Cloud and Container Optimization for Amazon Web Services
Automatically determine the cloud and container resources required to reduce cost and maximize performance in AWS
Amazon EC2 Optimization
Densify’s machine learning analyzes workload patterns to determine the optimal size and family for your AWS EC2 instances, enabling you to:
- Reduce application performance and stability issues associated with under allocating resources to your instances
- Increase utilization and lower costs by ensuring that you are not overallocating capacity to your instances
- Avoid leaving CPU, memory or other resources stranded by picking the wrong instance family
See How Our Machine Learning Can Optimize Your EC2 Environment »
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Optimization for Amazon RDS
Optimize your AWS Relational Database Services (RDS) using Densify’s machine learning based analysis, ensuring that you:
- Reduce application performance and stability issues associated with under allocating resources to your database
- Increase utilization and lower costs, for one of the most expensive service types, by ensuring that you are not overallocating capacity to your database
- Avoid leaving CPU, memory or other resources stranded by picking the wrong instance family
- Protect your existing RI investment by identifying when an RDS instance is covered by an RI and deffering the recommendation until it expires
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Optimize Your Amazon Auto Scaling Groups
Densify analyzes the utilization and scaling behavior of Auto Scaling groups (ASGs) and performs simulations to determine their optimal configuration, enabling you to:
- Analyze whether scaling groups are elastic, or are constrained by resource configurations
- Automatically assign the right instance size and family for nodes in your ASG
- Determine optimal scaling parameters such as the minimum and maximum size of your ASG
- Highlight when your scaling policies should be reviewed because nodes are not being added and removed in line with workload demand
Optimization analysis of AWS Auto Scaling groups
Optimize Your Amazon EKS Clusters
Densify’s analytics predictively, precisely and continuously determine the appropriate resource settings for your AWS EKS containers and nodes 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
- Automatically assign the ideal amount of CPU and memory for the nodes and node groups in your clusters
- Determine the right instance families for your nodes
- Automatically model scaling behavior to optimize scale groups
- Lower your cloud bill by deploying fewer nodes for the same containers
Densify automatically analyzes thousands of containers to determine optimal settings
Enable Collaboration with Product & Application Owners
Densify automatically produces Impact Analysis and Recommendation Reports to share with stakeholders, letting you:
- Clearly articulate details for every optimization recommendation, including predicted utilization, effort level, and cost impact
- Include as an attachment to ITSM change tickets or integrate into business collaboration and approval workflows using Densify’s API
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Integration with Automation API’s and IaC
Integrate With CI/CD Pipelines & Automation Tools
- Free your teams from manually selecting resources
- Eliminate errors – use APIs to tie directly into infrastructure as code templates like Terraform or AWS CloudFormation
- Ensure Performance by continuously aligning resources with application requirements