Tools

10 Best Kubernetes Rightsizing and Autoscaling Tools in 2026

The best Kubernetes rightsizing and autoscaling tools in 2026 include CAST AI, ScaleOps, StormForge, VPA, Goldilocks, Robusta KRR, Karpenter, KEDA, HPA, and Cluster Autoscaler. These tools optimize pod resources, replicas, and nodes. Usage.ai complements them with AWS infrastructure rightsizing recommendations and automated cloud commitment optimization.

Cloud & FinOps Content Specialist

Haider Ali

Cloud & FinOps Content Specialist

November 10, 2026

29 min read

Key takeaways

Best automated platforms: CAST AI and ScaleOps optimize workloads and infrastructure with less manual tuning.

Best pod rightsizing tools: StormForge, VPA, Goldilocks, and Robusta KRR help optimize CPU and memory requests.

Best autoscaling tools: HPA and KEDA scale replicas, while Karpenter and Cluster Autoscaler manage nodes.

Best for additional cloud savings: Usage.ai combines AWS infrastructure rightsizing recommendations with automated commitment optimization for eligible cloud spending.

10 Best Kubernetes Rightsizing and Autoscaling Tools Compared

Tool	Primary function	Best for	Pricing
CAST AI	Workload and node optimization	Automated infrastructure efficiency	Commercial plans, quote-based
ScaleOps	Continuous resource optimization	Production workload automation	Custom quote
StormForge	ML-based rightsizing	CPU and memory tuning	Custom quote
Kubernetes VPA	Pod resource recommendations and updates	Native rightsizing	Free, open source
Goldilocks	VPA-based recommendations	Visual resource analysis	Free, open source
Robusta KRR	Prometheus-based recommendations	Resource audits	Free, open source
Karpenter	Dynamic node provisioning	Flexible node autoscaling	Free, open source
KEDA	Event-driven replica scaling	Queues and asynchronous workloads	Free, open source
Kubernetes HPA	Metrics-based replica scaling	Variable application traffic	Included with Kubernetes
Cluster Autoscaler	Node group scaling	Existing node group architectures	Free, open source
Usage.ai (Complementary)	AWS rightsizing recommendations and cloud commitment optimization	Reducing eligible infrastructure and commitment costs	Savings-based pricing

Commercial pricing varies by plan, deployment size, and agreement. Free tools still require infrastructure and operational resources.

Important: These tools are not all alternatives. HPA and Karpenter, for example, commonly work together. HPA changes pod replicas, while Karpenter provisions nodes when additional capacity is required.

Kubernetes Rightsizing vs Autoscaling: What Actually Changes?

Kubernetes resource optimization happens at three main layers.

Layer	What changes	Tools
Pod rightsizing	CPU and memory requests or limits	VPA, Goldilocks, KRR, StormForge
Replica autoscaling	Number of running pods	HPA, KEDA
Node autoscaling	Worker-node capacity	Karpenter, Cluster Autoscaler

Rightsizing reduces overprovisioned resource requests. Autoscaling responds to changing demand.

For example, reducing a pod’s CPU request from 2 vCPUs to 1 vCPU may improve scheduling efficiency, but it does not automatically reduce your bill. Savings usually occur when that efficiency allows fewer nodes, smaller instances, or lower billable consumption.

How We Evaluated These Tools

We compared rightsizing capabilities, autoscaling methods, supported environments, automation controls, implementation requirements, and pricing to help teams choose tools for different Kubernetes workloads.

CAST AI: Best for Automated Kubernetes Infrastructure Optimization

CAST AI combines Kubernetes workload rightsizing with automated node provisioning and consolidation. Rather than relying only on resource recommendations, it can manage supported infrastructure changes through configured automation policies.

Key features

Workload optimization: Adjusts CPU and memory allocations using workload data.

Node provisioning: Selects compatible compute capacity for pending workloads.

Consolidation: Replaces or removes inefficient node capacity.

Spot optimization: Supports Spot capacity strategies with availability controls.

Pros

✅ Combines workload and node optimization.
✅ Reduces manual capacity planning.
✅ Useful for large or frequently changing clusters.

Cons

❌ Requires careful disruption and availability policies.
❌ Adds a commercial platform to the Kubernetes stack.

Pricing: Commercial pricing depends on the selected products and managed environment. Confirm current rates through a quote.

Best for: Teams wanting broad Kubernetes optimization through one platform.

ScaleOps: Best for Continuous Workload Optimization

ScaleOps continuously adjusts Kubernetes resource configurations to better match workload demand. It is designed for teams that want to reduce overprovisioning without repeatedly editing resource requests manually.

