StormForge
Machine learning that continuously rightsizes Kubernetes CPU and memory to cut cloud spend.
About
StormForge uses machine learning to automatically rightsize Kubernetes workloads, reducing cloud spend while improving reliability. Its Optimize Live product deploys a lightweight agent that analyzes existing Prometheus-compatible metrics and trains per-workload models on historical usage to generate continuous CPU and memory recommendations.
Teams review recommendations in the UI or CLI, apply them on a schedule, or export them as Kubernetes patches for GitOps workflows, with tuning for cost savings versus reliability goals. StormForge reports typical customer cloud cost reductions of 50 to 70 percent (vendor-reported) and is available in the IBM Cloud Catalog.
What it can do
- ML-generated CPU and memory rightsizing recommendations
- Per-workload models trained on historical usage
- Automatic, scheduled, or GitOps-based application
- HPA coordination and OOM protection guardrails
Setup
Install the agent with a single Helm chart and connect it to your Prometheus-compatible metrics stack.
Prerequisites
A Kubernetes cluster with Prometheus-compatible observability metrics.
Use cases
Works with
Trust score — 8/10
Scored 8/10 (verified tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 121; Trending score 76/100 from researcher assessment.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.