StormForge by CloudBolt
HPA Workload Optimization
Your autoscaling settings stay where you put them.
StormForge optimizes HPA-managed workloads through bi-dimensional autoscaling to ensure optimal vertical and horizontal scaling behavior. When the HPA is scaling on a resource metric, other rightsizing solutions will alter the vertical resource requests and change the workloads scaling profile causing it to thrash, leading to downtime and unexpected scaling behavior. StormForge uses Machine Learning to detect scaling behavior and update requests alongside HPA target utilization to preserve intended scaling.
But that optimization erodes silently. A CI/CD deploy resets your HPA targets to what’s in the manifest. An Argo CD sync overwrites a tuned value. A teammate changes a setting without realizing it was being managed. Scaling behavior degrades — and nobody notices until costs spike or performance drops.
Here’s why drift happens: when StormForge right-sizes a workload, it adjusts resource requests — which shifts the utilization ratio HPA depends on. Bi-dimensional autoscaling recalculates the HPA target alongside requests to keep your scaling behavior intact. But now the target utilization is being managed continuously. The next trigger – human or automation – resets it. The drift cycle begins.
Bi-dimensional autoscaling
When requests change, the utilization ratio HPA uses to trigger scaling shifts. If you only adjust requests without updating the HPA target, you change when and how aggressively the workload scales. Bi-dimensional autoscaling solves this by treating requests and HPA targets as a coupled pair, preserving your intended scaling behavior while reducing resource waste.
Continuous HPA reconciliation
The StormForge Applier watches HPA target utilization settings and detects drift. When something changes outside of StormForge, it reconciles — automatically restoring optimized values.
CI/CD-aware workload reconciliation
Recommended request settings are maintained across deployments. When Argo CD, Flux, or another CD tool deploys, StormForge manages the source of truth for optimized settings — ensuring the correct requests are deployed as the workload gets updated.
No silent regressions
Optimization doesn’t erode over time. Settings don’t drift back to defaults after a deploy. What StormForge optimized stays optimized — until a new recommendation says otherwise.
The Agent + Applier
Start with visibility. Add automation when you’re ready.
Not every team is ready to automate optimization on day one. Some need to see the recommendations first, build trust with the data, and prove value before granting write access. StormForge is built for that path.
StormForge runs as two lightweight, self-optimizing components. Each has a distinct job. Install them together or separately depending on where your team is.
The Agent: observe and recommend
A Kubernetes controller paired with a metrics forwarder. The controller watches your workloads and configuration. The forwarder streams CPU, memory, and usage metrics from each container to the StormForge SaaS backend over HTTPS. Just resource telemetry.
The SaaS-hosted ML engine analyzes usage patterns and generates right-sizing recommendations on the schedule you define. First recommendation takes just a few minutes.
The Applier: execute with precision
An optional, separately installable component that applies recommendations to your workloads. Three apply methods give you control over how changes land:
Server-side patches — Direct resource updates to workload specs.
Mutating admission webhook — Enables in-place pod resizing and advanced rollout strategies (Immediate and Hybrid).
GitOps export — Download recommendations as patches for CI/CD-driven apply. No Applier required.
After applying, the Applier validates rollout health and monitors the workload. If something goes wrong, it catches it. If HPA targets drift, it reconciles. If a CI/CD deploy overwrites optimized request settings, it reapplies.
Agent without Applier
Visibility first. Install the Agent alone to see recommendations without acting on them. No RBAC write permissions needed.
Agent + Applier
Full automation. Install both and define your policies. StormForge observes, recommends, applies, validates, and reconciles — continuously.
SaaS Delivery Model
All the intelligence in the cloud. Minimal footprint in your cluster.
Many Kubernetes optimization tools are self-hosted — meaning the recommendation engine, analytics, and data storage all run inside your cluster. Full control, but at the cost of operational overhead that scales with every cluster you onboard.
What gets installed in-cluster
The StormForge Agent and optional Applier — both lightweight and self-optimizing. Metrics forwarding and applying recommendations. That’s it.
What runs in SaaS
The ML recommendation engine. The web UI. All historical data and analytics. No self-hosted Prometheus dependency at scale. No cluster-side data warehousing.
No bundled infrastructure to manage
Competitors that run self-hosted require you to operate their Prometheus instance, manage their storage, and scale their infrastructure alongside yours. StormForge streams a targeted set of resource metrics to the SaaS backend via HTTPS — your cluster stays lean.
TCO advantage that compounds over time
Self-hosted tools add operational overhead to every cluster you onboard. SaaS overhead stays flat. At 10 clusters, the difference is noticeable. At 50, it’s a line item.
Calculate the total cost of ownership difference:
TCO Calculator →Trusted by the world’s largest financial institutions
Metrics are scoped to resource usage and performance data — not application payloads. Data is transmitted over HTTPS. SOC 2 compliant. No sensitive workload data leaves your cluster.
Integrations
Your stack stays yours. StormForge fits inside it.
Rip-and-replace doesn’t work for platform teams managing production Kubernetes. You’ve already invested in your GitOps pipeline, your observability stack, and your deployment model. StormForge works alongside all of it — not instead of it.
GitOps: Argo CD + Flux
Native integration with both. Configure StormForge as a recognized field manager in Argo CD. Mutate pods directly alongside Flux to prevent reconciliation conflicts. Or skip the Applier entirely and export recommendations as patches for your CI/CD pipeline.
Scaling: KEDA + HPA
Full support for workloads scaled by KEDA ScaledObjects. HPA target utilization is reconciled automatically — not just respected, actively maintained.
Platforms: EKS Add-On + OpenShift
Available as an AWS EKS add-on for streamlined procurement and installation. Dedicated install path for Red Hat OpenShift Container Platform.
Three ways to apply recommendations
Server-side patches (default). Mutating admission webhook (for in-place resizing). GitOps export (for CI/CD-driven apply). Pick the method that fits your deployment model — per namespace, per workload.
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
Up to 85% in Kubernetes savings
Right-size workloads and reduce your node footprint.
99% allocation accuracy
Maximize node efficiency and avoid wasted spend.
Ready to put Kubernetes rightsizing on autopilot?
*Free trial includes full optimization on 1 cluster for 30 days.
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