Explainer Video
Getting started with Kubernetes may be straightforward, but as you ramp up for Day 2 operations, you’re forced to make a choice. Either overprovision cloud resources by 50% or more, driving costs through the roof, risk business, impacting application performance and availability issues, or slow down time-to-market to spend time manually tuning your complex Kubernetes environment. It’s a terrible choice to have to make, especially given how important cloud native transformation is to your company’s success.
What if you could ensure your cloud native environment was never over provisioned, while still meeting SLAs and SLOs, and do it automatically so your dev team could focus on innovating?
StormForge automates the process of achieving and maintaining Kubernetes resource efficiency at scale.
We provide a platform for continuous scenario planning, using machine learning to provide actionable insights both in production and pre-production. With minimal time and effort, we start providing recommendations for configuration changes that will boost efficiency. Recommendations can be implemented automatically or with your approval. You’re always in full control.
While production optimization provides fast and easy efficiency gains, pre-production optimization lets you go deep to fully understand system behavior and achieve peak efficiency. StormForge performs experiments in your dev cluster using appropriate workloads to simulate a wide range of scenarios. Our machine learning analyzes the essentially infinite number of possible configurations to minimize cost, ensure performance, and find insights in patterns to drive key architectural improvements.
StormForge closes the gaps organizations experience as they reach Day 2 Kubernetes operations.
Getting started with StormForge is as easy as 1-2-3.
Talk to one of our Kubernetes experts, see a demo, and take it for a test drive.
Start today to reduce cloud costs, improve application performance, and get your dev teams focused on innovating.
Start getting resizing recommendations minutes from now.
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