We built an AI cloud engineer that prevents and fixes cloud waste autonomously.
The problem we kept seeing: FinOps tools generate hundreds of "optimization recommendations" that sit in dashboards forever. Industry implementation rate is ~5%. The tools work fine—humans just can't keep up.
So we asked: what if the tool actually fixed things?
Yasu connects to your AWS/GCP/Azure, identifies waste (idle resources, oversized instances, misconfigurations), and either auto-remediates or creates PRs for your review. We also integrate into CI/CD to catch costly mistakes before deployment—where fixes are 10x cheaper.
Tech stack: Multi-agent AI architecture, integrates with GitHub/GitLab for shift-left, lives in Slack/Teams for queries.
Early results: 30-35% cost reduction, 95% implementation rate.
We're a small team out of Utrecht (Netherlands). Would love feedback from anyone who's dealt with cloud cost pain—what's worked, what hasn't? / FinOps practice tips or IRL optimization tips!
Hi All! I'm John, founder of Yasu.
We built an AI cloud engineer that prevents and fixes cloud waste autonomously.
The problem we kept seeing: FinOps tools generate hundreds of "optimization recommendations" that sit in dashboards forever. Industry implementation rate is ~5%. The tools work fine—humans just can't keep up.
So we asked: what if the tool actually fixed things?
Yasu connects to your AWS/GCP/Azure, identifies waste (idle resources, oversized instances, misconfigurations), and either auto-remediates or creates PRs for your review. We also integrate into CI/CD to catch costly mistakes before deployment—where fixes are 10x cheaper.
Tech stack: Multi-agent AI architecture, integrates with GitHub/GitLab for shift-left, lives in Slack/Teams for queries.
Early results: 30-35% cost reduction, 95% implementation rate.
We're a small team out of Utrecht (Netherlands). Would love feedback from anyone who's dealt with cloud cost pain—what's worked, what hasn't? / FinOps practice tips or IRL optimization tips!