StormForge on AWS

Minimize Resource Waste While Ensuring Application Performance on AWS EKS

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StormForge on AWS EKS

StormForge optimizes the efficiency of your AWS EKS-based applications to reduce cloud costs, improve application performance, and free up developers to focus on innovation, not application tuning.

StormForge includes both experimentation-based optimization that works in a non-production environment and observation-based optimization that works in production. The combination of these approaches means that you can deploy applications with confidence and ensure they are always running at peak efficiency.

Additional Resources

Challenge

Reduce cloud application costs while ensuring application performance and developer velocity

The widespread adoption of Kubernetes, and the portability and flexibility of containers, has fundamentally changed – and accelerated – how developers build and deploy applications in cloud-native environments. 

Yet inefficient Kubernetes and application configurations result in millions of dollars in wasted cloud resources, business-impacting performance and availability issues, and thousands of hours of lost productivity every year. In fact, 48% of cloud spend is estimated to be wasted, according to the StormForge Cloud Waste survey

Engineers need to be empowered to make resource decisions that minimize the cost of running applications – and the time and effort spent making decisions – while ensuring business goals are met. 

The StormForge and EKS Solution

Optimizing Amazon Elastic Kubernetes Service (EKS) for cost, performance, and productivity

StormForge is the leader in Kubernetes resource optimization. The StormForge platform makes it easy to achieve cloud resource efficiency at scale using machine learning to optimize applications automatically, both prior to deployment and in production. The result? A significant reduction in cloud resource usage and costs while still ensuring application performance that meets SLAs and accelerating developer velocity.

With advanced machine learning, StormForge recommends configuration settings that minimize wasted resources. StormForge is the only optimization solution that combines pre-prod, experimentation-based optimization with observation-based optimization in prod, enabling intelligent business trade-offs and ensuring peak efficiency without time-consuming, ineffective trial-and-error tuning.

Benefits

StormForge enables DevOps and SRE teams to understand the behavior of their Kubernetes applications and ensure they operate efficiently to minimize costs while ensuring performance that meets user expectations.

Purpose Built for Kubernetes

Cloud and distribution agnostic, StormForge runs anywhere EKS runs, with the ability to intelligently scale.

Proactive Resource Optimization

StormForge improves the efficiency of your cloud native applications, meaning fewer resources used and lower cloud costs, while still ensuring application performance.

In-depth application insights

With the ability to explore and analyze applications under a wide range of scenarios, StormForge helps you identify key architectural and performance improvements.

Get value fast

By leveraging the observability data you’re already collecting, you can start optimizing your production environment today.

Advantages

Proactive optimization in pre-prod

StormForge Optimize Pro employs a process of rapid experimentation in your on-prod environment, using load testing to simulate a wide range of scenarios. StormForge ML finds the configuration that will result in the optimal outcomes, based on your goals for cost and performance.


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Observation-based optimization in prod

After deployment, StormForge Optimize Live keeps your application running smoothly. StormForge machine learning analyzes historical metrics and trends from your observability tools to recommend changes to CPU and memory that will boost efficiency. Recommendations can be automatically implemented to put your operations on autopilot.


Advanced machine learning

StormForge’s patent-pending ML algorithm is designed to explore complex, multi-dimensional application parameter spaces in a highly efficient way, specific to your app and data, running in your AWS environment. By analyzing multiple parameters and goals, StormForge empowers users to understand cost/performance trade-offs and make intelligent business decisions.


Intelligent autoscaling

Machine learning analyzes actual usage and historical trends to predict future resource needs. Recommendations, which can be automatically implemented, closely track actual usage to eliminate resource slack and reduce risk of CPU throttling and out-of-memory errors.

Case Study

Cloud-Native Data Protection Service Provider

Challenges

A data management and data protection for cloud-native applications service provider with a Kubernetes-centric architecture wanted to right-size its Kubernetes deployment and ensure that development moves quickly.

Solution

StormForge for regression, scalability, and stress testing to ensure the company’s services are hyper-scalable for customers.

Results

StormForge with machine learning delivered 3-4x in time savings compared with the company’s standard process. The company can also now analyze customer environments and provide guidance to maximize efficiency for cloud backups, restores, captures, and mobility activities.

Get Started

How do I get started?

Getting Started with StormForge is as easy as 1-2-3.

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