Atmosly Kubernetes Cost Optimization vs Klustercost

Atmosly Kubernetes Cost Optimization helps teams allocate and manage cloud spending across Amazon EKS, Google GKE and Azure AKS. Its normalized dashboard breaks costs down by account, cluster, namespace and service, with showback views for teams. The product detects idle workloads and orphaned volumes, provides daily budget alerts and monthly forecasts, and ranks reserved-instance and savings-plan opportunities by monthly savings. Right-sizing suggestions use workload p95 usage over time, estimate dollar impact and can be submitted as reviewable pull requests. A connected cluster can be assessed with read-only access; changes require explicit approval. Cost telemetry depends on an existing in-cluster Prometheus instance, and the page says kube-prometheus-stack works out of the box. The Personal plan is free forever for one engineer and one cluster, with cost recommendations in view-only mode and seven-day history. Team signups receive 30 days of Growth features without a credit card for self-serve signup. Paid plans start at $399/mo. The product supports Linux, web and self-hosted use; the self-hosted control plane is available on the Platform tier.

Klustercost gives Kubernetes users cost visibility by workload, monitoring pods and assigning prices to nodes. Its Go-based observers use Kubernetes informers and Prometheus to track pods, nodes and namespaces in real time. Pod and node data is stored in PostgreSQL every 10 minutes, and nodes are enriched with hourly virtual-machine costs from Azure retail pricing APIs.

Users can ask questions in plain English and receive natural-language answers with structured JSON; the site describes these AI queries as read-only. Access options include WhatsApp, Teams, Slack, Power BI, an HTTP API and MCP protocol. A Helm chart deploys the monitor, pricing service, database and optional AI layer. The project is open source under GPL-3.0 and its plan is free. Setup requires a running Kubernetes cluster, Helm 3.x, reachable Prometheus, Metrics Server and a StorageClass. Azure cost enrichment is supported; AWS and GCP support are planned. Natural-language queries through the MCP assistant require an OpenAI API key.