The infrastructure decision that helped bring Stable to market faster

Stable was built to help AWS teams better understand, manage, and reduce cloud spend at the resource level. But before the platform could deliver recommendations, alerts, and actionable cost visibility, Unicorne needed a reliable way to collect and structure cloud data across customer environments.

That is where CloudQuery came in.

Rather than building a custom data collection layer from scratch, Unicorne chose to build on a proven foundation. This allowed the team to skip months of engineering work and focus directly on the parts of Stable that matter most to users: cost intelligence, better visibility, and faster action.

Today, CloudQuery supports Stable’s nightly sync across connected AWS accounts, feeding data into Amazon S3 for historical analysis and Aurora PostgreSQL for low-latency application queries. That architecture gives Stable the backbone it needs to power its recommendation engine, alerts, and resource explorer experience.

For teams trying to get control of AWS spend, that speed matters. Stable exists to turn scattered cloud data into concrete decisions. Getting the underlying data layer right was a big part of making that possible.

Read the case study to learn how the right data foundation helped accelerate Stable’s launch.