Sold by: Pepperdata, Inc.
Deployed on AWS
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Pepperdata Capacity Optimizer is enterprise software that automatically increases utilization by up to 80%, improving performance and delivering 30% average cost savings on Amazon EMR. Pepperdata Capacity Optimizer eliminates the need for manual tuning by optimizing compute and memory in real time with no application code changes. Pepperdata Capacity Optimizer pays for itself, immediately decreasing instance hours/waste, and freeing developers from manual tuning to focus on revenue-generating innovation.
4.6
Overview
Pepperdata Capacity Optimizer provides real-time, automated Kubernetes resource optimization that increases utilization by 80%, improves performance by 20% on average, and delivers 30% cost savings on average with no application code changes.
Using patented algorithms to optimize Kubernetes and Amazon EMR, Pepperdata's Continuous Intelligent Tuning provides a real-time stream of data which allows scheduling decisions to be based on actual instead of allocated usage. This ensures that Kubernetes and Amazon EMR clusters use all available resources for each workload before adding new pods, thus reducing waste and cost by allowing more workloads to run on the same pod.
Pepperdata Capacity Optimizer also dynamically tunes the Cloud Autoscaler to respond to ever-changing application workloads in real time. The result is dramatically improved hardware efficiency, performance maximization, enhanced engineering productivity, and application resource optimization averaging 30% for Kubernetes and Amazon EMR. Pepperdata pays for itself by immediately decreasing instance hours/waste, increasing utilization, and freeing developers from constant manual tuning to focus on revenue-generating innovation.
Pepperdata Capacity Optimizer empowers customers to:
- Increase utilization by up to 80%
- Reduce monthly spend by an average of 30% and run more apps for the same cost
- Drive optimal price/performance cost outcomes
- Minimize (or eliminate) overprovisioning waste to run more applications without additional spend
- Eliminate manual tweaking and tuning of applications to free your developers from the tedium of managing apps to focus on revenue-generating innovation
Our customers range from startups and mid-market ISVs to the largest, most complex, and highly-scaled Kubernetes clusters in the world, at top enterprises such as Citibank, Autodesk, Royal Bank of Canada, and those in the Fortune 5.
PLATFORMS SUPPORTED: Amazon EKS, Spark and Amazon EMR on EKS, and Amazon EMR
Learn more about Pepperdata at pepperdata.com.
Pepperdata's version support matrix is available at https://help.pepperdata.com
Highlights
- Automatically optimize Kubernetes and Amazon EMR resources: Increase utilization up to 80%, improve performance by 20% on average, and slash costs by 30% on average - automatically, continuously, and in real time. Run the same number of workloads on fewer instances at lower cost.
- Free your developers from the tedium of trying to manual tuning applications for higher-value, revenue-generating activities with Pepperdata Capacity Optimizer, which optimizes node/pod performance automatically in real time without the need to apply recommendations or make code changes.
- Pepperdata pays for itself and has a 100% ROI Guarantee, with most customers achieving a 100-600% ROI in as little as 3 months. FOR A FREE 20-MINUTE PRODUCT DEMO, EMAIL info@pepperdata.com.
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Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
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Dimension | Cost/unit |
|---|---|
Per hour vCPU fee | $0.02 |
Dimensions summary
This listing uses one usage-based pricing dimension. You pay a per-hour fee for each vCPU the software optimizes. There are no tiers or fixed plans to choose from. Your cost scales directly with how many vCPUs run under the tool and for how long. When you use more vCPU-hours, you pay more; when you use fewer, you pay less. The software works across Kubernetes and Amazon EMR environments, but billing stays tied to this single vCPU-hour measure. This keeps the cost structure simple and aligned with your actual compute usage.
Top-of-mind questions for buyers
A vCPU is one virtual processor core that the software manages. Billing counts each vCPU under optimization, multiplied by the hours it runs. This applies across Kubernetes and Amazon EMR nodes. If the tool packs more pods onto the same vCPUs, you still pay only for the vCPUs present.
Charges accrue per vCPU-hour while nodes run under the tool. When your autoscaler removes nodes or scales down, fewer vCPUs run, so your charges drop. The software provisions new nodes only when existing ones are fully used, which reduces the vCPU-hours you pay for.
Yes. Billing follows actual vCPU-hours, so no manual plan change is needed. As clusters add vCPUs, charges rise; as they shrink, charges fall. The tool scales to thousands of nodes, and the per-hour vCPU fee stays the single measure across all of them.
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Resources
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Support
Vendor support
When you license Pepperdata software by purchasing Pepperdata Automated Resource Optimization for Kubernetes and Amazon EMR, it includes full technical support with guaranteed response times. Access Pepperdata Technical Support at support.pepperdata.com. If prompted to log in, use the same credentials that you use to log in to the Pepperdata dashboard.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Pepperdata, Inc.
By IBM Software
Top
25
In Container Cost Optimization, Cost Allocation and Accountability, Resource Cost Optimization
Top
10
In Analytic Platforms
Top
25
In High Performance Computing
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Real-time Resource Optimization
Patented algorithms optimize compute and memory allocation in real time based on actual usage patterns rather than allocated resources, enabling scheduling decisions that reduce waste across Kubernetes and Amazon EMR clusters.
