing pipelines into finance tooling keep working.
A Canadian public sector institution cut its Kubernetes spend by 45% with Randoli. The reduction didn't come from one change. It came from months of evidence-gathering: cost reports broken down by workload and team, rightsizing recommendations, and historical trends showing how consumption actually moved over time.
What their platform team valued most wasn't the recommendations themselves. It was being able to validate each cost decision against production telemetry first, answering "will this save money without hurting reliability" with data instead of a guess.
That turned the work from simple cost cutting into engineering-led optimization. Every change was justified on both spend and reliability, which is why the savings held instead of quietly reversing the next time a team hit a performance problem.
See the full detail on Cost Management for Kubernetes, or Randoli on OpenCost Enterprise.