Documentation Grafana Cloud What's new in Grafana Cloud ML-Enhanced guidance on SLO target selection

ML-Enhanced guidance on SLO target selection

Many teams struggle with selecting a realistic SLO target when creating or modifying SLOs. The target sets the sensitivity for when an SLO will start to burn budget, impacting alerting noise and “error budget remaining”. If you assume you want to create an SLO to ensure “99.5% of HTTP requests return successfully in under 500 ms”, how do you know that 99.5% is a realistic target for your service? People often guess or take a number from management.

Grafana SLO in collaboration with the Machine Learning team is proud to announce a major enhancement to our SLO creation wizard. After defining an SLI, the “step 2” target selection page now shows a computed likelihood estimate to help users select a realistic target. We query 90 days of history from the metrics used in the SLI definition, and run simulations to predict the likelihood of meeting a given target given the history of the metrics. The user can slide the target percentage and see an updated prediction of the likelihood of meeting or breaching that target.