Kubernetes infrastructure optimization

The problem

Cluster cost grows with service count-requests and limits are often set generously while real usage is rarely revisited.

FinOps and engineering speak past each other without shared workload and node facts.

Why “just lower limits” fails

Aggressive cuts without utilization data raise the risk of throttling and OOMs during peaks.

Raw metrics without mapping to services and owners do not tell you whom to optimize first.

How Opsy supports decisions

Surfaces node and workload context so requests and limits discussions rely on evidence, not gut feel.

Makes conversations between service owners and platform clearer-where waste lives and where bottlenecks are.

Product topic

Methodology and UI depth are on the CPU / RAM optimization topic page.

Why brute‑forcing CPU/RAM cuts fails

Requests and limits affect scheduling, autoscaling, and node cost. Random cuts without utilization context create latency pain or evictions.

FinOps wants cost numbers while engineers care about QoS-without shared facts, talks stall.

Tuning one service without neighbor context on the node can fake progress while the node stays hot.

Who needs this scenario

• Companies with meaningful cloud bills or fixed cluster budgets.
• Products with sharp load peaks-seasonality, campaigns.
• Teams after rapid growth that never revisited “just in case” requests.
• Orgs adopting FinOps and tying metrics to money.

Screenshots below come from the related Opsy product topic. Click an image to open it full screen.

Product topic →   All scenarios   Open demo