Key features

Continuous rightsizing: Tunes CPU and memory allocation as demand changes.

Workload-aware optimization: Considers resource usage and application behavior.

Automation policies: Controls how optimization changes are applied.

Infrastructure efficiency: Helps improve workload placement and node utilization.

Pros

✅ Reduces repetitive resource tuning.
✅ Supports changing production workloads.
✅ Combines recommendations with automation.

Cons

❌ Requires policy and integration setup.
❌ Pricing requires a commercial quote.

Pricing: Custom pricing based on deployment requirements and selected capabilities.

Best for: Platform teams managing resource-intensive production clusters.

StormForge: Best for Data-Driven Pod Rightsizing

StormForge analyzes workload performance and resource metrics to recommend Kubernetes CPU and memory configurations. It is useful when engineers want more systematic rightsizing than manually interpreting utilization charts.

Key features

ML-based recommendations: Uses historical workload metrics.

CPU and memory tuning: Recommends resource requests and limits.

Controlled automation: Supports applying recommendations through configured workflows.

Monitoring integration: Works with supported metrics sources.

Pros

✅ Data-driven resource recommendations.
✅ Supports review and controlled application.
✅ Focuses on workload efficiency.

Cons

❌ Recommendation quality depends on representative metrics.
❌ Changes still require performance validation.

Pricing: Commercial pricing is quote-based.

Best for: Teams seeking intelligent rightsizing with operational controls.

Kubernetes Vertical Pod Autoscaler (VPA): Best Native Pod Rightsizing Tool

Vertical Pod Autoscaler recommends CPU and memory requests based on observed workload usage. Depending on its configuration and Kubernetes version, it can also apply supported resource updates.

Key features

Recommendation mode: Provides resource targets without modifying workloads.

Automatic updates: Adjusts resource requests through supported update modes.

Resource analysis: Uses historical CPU and memory consumption.

Kubernetes integration: Works through Kubernetes controllers.

Pros

✅ Free and open source.
✅ No commercial optimization subscription.
✅ Supports recommendations and automated updates.

Cons

❌ Some changes may recreate or evict pods.
❌ Can conflict with HPA when both control the same resource metric.

Pricing: Free, open source. Cluster and monitoring costs still apply.

Best for: Teams wanting Kubernetes-native resource rightsizing.

Goldilocks: Best for Visual Rightsizing Recommendations

Goldilocks, developed by Fairwinds, uses VPA recommendations to display suggested resource requests in a web dashboard.

Unlike automated optimization platforms, Goldilocks focuses on helping engineers review resource configurations before making changes.

Key features

VPA-based recommendations: Shows suggested CPU and memory values.

Web dashboard: Makes recommendations easier to inspect.

Namespace controls: Enables analysis for selected namespaces.

Non-enforcing workflow: Does not automatically apply resource changes.

Pros

✅ Free and open source.
✅ Useful for teams new to rightsizing.
✅ Keeps changes under engineering control.

Cons

❌ Engineers must implement recommendations.
❌ Requires VPA components and suitable metrics.

Pricing: Free, open source.

Best for: Teams wanting an accessible Kubernetes rightsizing dashboard.

Robusta KRR: Best for Prometheus-Based Resource Audits

Robusta KRR is an open-source Kubernetes resource recommender that analyzes historical metrics from Prometheus-compatible systems.

Its CLI-based workflow is useful for teams already collecting resource metrics and wanting repeatable rightsizing reports.

Key features

Prometheus analysis: Uses historical resource metrics.

CPU and memory recommendations: Identifies potentially oversized requests.

CLI workflow: Supports local and scheduled analysis.

Optional enforcement: Separate components can apply recommendations.

Pros

✅ Free and open source.
✅ Useful with existing Prometheus monitoring.
✅ Can run recommendation analysis without installing a full optimization platform in the cluster.

Cons

❌ Requires sufficient metrics history.
❌ Basic KRR recommendations are not automatically applied.

Pricing: Open-source KRR is free. Optional commercial services may have separate fees.

Best for: Teams wanting lightweight, repeatable Kubernetes rightsizing audits.

Karpenter: Best for Flexible Node Autoscaling

Karpenter provisions worker nodes based on pending pod requirements. Instead of relying exclusively on predefined node groups, it can select compatible instance types and consolidate capacity when possible.

Karpenter is especially relevant to Amazon EKS. Azure’s AKS Node Auto Provisioning is also built on Karpenter, while other providers need separate compatibility checks.

