Surface EBS disk IO burst-credit balance in DB observability reports

Many production databases on AWS suffer unexpected IO throttling when attached EBS volumes exhaust their burst-credit pools; the symptom surfaces as noisy DB alerts, slow queries, and recurring incidents that SREs, DBAs and platform teams at large enterprises struggle to diagnose. This is especially acute where teams operate at scale across hundreds of volumes and migrations from legacy volume types to provisioned IOPS are partial or ongoing. You could build a DB-observability feature that surfaces EBS disk IO burst-credit balance as a first-class metric—ingesting CloudWatch/EBS telemetry, correlating credit balance with DB latency, query hotspots, traces and tenants, and producing prioritized, explainable alerts plus automated runbooks (e.g., resize or migrate volumes, provision IOPS, or throttle writes). Adding predictive forecasts of credit depletion and a cost/reliability impact estimator would let teams choose the most effective remediation rather than chasing symptoms. The timing is favorable: enterprises spend roughly $100K/year on observability (a $25.0B addressable market across about 250,000 enterprises), observability consolidation favors single-pane solutions that tie alerts to root metrics, and cloud-native adoption increases exposure to managed block-storage behaviors that cause these incidents; the idea scores 78/100 on market fit with revenue potential 82/100. To stand out you must deeply integrate DB and EBS telemetry so alerts point to a depleted burst bucket and the exact SQLs or tenants affected, rather than simply surfacing raw CloudWatch graphs; that integration and explainability are the product’s primary strengths. Honest challenges include medium competition from established observability vendors, the need for cross-account CloudWatch access and precise credit accounting across volume families, and the engineering effort required to generalize beyond AWS.