Cloud Waste Scanner

Cloud Waste Scanner is an engineering-first cloud cost optimization platform built for teams that need action, not just dashboards. It helps DevOps, Platform, and FinOps teams find cloud waste quickly and turn findings into practical remediation workflows. The platform highlights common high-impact waste patterns such as idle compute, oversized instances, orphaned storage, and other low-value cloud spend. Instead of showing raw data only, Cloud Waste Scanner prioritizes opportunities by impact and helps teams execute fixes in a repeatable weekly operating rhythm.

★★★★★

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• Local-first credential security. Cloud credentials are stored exclusively on the user's device. Provider API calls are made directly from the local environment, eliminating the need to upload cloud keys to external servers and ensuring sensitive access data remains under the user's control.
• Idle compute resource detection. The platform scans for compute instances that are powered on but show minimal or no workload activity, enabling teams to stop or downsize these resources and immediately halt unnecessary spending.
• Oversized instance analysis. Historical performance data is analyzed against current instance types to identify resources consistently underutilized. Recommendations are generated to resize to smaller, cost-appropriate instances without sacrificing performance.
• Orphaned storage identification. Detects storage volumes, unattached disks, and obsolete snapshots that are no longer connected to active resources but continue to incur charges, facilitating safe and straightforward cleanup.
• Impact-based opportunity prioritization. Savings opportunities are automatically ranked by their potential financial impact. This allows engineering and finance teams to focus remediation efforts on the changes that will deliver the highest cost reduction first.
• PDF and CSV report generation. Creates structured PDF reports suitable for management reviews and stakeholder presentations, alongside CSV exports for detailed data manipulation in spreadsheets or external analysis tools.
• Multi-provider ecosystem support. Compatible with a wide range of cloud infrastructure providers including AWS, Microsoft Azure, Google Cloud, Oracle Cloud, Alibaba Cloud, IBM Cloud, DigitalOcean, and dozens more, centralizing waste analysis across disparate platforms.
• Unused network resource discovery. Identifies unassociated public IP addresses, idle load balancers, and other network components that are billing without handling traffic, allowing for their prompt release and cost elimination.
• Cross-team review workflows. A single scan result can be accessed by engineering for technical implementation, by finance for cost verification, and by management for approval, using consistent dashboards and exports without rebuilding data in slide decks.
• Scan definition as fresh execution. A scan is strictly defined as a new data collection run across connected accounts. Opening, filtering, or exporting previously generated results does not consume additional scan quota, providing predictable usage.
• Governance dashboard views. Provides persistent dashboard views displaying waste metrics and compliance trends over time, enabling teams to track the effectiveness of optimization efforts and enforce internal policies.
• Data-backed review evidence. Every cost-saving recommendation includes supporting utilization metrics and performance data sourced directly from cloud provider APIs, creating a verifiable basis for operational changes and financial approvals.

Development of Cloud Waste Scanner began with the specific goal of building an optimization tool that prioritized actionable outcomes over passive data visualization. The engineering team focused on creating a secure, privacy-preserving architecture from the outset, adopting a local-first model where all sensitive operations occur within the user's own infrastructure. The program is primarily written in the Rust programming language, selected for its performance characteristics, memory safety guarantees, and ability to produce native executables without a heavy runtime dependency.

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