Cloud Cost Anomaly Detector with Root Cause Analysis for Startup Engineering Teams
Infrabase scans for security gaps, costs, and policy violations in cloud accounts. But the most acute pain for startups is unexpected cloud cost spikes, a developer leaves a GPU instance running, a misconfigured auto-scaler provisions 50 nodes, or a data pipeline reprocesses 3 months of data. The missing tool is a cost anomaly detector that catches spikes within hours (not at month-end) and traces them to the specific resource and commit that caused them.
Problem Statement
Startup engineering teams discover cloud cost spikes at the end of the month when the AWS bill arrives. A developer's test deployment that should have cost $5 ran for 2 weeks at $3,000. A misconfigured auto-scaler added $8,000 in compute overnight. AWS Cost Explorer data is 24-48 hours delayed and doesn't trace costs to specific git commits or deployments.
The Idea
A real-time cloud cost anomaly detector for startup engineering teams that catches spending spikes within 2 hours and traces each anomaly to the specific resource, team, and deployment that caused it.
Why Now
Average startup cloud spend grew to $15K-50K/month, but 35% of that is waste. AWS Cost Explorer shows data 24-48 hours late. By the time a $2,000 GPU instance is discovered, it's been running for a week. FinOps tools (Kubecost, CloudHealth) are designed for enterprises, not startups.
Target User
Engineering managers and DevOps engineers at startups spending $5K-100K/month on cloud
Target Market
VC-backed startups on AWS, GCP, or Azure with 5-50 person engineering teams
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