On-demand Webinar

Optimize your cloud costs effectively with Harness solutions | Harness Resource

Cloud waste is one of the biggest pain points for technical teams tasked with delivering value at speed. In this session, we’ll dive into how Harness Cloud Cost Management helps you go beyond basic visibility to actually take action on cost savings—automatically. We’ll cover practical optimization features like using Cloud AutoStopping to automatically start/stop idle resources and Kubernetes cost efficiency with Cluster Orchestrator. Learn how to bake cost optimization directly into your workflow and reduce waste without slowing down innovation.

Key Takeaways:

Automating idle resource management with AutoStopping
Cost allocation and optimization in Kubernetes environments
Integrating cost awareness into engineering workflows

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Published

January 1, 2024

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What you'll learn

Key Takeaways

Automate Resource Shutdowns with Cloud AutoStopping

Automatically shut down idle cloud resources and restart them upon detecting activity. This eliminates manual intervention and can yield up to 72 percent savings on workloads.

Optimize Long-Term Spend with Commitment Orchestrator

Machine learning algorithms predict compute usage trends to automate long-term cloud commitments. This ensures optimal use of reserved instances and savings plans without manual tracking errors.

Map Kubernetes Dependencies Using Smart Import

An eBPF agent creates traffic maps to easily import complex Kubernetes resources and dependencies. This allows users to generate configuration rules with a single click.

Track Multi-Cloud Costs and Detect Anomalies

Centralized dashboards visualize cloud costs across multiple providers using Cost and Usage Reports. Additionally, machine learning algorithms highlight anomalous spending and improve based on user feedback.

Maximize Efficiency with Cluster Orchestrator

Manage Kubernetes scaling, spot instance interruptions, and optimal bin packing automatically. Users can also set custom distribution rules to split workloads between spot and on-demand instances.