Automated
Kubernetes Optimization

Densify is an AI-driven platform that automates the optimization of cloud and Kubernetes resources. It helps organizations reduce costs, improve application performance, and eliminate manual tuning by continuously analyzing and adjusting resource allocations across cloud and container environments.

Densify’s Kubernetes optimization leverages AI-driven analytics to determine optimal resource settings for containers, pods, deployments, ReplicaSets, nodes, node groups, and clusters. It provides: 

• Automated Optimization: Eliminates manual effort by automatically adjusting resource configurations. 

• Full-Stack Analysis: Considers the entire stack, from containers to clusters, ensuring comprehensive optimization. 

• Risk and Waste Detection: Identifies resource saturation and imbalances to prevent performance issues.  

Organizations using Densify can expect: 

• Cost Reduction: Lower cloud and container costs by up to 50% through efficient resource utilization. 

• Improved Reliability: Enhance application stability by preventing resource-related issues. 

• Operational Efficiency: Reduce manual intervention with automated optimization processes. 

• Enhanced Visibility: Gain insights into resource usage and optimization opportunities across environments

Densify empowers FinOps teams by: 

• Providing Cost Transparency: Offers detailed insights into the cost impact of resource changes. 

• Enabling Policy Enforcement: Integrates with policy frameworks like HashiCorp Sentinel, Azure Policy, and AWS Config to enforce resource usage guidelines. 

• Facilitating Collaboration: Allows easy sharing of findings and recommendations with stakeholders.

Yes, Densify seamlessly integrates with: 

• Infrastructure as Code (IaC): Supports tools like Terraform and CloudFormation for automated resource selection. 

• CI/CD Pipelines: Incorporates optimization into continuous integration and deployment workflows. 

• Policy Frameworks: Works with existing policy enforcement tools to maintain compliance and governance.

Densify provides broad platform support, including: 

• Cloud Providers: AWS, Azure, Google Cloud Platform. 

• Container Orchestration: Kubernetes, Red Hat OpenShift, Amazon EKS, Azure AKS, Google GKE, Nutanix Karbon Platform (NKP), Oracle OKE. 

• Virtualization Platforms: VMware environments. 

To explore Densify’s capabilities: 

• Book a Demo: Schedule a personalized demonstration to see Densify in action. 

• Request a Free Trial: Start a 30-day free trial to experience the benefits firsthand. 

Visit the Densify homepage to learn more and get started. 

Achieving a balance between performance and cost efficiency in Kubernetes environments requires intelligent resource specifications that match workload demands and the ability to scale up and down if needed. Densify offers AI and machine learning-driven precision to rightsize container resources, nodes and autoscaler configurations, ensuring performance SLAs are met while minimizing cost. 

• Best Practices include: 

• Using analytics that watch over workloads to learn true needs.

• Apply accurate CPU and memory limits to prevent overprovisioning.

• Continuously monitor usage to identify idle workloads. 

• Automate optimization settings to suit resource demands.

• Use autoscalers to match capacity with real-time demand. 

Kubernetes autoscaling helps dynamically adjust capacity to match changing workloads, however scalers are reactive and they don’t know what the actual correct resources are for every container. Analytics like Densify determine that precisely so that scalers can act based on correct information. 

Approach: 

• Horizontal Pod Autoscaler (HPA): Scales pod count based on metrics like CPU or custom application signals. 

• Vertical Pod Autoscaler (VPA): Adjusts CPU/memory requests of pods over time. 

• Cluster Autoscaler: Manages node groups by adding/removing nodes based on unschedulable pods. 

• Densify analyzes actual usage trends to recommend optimal HPA/VPA settings and cluster scaling limits to prevent over-allocation and underperformance. 

Correct resource requests and limits are determined by observing workloads and building predictive models for how they might behave in the future. This allows for proactive settings vs reactive, on the fly which can introduct instability.  

Techniques: 

• Collect metrics via Prometheus or other telemtry to determine historical usage. 

• Enable analytics engines like Densify that provide machine learning and AI to determine intelligent settings.

• Avoid static, blanket settings; tune per workload. 

There are a number of solutions that target Kubernetes cost optimization but very few deal with the optimization of the workloads and infrastructure itself.  

Popular software solutions: 

• Densify: AI / ML-powered optimization and rightsizing, with direct integration to autoscaling and scheduling policies for automated remediation

• Kubecost: Detailed cost breakdown per namespace, label, and pod

• CloudZero: Focuses on unit cost metrics per feature or team

• Finout: Broad Cloud cost management and reporting

• StormForge: Kubernetes performance and cost tuning

• Cloudhealth: Enterprise cloud cost governance

• Cloudability: FinOps and cost governance

To improve Kubernetes cluster efficiency, focus on rightsizing container resources by adjusting CPU and memory requests and limits based on actual usage patterns. Use autoscaling mechanisms like HPA (Horizontal Pod Autoscaler) and Cluster Autoscaler to dynamically allocate resources based on demand.

Implement bin packing strategies and node affinity rules to maximize pod density and reduce node count. Monitor workloads with tools like Prometheus and Densify to identify overprovisioned or underutilized resources.

Finally, enforce resource quotas and use taints/tolerations to control workload placement and avoid resource fragmentation. 

Yes, Densify and Kubex allow you to optimize K8s from the individual containers right down to the optimal cloud instance for nodes.