Akamas Insights is an AI-powered optimization solution for Kubernetes. It analyzes telemetry from tools like Prometheus, Datadog, and Dynatrace to automatically detect inefficiencies and reliability risks across clusters, pods, and runtimes. It then provides ready-to-apply, data-backed configuration recommendations โ enabling safe, continuous, full-stack optimization.
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Do you use Akamas Insights?
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Maker
๐
๐ Hi everyone โ Stefano here, CTO and co-founder at Akamas.
We built Akamas Insights because we kept seeing the same problem in every Kubernetes environment: teams had tons of observability data but no clear way to turn it into action.
Optimization was still manual, time-consuming, and siloed between dev, SRE, and platform teams.
With Insights, we wanted to change that โ using AI to automatically detect inefficiencies and reliability risks from telemetry data and generate ready-to-apply configuration recommendations that are safe, measurable, and easy to review.
Itโs been exciting to see how teams during our beta used it to find wasted resources, fix misconfigured app runtimes (e.g. JVM), and actually collaborate on optimization for the first time ๐
Weโd love your thoughts โ how do you handle optimization in your stack today, and what would make Akamas Insights more valuable for you?
It leverages telemetry from popular observability backends to detect inefficiencies and generate actionable configuration improvements that drive real performance gains and cost savings.
vs Alternatives
Most optimization tools stop at dashboards or cost reports โ Insights closes the loop between observability data and actionable configuration changes.
It leverages telemetry from popular observability backends to detect inefficiencies and generate actionable configuration improvements that drive real performance gains and cost savings.
vs Alternatives
Most optimization tools stop at dashboards or cost reports โ Insights closes the loop between observability data and actionable configuration changes.