Most teams see one line item: "EKS — $82,400/mo." Atmosly breaks spend down to namespace, service, and team, finds the idle and oversized workloads, and turns real p95 usage into right-sizing the dev team can apply themselves. Across AWS, GCP, and Azure — not a spreadsheet.
Cost Intelligence runs continuously on every connected cluster, powered by in-cluster cost telemetry. No tagging project, no quarterly spreadsheet — just signals your engineers can act on.
One bill becomes a map. See cost by account, by cluster, by namespace, and by service — instance type, load balancer, NAT gateway, data transfer, and storage, each on its own line. Then hand each team its own showback view.
Atmosly reads each workload's p95 over time and proposes a concrete new request/limit, with the dollar impact attached. Reserved-instance and savings-plan opportunities surface the same way — ranked by monthly savings.
Set budgets and get notified the moment daily spend crosses the line. A monthly forecast tells you where you'll land. Idle workloads and orphaned volumes get flagged before they quietly bleed another month of spend.
The numbers engineers trust because they come from the cluster — and finance trusts because they reconcile to the cloud bill.
AWS, GCP, and Azure spend normalized into a single dashboard, with per-cloud and per-account totals reconciled to the bill.
Powered by in-cluster cost telemetry, so spend maps to the actual workloads — not just the cloud account.
Concrete request/limit changes from observed usage, each with its dollar impact, applied as a reviewable PR.
Reserved-instance and savings-plan recommendations sized to your steady-state, ranked by monthly savings.
Give every team its own cost view so spend has an owner — without standing up a FinOps function.
Daily-budget and monthly-estimate alerts, plus a month-end forecast from your current run-rate.
*Representative of customer-reported outcomes. Your results depend on workload mix and current utilization.
A deploy, an incident, a security finding, and a cost change are the same story — surfaced in one UI, with one audit trail.
Platform EngineeringA deep technical guide to how Atmosly built a CI/CD engine that processes hundreds of pipeline executions daily across customer environments on EKS, GKE, and AKS. Covers our four-layer architecture, three-layer caching engine, canary/blue-green/rolling deployments, DevSecOps pipeline, approval system, and pipeline observability at Kubernetes scale.
KubernetesLens, K9s, and Octant for Kubernetes management. We analyze performance, features, and how AI tools like Atmosly are changing the game.
KubernetesThe definitive guide for startups adopting Kubernetes. Learn why to start early, how to build a lean stack, and how to avoid the 'PaaS Trap' while maintaining developer velocity.
Connect one cluster, read-only. The cost breakdown, idle workloads, and right-sizing recommendations show up on your dashboard in about five minutes. Free, no sales call.