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OpenCost — your favorite open source cost monitoring tool for Kubernetes and cloud spend

OpenCost give teams visibility into current and historical Kubernetes and cloud spend and resource allocation. These models provide cost transparency in Kubernetes environments that support multiple applications, teams, departments, etc. It also provides visibility into the cloud costs across multiple providers.

OpenCost was originally developed and open sourced by Kubecost. This project combines a specification as well as a Golang implementation of these detailed requirements.

OpenCost UI Walkthrough OpenCost UI Walkthrough

To see the full functionality of OpenCost you can view OpenCost features. Here is a summary of features enabled:

  • Real-time cost allocation by Kubernetes cluster, node, namespace, controller kind, controller, service, or pod
  • Multi-cloud cost monitoring for all cloud services on AWS, Azure, GCP
  • Dynamic on-demand k8s asset pricing enabled by integrations with AWS, Azure, and GCP billing APIs
  • Supports on-prem k8s clusters with custom CSV pricing
  • Allocation for in-cluster K8s resources like CPU, GPU, memory, and persistent volumes
  • Easily export pricing data to Prometheus with /metrics endpoint (learn more)
  • Free and open source distribution (Apache2 license)

Getting Started

You can deploy OpenCost on any Kubernetes 1.20+ cluster in a matter of minutes, if not seconds!

Visit the full documentation for recommended installation options.

Usage

Contributing

We ❤️ pull requests! See CONTRIBUTING.md for information on building the project from source and contributing changes.

Community

If you need any support or have any questions on contributing to the project, you can reach us on CNCF Slack in the #opencost channel or attend the biweekly OpenCost Working Group community meeting from the Community Calendar to discuss OpenCost development.

FAQ

You can view OpenCost documentation for a list of commonly asked questions.

OpenCost's Projects

opencost icon opencost

Cost monitoring for Kubernetes workloads and cloud costs

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