You Have Financial Visibility Covered. Now, It’s Time to Optimize.
Optimizing cloud resources means going beyond financial analysis to impact resource efficiency. Choosing sub optimal instances can be a cost or performance disaster in the cloud:
- Needlessly oversized instances can cause low utilization, wasting resources and increasing costs
- Performance problems happen if resources don’t align with what the apps actually need
- Auto Scaling groups can get pinned, and not scale up and down as loads change
- RIs, Saving Plans, and other commitments are much larger than necessary, locking in spend that you might be able to reduce before committing
Densify uses a unique, high trust approach to solve all of this, combining machine learning and deep analytics to automate the determination of the ideal instance types and resource specifications for your infrastructure, ensuring the optimal balance of performance and cost.
Optimization directives show exactly the steps to take to reduce costs while preserving cloud performance
Automatically Set Cloud Compute & Database Instance Types
Densify’s machine learning analyzes workload patterns to determine the optimal size and family for your compute and database 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
Optimize the Configuration of Your 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
Optimization analysis of AWS Auto Scaling groups
Densify provides top-down visibility into risks and opportunities across clouds
Achieve Top-Down Capacity Visibility Across Your Cloud Infrastructure
Densify combines detailed analysis of thousands of entities into a dynamic view of the scope, efficiency, and health across all your cloud environments. This lets you:
- Compare results between accounts, business groups, applications, or by any other tag
- Provide rankings by efficiency rating, amount of risk, savings potential, and other KPIs
- Assess the impact of implementing optimization recommendations
- Calculate financial impacts and estimated savings
Generate Detailed Reports and Data Extracts to 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
Densify Impact Analysis Reports provide complete visibility for stakeholders into the projected impact of recommended optimizations
See how Densify integrates with IaC tools to automate cloud resource selection
Seamlessly Integrate Machine Learning into CI/CD Pipelines & Automation Tools
Densify’s precise resource specifications integrate seamlessly into your deployment pipeline to automate the selection of instance types and scaling parameters for your cloud infrastructure, so you can:
- Free your teams from the burden of manually selecting resources
- Eliminate errors by using APIs to integrate directly into infrastructure as code (IaC) templates like Terraform or CloudFormation
- Ensure performance by continuously aligning resources with application requirements
"Densify…came up with the recommendation to optimize using an instance class that we had never even heard of…ultimately, we decided to implement the change, and as a result, we are more cost-efficient and our customer-facing metrics are completely unchanged."
- Mike Lee, Application Owner, Autodesk