Our Servo sits along the application in either a load-generation or a canary environment and gets its metrics from a monitoring system. From there, the Servo reports its observations back to the Opsani SaaS, where our ML algorithms determine a set of changes and send those back to the servo. The servo then makes the suggested environmental changes, observes the results, and retransmits the output back to the ML backend. And, through this AI-powered iterative process, Opsani quickly tunes the application’s operating environment for optimal performance at the lowest cost. Opsani can also auto-promote those settings into the CI-CD pipeline by integrating with tools like Spinnaker, Jenkins, and Github.
Learn more about how Opsani makes adjustments to your pipeline in our datasheet.