Hari Chandrasekhar

Optimize Kubernetes performance and cost with Karpenter and Sedai. Learn how AI-driven insights enhance node provisioning, predictive scaling, and instance selection.

Learn how to seamlessly integrate Infrastructure as Code (IaC) with autonomous systems for cloud cost & resource optimization. This guide explores strategies for maintaining DevOps best practices while leveraging AI-driven optimization in CI/CD pipelines, ensuring your IaC remains accurate and your cloud costs are minimized. Ci/CD tools mentioned include Jenkins, Argo CD, Circle CI and GitLab CD. IaC tools mentioned include Terraform, Ansible, AWS Cloud Development Kit (CDK), Puppet and Helm. A case study showing how Gitlab IaC is integrated with Sedai's autonomous optimization capability at KnowBe4, a leading cybersecurity platform is included.

A technology company achieved a 25% cost saving across 1,400 Kubernetes services with Sedai's autonomous optimization technology by rightsizing Dev/Test environments. AI-powered autonomous optimization optimized Kubernetes requests and limits in Kubernetes workloads and optimized instance count and type. An AI driven approach was more effective than manual optimization, especially given the small spend on each individual service. Optimization is one key capability of Sedai's autonomous cloud management platform.

In the past few years, we have seen a huge increase in adoption for Kubernetes. Kubernetes is an excellent platform, which provides you with all the facilities to fine-tune your application and run it the way you need. We at Sedai, use Kubernetes for deploying our applications along with surveillance and some other managed services from AWS. One question we get a lot is “Is there a need for another system or an autonomous system to manage Kubernetes?” Kubernetes is in fact a declarative system, an excellent one at which you can specify the needs of running your application, and Kubernetes will take care of the rest.