Automated AWS cost-waste detection + fix agent (no manual ops)

Executive Summary

Many companies — from small engineering teams to large enterprises — struggle with unpredictable AWS bills and persistent waste; industry estimates commonly put avoidable cloud spend at 10–30% and remediation often requires specialist time that teams don’t have. This is a broad problem across roughly 2,000,000 AWS customers where FinOps teams or engineers are asked to triage alerts, validate recommendations, and then perform risky manual fixes that slow velocity and create change management overhead. You could build an autonomous agent that continuously analyzes 6–12 months of telemetry, detects common waste patterns (idle EC2/RDS, orphaned EBS volumes, oversized instances, inefficient S3 class usage, unmanaged snapshots, suboptimal Savings Plans) and executes low-risk remediations automatically while escalating higher-risk changes for opt-in approval. Combine least-privilege automation, simulation/dry-run and explainable change logs with a recommendations engine that can convert Savings Plan/RI suggestions into actionable purchases; a reasonable target outcome is 10–25% reduction in waste for many customers within the first 90 days. The timing is favorable: cloud spend is rising, FinOps adoption is creating repeatable procurement channels, and agentive automation (APIs + LLMs) makes safe end-to-end remediation technically feasible — together supporting a roughly $12.0B addressable market at an assumed $6,000 ACV. Customers are moving beyond dashboards toward outcomes, which enables pricing models tied to measured savings. To stand out you must prioritize trust and safety: least-privilege execution, reversible changes, comprehensive audit trails, conservative defaults, and strong explainability, targeting mid-market accounts with $100k–$5M annual AWS spend where operations are thin; the core challenges will be establishing vendor trust, minimizing false positives, and achieving deep, low-friction integrations across diverse customer environments.