Optimize Performance, Reliability, and Cost. Together
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 — enabling safe, continuous, full-stack optimization.
👋 Hi everyone — 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 — using AI to automatically detect inefficiencies and reliability risks from telemetry data and generate ready-to-apply configur
From a performance engineering perspective, this should hit close to home. That’s the kind of automation that could save teams a lot of tuning cycles and post-incident retrospectives.
Hi everyone, I currently work at Akamas and Insights is our latest tool for addressing the often ignored problem of cloud related costs. It's a SaaS applictaion so it's unintrusive and you don't need to install it in your kubernetes cluster. You just need to supply your cluster's telemetry data from compatible sources (currently we support Dynatrace, Datadog, Prometheus and AWS-managed Prometheus) and Akamas Insights will import them and compute cost saving recommendations and potential cluster
Quite powerful tool to tame the huge complexity of analyzing performance, bottleneck and mainly waste potential in AWS envs. And nice looking too!
A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.
Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.
Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.
The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.