OpenCost
OpenCost is an open-source tool for monitoring and managing cloud costs in Kubernetes environments, providing real-time visibility and allocation of costs.
Category | Cost Security & FinOps |
---|---|
Last Commit | 1 year ago |
Last page update | 19 days ago |
Pricing Details | Free and open-source |
Target Audience | DevOps teams, cloud architects, and Kubernetes administrators. |
OpenCost manages opaque and unmanaged cloud costs in Kubernetes environments by providing real-time cost monitoring, allocation, and visibility. This vendor-neutral, open-source project is designed to integrate with Kubernetes clusters, requiring at least Kubernetes 1.21+ and officially supporting up to Kubernetes 1.28.
The technical architecture of OpenCost relies on Prometheus for metric scraping and data storage, enabling granular cost visibility down to individual workloads, namespaces, and specific labels within the Kubernetes cluster. The tool supports various cloud providers, including AWS, Azure, GCP, Oracle Cloud Infrastructure, and on-premises deployments, each with specific configuration requirements. Deployment can be managed through Helm, which simplifies the setup process across different cloud environments.
Key operational considerations include the need for proper configuration to integrate cloud costs and usage reports from providers. The OpenCost API offers extensive capabilities for real-time and historical reporting, allowing queries based on on-demand list pricing and cloud provider cost reports. Parameters such as window
, aggregate
, step
, and resolution
can be specified to tailor the cost data retrieval to specific needs.
Operational limitations include the dependency on Prometheus, which can impact performance if not properly scaled. Additionally, the UI and API performance may degrade with large datasets, particularly when querying extensive time windows or detailed cost breakdowns. However, OpenCost's design ensures that data stored in Prometheus remains intact during upgrades or downgrades, minimizing data loss risks.