Continuous Intelligent Tuning
Continuous stream of data-driven optimization that automatically tunes cluster resources and dynamically adjusts Cloud Autoscaler responses to changing application workloads without requiring application code modifications.
Multi-platform Support
Supports optimization across Amazon EKS, Spark, Amazon EMR on EKS, and Amazon EMR environments for comprehensive resource management across different deployment architectures.
Automated Workload Consolidation
Enables multiple workloads to run on the same pod by optimizing resource utilization, allowing more applications to execute on fewer instances while maintaining performance.
Zero-touch Configuration
Automatically optimizes node and pod performance without requiring manual tuning, code changes, or manual application of recommendations, eliminating the need for developer intervention in resource management.
Real-time Pod Resource Optimization
Automatically adjusts CPU, GPU, and memory requests and limits based on real-time workload behavior through continuous analysis and vertical scaling without pod restarts or downtime
Node and Karpenter Cluster Optimization
Optimizes node configurations and integrates with Karpenter to ensure efficient cluster-level resource utilization and node consolidation
Spot Instance and GPU Resource Management
Intelligently manages spot instances and optimizes GPU allocation for machine learning and AI workloads to reduce compute costs while maintaining workload reliability
Self-Hosted Deployment Architecture
Deploys entirely within customer infrastructure as a self-hosted platform, ensuring workload data and cluster metrics remain under customer control without external API dependencies
Dynamic Replica Scaling and Cost Monitoring
Dynamically adjusts replica counts based on actual demand patterns and provides real-time visibility into Kubernetes spending with actionable cost comparison dashboards
Real-time Resource Automation
Automates application resource management in real time by addressing resource utilization and overprovisioning to prevent performance issues before they occur across compute, storage, and network resources.
Multi-cloud Cost Optimization
Optimizes cloud spend across all major public clouds through automated resource provisioning, supporting cloud migration planning and on-premises workload optimization.
Kubernetes Resource Management
Automates resource management in Kubernetes environments across all major container management platforms, enabling elasticity optimization at every layer.
Hypervisor Infrastructure Optimization
Automates virtualized, private, and hybrid cloud infrastructure management across all major hypervisors through rightsizing and host density optimization.
GPU Workload Optimization
Optimizes GPU workloads for resource-intensive applications by balancing performance and cost efficiency through automated resource allocation.
Standard contract
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Customer reviews
Banking
Great Easy to use Product. A must for ETL and Big Data
Reviewed on Aug 12, 2024
Review provided by G2
What do you like best about the product?
Capacity Optimizer is a simple easy to implement - easy to use application that will save you money right from the get go. Right out of the box you will see a 10 to 15 % savings. The support is top notch - they go above and beyond in getting the most out of the product. This is a no brainer for any Big Data workload.
What do you dislike about the product?
The Pepperdata team works closely with you to answer and problems you may have. I have not had any serious problem that would make me not use it
What problems is the product solving and how is that benefiting you?
Cost Savings - We have seen a significant cost savings
Visiblity - We have more visiblity than what AWS gives us with Pepperdata
Visiblity - We have more visiblity than what AWS gives us with Pepperdata
Benjamin S.
Improves spark report performance and saves overall compute spend.
Reviewed on Aug 12, 2024
Review provided by G2
What do you like best about the product?
The ease of installation, great dashboards for cost and capacity visibility.
What do you dislike about the product?
Wish we could use it on our other applications!
What problems is the product solving and how is that benefiting you?
Lowering our overall cost.
Rahul C.
Saved 75% cost on EMR
Reviewed on May 23, 2024
Review provided by G2
What do you like best about the product?
The capacity optimizer helped us to reduce the wastage on EMR clusters and increased the efficiency. For one of the clusters - the savings was as big as 75%. For others we are seeing atleast 30% savings.
What do you dislike about the product?
Needs fine tuning multiple times, I would love to see some automated feedback in place to recommend the right set of settings based on the workloads.
What problems is the product solving and how is that benefiting you?
Cost optimization
Lee G.
Save money on your big data workloads
Reviewed on Feb 13, 2024
Review provided by G2
What do you like best about the product?
The capacity optimizer has been proven to save a lot of money. What I like best about the product, though, is the support we have received throughout our journey of migrating our EMR clusters from EC2 to EKS.
What do you dislike about the product?
There are still some features that need to be worked out for EMR on EKS and EMR Notebooks, but the Pepperdata team is actively working on these. These are relatively new offerings, so it's not surprising that there is still some work that needs to be done.
What problems is the product solving and how is that benefiting you?
The optimizer's main benefit is cost savings on compute, specifically with workloads running on EMR.
Nachiket K.
Good for visualization. Need more details and understanding 9f.how the optimization works
Reviewed on Feb 13, 2024
Review provided by G2
What do you like best about the product?
The visualization is good with clear uncluttered dashboards and metrics analyser
What do you dislike about the product?
Optimizer does not work always. Details on how the optimizer works is not so clear.
What problems is the product solving and how is that benefiting you?
Optimizing kubernetes resource usage for data pipeline infrastructure