Reducing Kubernetes Cost: Kubecost vs Cast.ai

  • 68% have seen increases in Kubernetes costs
  • 44% rely on monthly estimates to determine total expenses
  • 24% do nothing to curb or control their costs
  • Just 38% can predict their bills within 10% accuracy

Tackling Cost in Two Ways

  • monitoring
  • optimization

Comparison Criteria

  • Performance: Are there bottlenecks within your ecosystem, or other resource-allocation issues impacting how efficiently your applications run? How a tool measures performance-without incurring too much overhead while running as a partner application-influences its ability to uncover unused resources or similar problems (for example).
  • Maintainability: How easy does the tool make it to maintain continual oversight over your costs? Does the tool itself require any housekeeping to help it run effectively over extended periods of time?
  • Setup Complexity: Is the application easy to install, configure, and get started with?
  • Ease of use: Is the application user-friendly and approachable enough to harness on a daily basis? Are its core features usable without requiring too much of a learning curve?
  • Savings: How much money by dollar amount or percentage, on average, can the tool save you within a certain timeframe?
  • Price: How much does the tool itself cost, and what’s the cost structure?

Introducing Kubecost

  • Setting up metric storage
  • Setting limits and requests
  • Making cloud integrations
  • Using Prometheus, Grafana, or existing node exporters
  • Exposing Kubecost’s APIs and functionality with Ingress
  • Reflecting reserved or committed pricing
  • Deploying without consistent volumes

Introducing Cast.ai

Conclusion

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>> www.loft.sh << Build Your Internal Kubernetes Platform With Virtual Clusters, Namespace Self-Service & Secure Multi-Tenancy

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>> www.loft.sh << Build Your Internal Kubernetes Platform With Virtual Clusters, Namespace Self-Service & Secure Multi-Tenancy

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