When evaluating Harness Cloud Cost Management against CAST AI, it’s clear that both platforms are designed to optimize cloud costs, but their focus and feature sets differ significantly.

Harness Cloud Cost Management is built for a broader cloud ecosystem, offering in-depth cost management across multi-cloud environments (AWS, Azure, GCP), with full Kubernetes support. Harness excels in automation, providing tools like Cloud AutoStopping, Spot Instance Orchestration, and Commitment Orchestration. These capabilities enable teams to save on non-production workloads, automate the orchestration of cost-saving resources, and purchase and manage Reserved Instances and Savings Plans effectively. Harness also stands out with its robust governance and budgetary controls, ensuring that teams have the visibility and control they need to manage cloud spend across all environments.

CAST AI, on the other hand, is heavily focused on Kubernetes cost optimization. While it offers solid AI-driven recommendations for K8s workloads, its multi-cloud support is limited primarily to Kubernetes environments. CAST AI does provide features like Spot Instance Orchestration, but it lacks more comprehensive cloud cost management features that extend beyond Kubernetes. It also lacks support for important governance tools, such as budgets, multi-cloud chargebacks, or detailed reporting for non-Kubernetes environments.

Key Feature Comparisons:

• Multi-Cloud Support:
  Harness supports AWS, Azure, GCP, and Kubernetes, offering comprehensive cost optimization and visibility across these platforms. In contrast, CAST AI is mainly focused on Kubernetes clusters and does not fully support non-K8s workloads in multi-cloud environments.
• Governance & Budgeting:
  Harness offers robust governance features with out-of-the-box policy rules, automated rule enforcement, and AI-assisted governance rule creation. CAST AI lacks some of these governance capabilities, particularly in areas such as budgets and multi-cloud cost controls.
• Cost Allocation & Chargebacks:
  Both platforms offer Kubernetes cost allocation by node, cluster, and workload. However, Harness extends this functionality beyond Kubernetes with multi-cloud cost perspectives and chargebacks, while CAST AI’s allocation features are limited to Kubernetes-only environments.
• Automated Cost Management:
  Harness leads with full-featured automation, including auto-stopping idle resources across clouds, Spot Instance Orchestration, and Commitment Orchestration. CAST AI focuses on Kubernetes automation but offers less comprehensive cloud-wide automation beyond K8s.

In summary, Harness offers a more comprehensive solution for cloud cost management, with features that extend across multi-cloud environments and on-prem resources, coupled with robust governance and automation. CAST AI, while strong in Kubernetes cost optimization, falls short in providing the breadth of capabilities needed for full multi-cloud governance and automation, making it less suitable for businesses looking to manage cloud costs holistically.