Cud Advisorv1.0.0

A startup running production GKE clusters for its SaaS platform with consistent compute usage over 6+ months. This scenario benefits from analyzing steady-state workloads to recommend resource-based CUDs for cost savings, while ensuring SUDs are accounted for to avoid over-commitment.

An e-commerce business using Compute Engine and Cloud Run, with baseline steady-state usage and variable peaks during holiday seasons. The analysis focuses on separating stable workloads for CUDs from variable ones for SUDs, recommending spend-based CUDs for flexibility.

A large enterprise moving legacy on-premises applications to GCP Compute Engine, with predictable, long-running workloads. This scenario involves analyzing usage history to recommend resource-based CUDs for high discounts, calculating break-even timelines for commitment investments.

A media company leveraging Cloud Run for scalable content delivery with consistent baseline usage. The analysis assesses CUD coverage for Cloud Run workloads post-2025 updates, recommending spend-based CUDs to balance savings and flexibility against SUD interactions.

Businesses with recurring revenue from software subscriptions, often running stable GKE or Compute Engine workloads. This model benefits from CUDs by reducing infrastructure costs to improve profit margins, with analysis focusing on long-term commitment strategies.

Consulting firms using GCP for client projects with variable compute needs across different engagements. This model uses CUD analysis to optimize costs for steady-state project workloads, recommending spend-based CUDs to adapt to changing project demands.

Companies processing large datasets with consistent Compute Engine usage for analytics pipelines. This model leverages CUDs to secure discounts on predictable compute resources, with analysis emphasizing coverage gaps and risk scenarios for cost-effective scaling.