Real-Time Cloud Cost Optimization for AWS

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Pepperdata Capacity Optimizer

Pepperdata’s real-time cloud cost optimization is the only solution that addresses in-application waste automatically.

Removes the need for manual tuning, applying recommendations, or changing application code
Eliminates the waste inside the application that other optimizations can’t
Frees engineers from tedious optimization tasks and empowers them to focus on innovation

Compare conventional optimizations to
Pepperdata Real-Time Cost Optimization

Compare conventional optimizations to Pepperdata

Most cloud cost optimization solutions focus only on improving infrastructure price/performance. Pepperdata Capacity Optimizer immediately and automatically reduces the often unnoticed waste inside the Spark application itself.

Pepperdata Capacity Optimizer immediately and automatically reduces the often unnoticed waste inside the Spark application itself that can significantly inflate your costs.

Observability and Monitoring

Managed Autoscaling

Instance Rightsizing

Manual Application Tuning

Spark Dynamic Allocation

What it does

Identifies and quantifies the waste in Spark environments
Provides tuning recommendations for developers to implement manually

What it doesn’t do

Automatically eliminate waste

What Pepperdata Capacity Optimizer does

Capacity Optimizer observes your applications to identify waste and automatically reduces it
Continuously and autonomously tunes Spark application clusters in real time
Frees developers to spend time on higher value tasks versus tediously applying recommendations

What it does

Prevents instances from running before or after resources are requested

What it doesn’t do

Prevent applications from wasting requested resources at runtime
Doesn’t eliminate waste inside applications

What Pepperdata Capacity Optimizer does

Reduces instance hours and costs by reacting to underutilized resources
Optimizes the autoscaler so that it launches new nodes only when running nodes are fully utilized

What it does

Matches instance resources to application requirements
Prevents the deployment of resources that can’t be scheduled
Karpenter for Kubernetes can automatically select instances based on your unique workload profiles

What it doesn’t do

Prevent inefficient applications from driving waste—even with optimal instance types

What Pepperdata Capacity Optimizer does

Solves the problem of inefficient applications after you’ve rightsized
Automatically eliminates waste inside the application in real-time

What it does

Developers do their best to match resource allocations to the peak of the utilization curve
Prevent the application from failing due to too few resources

What it doesn’t do

Can’t match resource requests in real time to actual utilization of dynamic workloads
Doesn’t eliminate the waste that occurs when usage is not at peak (which is most of the time)

What Pepperdata Capacity Optimizer does

Capacity Optimizer detects wasted capacity in each node in real time
Automatically increases the virtual capacity of underutilized nodes
Helps run more jobs without increased spend

What it does

Acts like an autoscaler within Spark
Adds more tasks when needed and kills idle ones—improving resource utilization

What it doesn’t do

Spark Dynamic Allocation (SDA) doesn’t prevent low resource utilization inside the Spark application itself
Can lead to 30 percent waste or more

What Pepperdata Capacity Optimizer does

Capacity Optimizer informs the scheduler about waste inside the Spark application itself
Automatically provisions the correct resources for applications to run for peak optimization

Observability and Monitoring

Managed Autoscaling

Instance Rightsizing

Manual Application Tuning

Spark Dynamic Allocation

What it does

Identifies and quantifies the waste in Spark environments
Provides tuning recommendations for developers to implement manually

What it doesn’t do

Automatically eliminate waste

What Pepperdata Capacity Optimizer does

Capacity Optimizer observes your applications to identify waste and automatically reduces it
Continuously and autonomously tunes Spark application clusters in real time
Frees developers to spend time on higher value tasks versus tediously applying recommendations

What it does

Prevents instances from running before or after resources are requested

What it doesn’t do

Prevent applications from wasting requested resources at runtime
Doesn’t eliminate waste inside applications

What Pepperdata Capacity Optimizer does

Reduces instance hours and costs by reacting to underutilized resources
Optimizes the autoscaler so that it launches new nodes only when running nodes are fully utilized

What it does

Matches instance resources to application requirements
Prevents the deployment of resources that can’t be scheduled
Karpenter for Kubernetes can automatically select instances based on your unique workload profiles

What it doesn’t do

Prevent inefficient applications from driving waste—even with optimal instance types

What Pepperdata Capacity Optimizer does

Solves the problem of inefficient applications after you’ve rightsized
Automatically eliminates waste inside the application in real-time

What it does

Developers do their best to match resource allocations to the peak of the utilization curve
Prevent the application from failing due to too few resources

What it doesn’t do

Can’t match resource requests in real time to actual utilization of dynamic workloads
Doesn’t eliminate the waste that occurs when usage is not at peak (which is most of the time)

What Pepperdata Capacity Optimizer does

Capacity Optimizer detects wasted capacity in each node in real time
Automatically increases the virtual capacity of underutilized nodes
Helps run more jobs without increased spend

What it does

Acts like an autoscaler within Spark
Adds more tasks when needed and kills idle ones—improving resource utilization

What it doesn’t do

Spark Dynamic Allocation (SDA) doesn’t prevent low resource utilization inside the Spark application itself
Can lead to 30 percent waste or more

What Pepperdata Capacity Optimizer does

Capacity Optimizer informs the scheduler about waste inside the Spark application itself
Automatically provisions the correct resources for applications to run for peak optimization

Pepperdata customers save an average of 30 percent on their Spark workload costs in the cloud on top of the other optimization methods.

Autonomous

Dynamically optimizes without manual intervention or recommendations

Continuous

Automatically calibrates resources 24/7 based on application needs

Real Time

Identifies waste and optimizes resources in real time without models

Autonomous

Dynamically optimizes without manual intervention or recommendations

Continuous

Automatically calibrates resources 24/7 based on application needs

Real Time

Identifies waste and optimizes resources in real time without models

Augmented FinOps for Spark on Amazon EMR and Amazon EKS

Augmented FinOps for Spark

Pepperdata’s Augmented FinOps solution works automatically in real time to maximize the optimization of your cloud deployments. Here’s how it does it.

Daily and yearly customer savings with Pepperdata e1715115021247

Cloud usage and cost transparency

Observe and identify cloud cost data at the Spark application layer.
Workload optimization

Run more apps without additional spend by packing pending pods onto underutilized nodes automatically in real time.

LEARN ABOUT AUGMENTED FINOPS

Autodesk reduces Amazon EMR costs by 50%

“Pepperdata allowed us to significantly increase capacity for our Amazon EMR workloads and reduce our EC2 costs by over 50 percent. We can focus on our business, while they optimize for costs and performance.”

—Mark Kidwell, Chief Data Architect, Platforms and Services, Autodesk

Challenge

Autodesk was experiencing runaway costs as they scaled its Spark workloads
Solution

Autodesk installed Pepperdata Capacity Optimizer to automate cloud cost optimization in real time
Results

Autodesk successfully reduced Amazon EC2 costs by over 50 percent