Incorrect resource specifications can be a disaster for container environments:
Densify uses a unique approach to solve all of this, combining machine learning and deep analytics to automate the process of allocating the ideal resources to all components in your Kubernetes stack, ensuring the optimal balance of performance and cost.
Densify’s machine learning analyzes workload patterns to determine the optimal CPU and memory requests and limits for your containers.
Densify analyzes the utilization and scaling behavior of nodes in your clusters and performs simulations to determine their optimal configuration.
Densify analyzes your clusters and namespaces to ensure they are configured in an optimal way.
Densify’s precise resource specifications integrate seamlessly into your deployment pipeline to automate the correct sizing of containers, so you can:
Combine detailed analysis of thousands of entities into a single, top-down view of the scope, efficiency and health of your container environment.