Extract the Most
From Your Cloud Investment
Pepperdata Capacity Optimizer eliminates the hassle of manual tweaking and tuning while delivering up to 50% cost reduction by:
- Automatically adjusting cluster resources in real time.
- Using available resources per each workload before adding new nodes or pods.
- Dynamically tuning your autoscaler to respond to your changing workloads in real time.
- Precisely visualizing the realized and potential cost savings per team.
Increase Capacity Utilization for Your Workloads
Capacity Optimizer rapidly identifies where more work can be done and adds tasks to nodes with available resources. The result: CPU, memory, and I/O resources are automatically optimized to increase utilization, enabling more applications to be launched in both Kubernetes and traditional big data environments. Working autonomously in real time, Capacity Optimizer frees your developers to develop rather than fine tune.
Pepperdata Boosts 3D Design Software Performance,
Cuts Costs
A software company found that scaling Amazon EMR resources to handle workloads resulted in runaway costs. Check out the case study to see how Pepperdata significantly increased capacity for their Amazon EMR workloads and reduced costs by over 50%!
Automatically Reduce Your Amazon EMR Costs
- Automatically tune your cloud deployment for optimal performance
- Get full-stack observability, automated tuning, and job-specific recommendations for Spark and MapReduce
- Automatically optimize node performance and prevent waste by applications
- Customize alerts to quickly understand and troubleshoot application and infrastructure issues
Benefits for Your Team
Operation Teams
Ensure cluster stability and efficiency
Run more jobs on existing hardware
Avoid overspending on infrastructure
Application Teams
Run more jobs
Access additional cluster capacity
Spend less time in backlog queues
Business Teams
Report on capacity trends
Get accurate chargeback reporting
Increase productivity and ROI