Automatically determine the cloud and container resources required to reduce cost and maximize performance in Azure. Optimizing cloud resources means going beyond financial analysis to impact resource efficiency, as choosing sub-optimal instances can be a cost and/or performance disaster. Densify’s machine learning analyzes workload patterns to determine the optimal size and family for your Azure Virtual Machines (VMs), enabling you to:

• Reduce application performance and stability issues associated with under allocating resources to your VMs
• Increase utilization and lower costs by ensuring that you are not over allocating capacity to your VMs
• Avoid leaving CPU, memory or other resources stranded by picking the wrong instance family

Densify’s analytics predictively, precisely, and continuously determine the appropriate resource settings for your Azure Kubernetes Service (AKS) containers allowing you to:

• Avoid application performance and stability issues
• Visualize the overall resource health of your entire Kubernetes environment with Histograms
• Increase node and cluster utilization by avoiding allocating too much CPU and memory to your applications
• Ensure cluster resource and namespace quotas are constantly aligned with app team requirements
• Lower your cloud bill by deploying fewer nodes for the same containers

Densify also optimizes for AWS and Google Cloud Platform.