Pepperdata Capacity Optimizer

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
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Autonomous

Dynamically determines where more work can be done and applies optimizations without manual intervention or recommendations

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Continuous

Runs 24/7 without developer oversight, constantly calibrating application resources based on changing needs

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Real Time

Identifies where more work can be done and optimizes those resources automatically in real time without the use of models

white bg 3rd icon

Autonomous

Dynamically determines where more work can be done and applies optimizations without manual intervention or recommendations

icon continuous cco page

Continuous

Runs 24/7 without developer oversight, constantly calibrating application resources based on changing needs

white bg 2 icon

Real Time

Identifies where more work can be done and optimizes those resources automatically in real time without the use of models

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

5 myths

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

5 myths

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

5 myths

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

5 myths

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

5 myths

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

5 myths

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

5 myths

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

5 myths

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

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

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

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    Challenge

    Autodesk was experiencing runaway costs as they scaled its Spark workloads

  • cco autodesk icon solution 50 50px

    Solution

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

  • cco autodesk icon results 50 50px

    Results

    Autodesk successfully reduced Amazon EC2 costs by over 50 percent

Optimize your cloud costs by 30% within 6 hours

Pepperdata pays for itself with 100% ROI or more guaranteed.
You pay only if we save you money.

If you’re running Spark, give us 6 hours and we’ll save you 30% on top of the other optimizations that you’ve already done.

Here’s how the evaluation works:

  1. Install Pepperdata: ~30 minutes: Pepperdata is installed in your environment.
  2. Run Pepperdata: ~5 hours: Run your workloads as normal. Pepperdata goes to work immediately reducing the waste in your Spark environment.
  3. Review results: ~30 minutes: Meet with Pepperdata Solutions Architect to review your cost savings, utilization data, and ROI

Sign up for a free, no-risk Proof-of-Value to see how much you can save with Pepperdata Capacity Optimizer.

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Products

A Quick Guide to Get You Started with Spark on Kubernetes (K8s)

Pepperdata Real-Time Cost Optimization
E-Books

Pepperdata Real-Time Cost Optimization for Data-Intensive Workloads on Amazon EMR and EKS

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Case Studies

Financial Services Giant Saves $20M with Pepperdata Real-Time Cost Optimization

Explore More

Looking for a safe, proven method to reduce waste and cost by up to 47% and maximize value for your cloud environment? Sign up now for a free Cost Optimization Proof-of-Value to see how Pepperdata Capacity Optimizer can help you start saving immediately.