Continuous Kubernetes rightsizing engineers can trust
No more manual tuning for CPU and memory requests
Your developers shouldn’t have to guess, and your platform team shouldn’t burn cycles tweaking YAML. Our ML-powered engine analyzes usage patterns every 15 seconds to forecast demand and rightsize resources automatically—adjusting in real-time to daily usage spikes and long-term trends.
Rightsizing that works with your autoscalers
Unlike basic tools that break HPA, our bi-dimensional scaling harmonizes vertical and horizontal pod adjustments. We fine-tune both resource requests and HPA utilization targets—cutting overprovisioning while avoiding CPU throttling, latency issues, or OOMKills.
“Stormforge has been a game-changer for our team. We’ve been able to effortlessly right-size our Kubernetes clusters and optimize our workloads saving our engineering teams hours on an ongoing basis.”
The only rightsizer that understands Java
Standard rightsizing tools miss the mark with Java. We go deeper—optimizing JVM heap sizes and container memory limits in tandem, so your memory footprint shrinks without triggering GC storms or container crashes.
Built to optimize thousands of workloads, automatically
Deployed across some of the largest Kubernetes estates in the world. We auto-discover every pod, learn its patterns in days, and apply optimization recommendations within policy-defined guardrails. Integrates directly into your GitOps pipelines—no rewrites required.
Know what you’re saving—in real dollars
Your optimization recommendations deserve accurate cost data behind them. Our integrated cost allocation connects directly to your cloud bills, showing actual savings based on your negotiated rates and discounts—not inflated estimates using on-demand pricing. See exactly what each recommendation will save before you apply it, and track realized value after.
Feature highlights
ML-forecasting
Bi-dimensional scaling (patent pending)
JVM optimization
Policy automation
GitOps integration
Enterprise visibility
Bill-accurate savings
Why platform engineers choose CloudBolt
90% faster time-to-savings
From months to hours, for immediate cost reductions.
Reduced manual work
Eliminate repetitive, unsustainable resource tuning
40-60% Kubernetes savings
Right-size workloads and reduce your node footprint.
99% allocation accuracy
Maximize node efficiency and avoid wasted spend.
What our customers say
Stormforge has been a game-changer for our team. We’ve been able to effortlessly right-size our Kubernetes clusters and optimize our workloads saving our engineering teams hours on an ongoing basis. What truly sets Stormforge apart is their incredible team—not only is the tool easy to use, but the people behind it are professional, knowledgeable, and genuinely supportive. In a world of over-promising and under-delivering, Stormforge is a company that actually delivers on their promise. It’s a refreshing and valuable partnership.”
StormForge has added immediate gains by overall net-capacity savings due to overprovisioned workloads.
StormForge was easy for me to get up and running quickly, and we achieved immediate efficiencies that reduced our cloud costs and lowered the amount of toil we had from manually configuring requests.
The machine learning that StormForge leverages to set requests and limits on pods allows our engineering and SRE teams to focus on our customer experience rather than infrastructure settings.
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FAQs
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Is StormForge delivered as a SaaS solution or available for on-premises deployment?
StormForge Optimize Live uses a hybrid architecture. The StormForge Agent runs entirely within your Kubernetes cluster (on-premises), collecting metrics and applying recommendations locally. The web application and machine learning control plane are SaaS-hosted, accessible at app.stormforge.io. This gives you the security of keeping your workload data within your environment while providing the convenience of a managed service for analysis and recommendations.
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How is StormForge hosted within Kubernetes environments?
The StormForge Agent is deployed as a Helm chart directly into your Kubernetes cluster in the stormforge-system namespace. It runs alongside your applications, collecting metrics every 15 seconds and applying recommendations locally. The SaaS control plane processes the machine learning analysis and provides the web interface for viewing recommendations and configuring optimization settings. No application data leaves your cluster – only anonymized usage metrics are transmitted.
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How can recommendations from StormForge be applied?
You have complete flexibility in how you apply recommendations. During the initial 7-day learning period, you can only apply recommendations manually for review. After that, you can: 1) Apply recommendations on-demand through the web UI with “Apply Now”, 2) Enable automatic deployment by setting the live.stormforge.io/auto-deploy annotation to “Enabled” for hands-free optimization, or 3) Integrate recommendations into your CI/CD workflow by downloading patches from the UI or using the StormForge CLI. You can also configure deployment thresholds to ensure only impactful changes trigger pod restarts.
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What is the difference between autoscaling and rightsizing, and how does StormForge support both?
Yes, StormForge provides bi-dimensional autoscaling that combines both approaches. Horizontal autoscaling (like HPA) adds or removes pod replicas in seconds to handle traffic spikes, while vertical rightsizing continuously optimizes the CPU and memory requests and limits for each container over hours, days, and weeks. Unlike basic VPA tools that conflict with HPA, StormForge uses forecast-based machine learning to harmonize both dimensions. We optimize the resource settings of the pods that sit on your nodes (requests/limits), while tools like Karpenter optimize the nodes themselves. This gives you the best of both worlds – rapid scaling for performance and intelligent rightsizing for cost efficiency.
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How do I know the savings numbers are accurate?
Unlike tools that estimate savings using public list prices, CloudBolt connects your optimization recommendations directly to your actual cloud bills. Savings projections reflect your negotiated rates, committed use discounts, and credits—so the numbers you see are the numbers you’ll actually realize.