StormForge · StormForge (a CloudBolt company) · infraplz.dev
StormForge (a CloudBolt company)
StormForge
ML-based Kubernetes resource optimization platform. Machine learning models trained on historical utilization patterns predict optimal CPU and memory requests and limits. Optimize Live provides automated rightsizing that applies via the Kubernetes API or exports as YAML for GitOps. Node Optimization uses ML to guide cluster autoscaler decisions with predictive algorithms. Java JVM heap size recommendations included.
StormForge is a Kubernetes resource optimization platform that applies ML models trained on historical workload utilization to predict optimal CPU and memory resource requests and limits. The optimization engine analyzes 14+ days of metrics per container, computes recommendations calibrated to actual usage distributions, and applies them either directly to live deployments via the Kubernetes API or exports as YAML for GitOps workflows.
Optimize Live is the continuous mode: it monitors utilization in real time and adjusts resource settings as usage patterns shift without manual review cycles. Node Optimization extends the same predictive approach to cluster autoscaler configuration and node group sizing. JVM heap size recommendations cover Java workloads specifically.
Paid-only; PAYG and Enterprise tiers via sales. Kubernetes-only; AWS, GCP, and Azure supported.
Key Features
ML rightsizing recommendations: analyzes 14+ days of per-container utilization metrics to predict CPU and memory resource request and limit settings calibrated to actual usage distributions
Optimize Live continuous mode: monitors utilization in real time and adjusts resource settings automatically as workload patterns change, without requiring manual review cycles
Direct Kubernetes API patching: applies recommended resource settings to live deployments immediately, with optional GitOps export mode that generates YAML for review before application
Node Optimization: applies ML-driven predictions to cluster autoscaler configuration and node group sizing, guiding scale-up and scale-down based on forecast workload demand
JVM heap size recommendations: specialized optimization for Java workloads that analyzes heap utilization and GC behavior to recommend optimal `-Xmx` and `-Xms` settings
EKS, GKE, AKS, and self-managed Kubernetes on AWS, GCP, and Azure with cloud-provider-specific instance selection recommendations
