ScaleOps vs StormForge (2026): Which Rightsizing Tool Wins

ScaleOps vs StormForge is the workload-rightsizing head-to-head, and the decision comes down to automation style. ScaleOps is the autonomous option: continuous pod rightsizing that applies changes hands-off and scales proactively before traffic spikes. StormForge is the precision option: ML-model-based recommendations built for performance-critical workloads where a bad rightsizing call causes an incident. Both typically save 25-40% on workload compute.

Both tools live in the same category of our Kubernetes cost optimization tools roundup: workload rightsizing, the layer that fixes over-provisioned CPU and memory requests. If you are still comparing across layers - rightsizing versus node optimization versus cost visibility - start with the roundup or our Cast AI vs Karpenter comparison. This page goes deep on the two commercial rightsizers for teams that already know the workload layer is where their waste lives.

The short answer

• Pick ScaleOps if you want hands-off, autonomous rightsizing - an in-cluster operator that continuously adjusts requests and scales up before traffic spikes, with no recommendation queue for humans to review.
• Pick StormForge if your workloads are performance-critical and an incident costs more than the compute savings - its ML models are built to rightsize without the performance regressions that naive VPA recommendations risk.
• Either way, keep Karpenter. Both tools rightsize pods; neither provisions nodes. They complement Karpenter rather than replacing it.

Deciding factor to pick

The rule: choose ScaleOps when you want the tool to act for you, and StormForge when you want it to be right before it acts.

What each tool is

• ScaleOps is a commercial platform focused specifically on workload-level rightsizing and predictive scaling. It runs as an in-cluster operator that analyses actual usage patterns and adjusts CPU and memory requests continuously - autonomously, without a human reviewing each change. It also scales proactively before traffic spikes based on learned patterns, and has extended into GPU rightsizing. The market has noticed: ScaleOps raised a $130M Series C at an $800M valuation in March 2026, driven largely by AI and GPU demand. It does not touch node provisioning - it sits on top of Karpenter or Cluster Autoscaler.

• StormForge is the ML-based rightsizer, acquired by F5 in 2024 and now part of the F5 AI Infrastructure portfolio. It uses machine learning models trained on historical utilization to recommend optimal CPU and memory requests - typically more accurate than the percentile-based math VPA uses. Its defining strength is performance-critical workloads: financial services and healthcare teams pick it precisely because blind VPA-style recommendations risk incidents, and StormForge’s models weigh performance impact, not just utilization. It is commercial only, sold under enterprise licensing.

ScaleOps vs StormForge: head-to-head

A few of these dimensions deserve unpacking.

Automation posture. This is the real divide. ScaleOps is built to act: it rightsizes continuously and autonomously, which is why teams describe it as hands-off. StormForge is built to be trusted: its ML models are trained to avoid the recommendation that saves 30% of memory and causes a Saturday-night OOMKill. If your platform team is comfortable delegating resource requests to a controller, ScaleOps removes the most toil. If your workload owners need to see and trust each change, StormForge’s model fits better.

Savings. On paper they tie - both typically deliver 25-40% on workload compute. The honest difference is time-to-savings and risk shape. ScaleOps compounds faster because nothing waits on a human. StormForge deliberately trades a slice of savings for performance safety on the workloads where that trade is worth it.

Ownership and momentum. ScaleOps’s $130M Series C at an $800M valuation (March 2026) signals an independent vendor investing hard, with AI/GPU rightsizing as the growth engine. StormForge’s F5 acquisition brings enterprise support and integrations that regulated buyers value, but post-acquisition integration is still settling and the community is smaller than ScaleOps or Cast AI.

When to choose ScaleOps

ScaleOps wins when hands-off automation is the priority. Choose it when:

• You want autonomous, continuous rightsizing with no recommendation queue - the operator adjusts requests as usage changes.
• Your traffic has predictable spikes and you want the tool to scale up before them, not react after.
• You run GPU workloads and want rightsizing that extends beyond CPU and memory.
• You already run Karpenter and want the workload-layer savings it does not deliver - ScaleOps drops in beside it with no rip-and-replace.
• You want fast time-to-savings without changing how workload owners work day to day.

In short, ScaleOps is the pick when you want the rightsizing problem to disappear into an operator and stay disappeared.

When to choose StormForge

StormForge wins when the cost of an incident exceeds the cost of compute. Choose it when:

• Your workloads are performance-critical - trading systems, payment paths, patient-facing services - where a bad rightsizing call is an outage, not a rounding error.
• You have been burned by VPA recommendations that looked fine on percentiles and failed under real load.
• You are an enterprise buyer that values F5’s support, licensing structure, and vendor relationship, especially in regulated industries.
• Your workload owners need reviewable, ML-backed recommendations rather than a controller that acts unilaterally.

In short, StormForge is the pick when being right matters more than being fast, and you want an enterprise vendor standing behind the model.

Do they replace VPA or Karpenter?

Neither tool replaces Karpenter - both are explicitly complementary. Karpenter provisions nodes; ScaleOps and StormForge rightsize the pods that land on them. They actually make Karpenter better: accurate pod requests give Karpenter honest signals, so bin-packing tightens and node count drops. The standard 2026 stack in our full roundup is Karpenter for nodes, one of these two for workloads, and Kubecost or OpenCost for visibility.

VPA is the tool they do displace. VPA ships free in the Kubernetes ecosystem, but auto mode has restart edge cases and its recommendations are simpler than what either commercial tool produces. The pragmatic on-ramp: start with Goldilocks and VPA in recommendation mode, then upgrade to ScaleOps or StormForge when manual review overhead exceeds the licence cost.

What about ScaleOps vs Cast AI?

A lot of shoppers comparing rightsizers are really running a three-way with Cast AI, so it is worth being precise: Cast AI is not a rightsizing specialist. It optimizes the cluster and node layer - bin-packing, spot automation, replacing Cluster Autoscaler with its own provisioner - and its workload-level rightsizing is thinner than either tool on this page. ScaleOps and StormForge optimize the workload layer on top of whatever node provisioner you run. That is why “ScaleOps vs Cast AI” is usually a question of which layer hurts most, not which tool is better, and why large clusters sometimes run Cast AI for nodes and ScaleOps for pods. For the node-layer decision, see Cast AI vs Karpenter; for the visibility angle, Cast AI vs Kubecost.

Common pitfalls

• Enabling automation on day one. Run either tool in observe or recommend mode first and compare its calls against live behaviour before letting it act. This is doubly true for ScaleOps, where automation is the product.
• Judging savings on vendor numbers. Both claim the same 25-40% band; your result depends on how over-provisioned you already are. Re-baseline for 2-4 weeks on your own cluster before declaring ROI.
• Buying rightsizing when your waste is at the node layer. If nodes are poorly packed or spot usage is zero, Karpenter and spot automation may be the bigger lever. Rightsizing fixes requests, not provisioning.
• Skipping visibility. Without Kubecost or OpenCost attribution, you cannot prove which tool saved what, or hold teams accountable for the spend that remains.

The verdict

ScaleOps vs StormForge is a choice of automation philosophy, not savings ceiling. Both deliver 25-40% on workload compute, both complement Karpenter, and both beat raw VPA. Pick ScaleOps for autonomous, hands-off rightsizing with proactive scaling and GPU momentum - the default for most teams in 2026. Pick StormForge when workloads are performance-critical, incidents are expensive, and F5’s enterprise backing matters. Whichever you choose, validate in observe mode on your own workloads first, and see where each fits in the wider stack in our Kubernetes cost optimization tools 2026 roundup.