Key features

Dynamic provisioning: Creates nodes for unscheduled workloads.

Instance selection: Chooses suitable instance types.

Node consolidation: Removes or replaces unnecessary capacity.

Scheduling awareness: Considers resource requests and placement constraints.

Pros

✅ Flexible infrastructure provisioning.
✅ Can improve node utilization.
✅ Free and open source.

Cons

❌ Does not rightsize pod resource requests.
❌ Provider compatibility and disruption settings need attention.

Pricing: Free, open source. Cloud infrastructure charges apply.

Best for: EKS teams seeking flexible node provisioning and consolidation.

KEDA: Best for Event-Driven Autoscaling

Kubernetes Event-driven Autoscaling (KEDA) scales workloads using external events and metrics, including queue length and supported application signals.

It can scale compatible workloads to zero when idle, making it particularly useful for asynchronous processing.

Key features

Event triggers: Scales based on queues and external metrics.

Scale to zero: Stops eligible idle workloads.

Scaler integrations: Supports numerous event sources.

HPA integration: Uses Kubernetes horizontal scaling mechanisms.

Pros

✅ Excellent for event-driven applications.
✅ Supports scale-to-zero workloads.
✅ Free and open source.

Cons

❌ Does not rightsize CPU or memory requests.
❌ Incorrect trigger thresholds can cause inefficient scaling.

Pricing: Free, open source. Event-source and infrastructure costs may apply.

Best for: Queue workers, background processing, and event-driven services.

Kubernetes Horizontal Pod Autoscaler (HPA): Best for Native Replica Scaling

HPA adjusts the number of running workload replicas based on CPU, memory, or configured custom and external metrics.

For example, an application may run three replicas during normal traffic and increase to eight when demand rises.

Key features

Replica scaling: Adjusts pod counts automatically.

CPU and memory metrics: Supports resource-based targets.

Custom metrics: Supports application-specific scaling.

Scaling behavior: Configures stabilization and scaling policies.

Pros

✅ Built into Kubernetes.
✅ No commercial autoscaling subscription.
✅ Works with node autoscalers.

Cons

❌ Does not change pod resource requests.
❌ Requires appropriate metrics and scaling targets.

Pricing: Included with Kubernetes. Metrics infrastructure and additional compute capacity may incur costs.

Best for: Applications with variable traffic and measurable scaling signals.

Cluster Autoscaler: Best for Existing Node Group Architectures

Cluster Autoscaler adjusts Kubernetes node-group capacity when pods cannot be scheduled or existing nodes become unnecessary.

Unlike Karpenter’s more flexible provisioning approach, Cluster Autoscaler typically works within configured node groups.

Key features

Node group scaling: Adds or removes worker nodes.

Pending pod detection: Responds to unschedulable workloads.

Scale-down evaluation: Identifies removable nodes.

Cloud integration: Supports compatible provider node groups.

Pros

✅ Mature open-source autoscaler.
✅ Works with established node group architectures.
✅ Supports common managed Kubernetes environments.

Cons

❌ Usually depends on predefined node groups.
❌ Does not rightsize CPU or memory requests.

Pricing: Free, open source. Cloud infrastructure charges apply.

Best for: Teams already using managed Kubernetes node groups.

Which Kubernetes Tools Should You Use Together?

Kubernetes optimization usually works best as a stack rather than a single product.

Scenario	Suggested stack	Why it works
Small EKS team	KRR + HPA + Karpenter	Rightsizing recommendations, replica scaling, and node provisioning
Event-driven applications	KEDA + Karpenter	Event-based replicas and dynamic node capacity
Conservative production setup	Goldilocks + HPA + Cluster Autoscaler	Manual rightsizing review with automated capacity scaling
Fully managed optimization	CAST AI or ScaleOps	Broader optimization through a commercial platform
Stable eligible compute baseline	Rightsizing stack + Usage.ai	Infrastructure efficiency plus commitment savings

These are example combinations. Check controller compatibility and avoid overlapping automation policies before deployment.

For more options, explore our cloud infrastructure optimization tools comparison.

The Kubernetes Optimization Order: Rightsize, Scale, Consolidate, Commit

A useful way to approach Kubernetes optimization is to address resource efficiency before purchasing additional long-term capacity commitments.

Rightsize pods. Use VPA, Goldilocks, KRR, or a commercial optimizer to identify oversized CPU and memory requests.

Scale replicas. Use HPA or KEDA to adjust workload capacity as traffic changes.

Consolidate nodes. Use Karpenter or Cluster Autoscaler to reduce unnecessary worker-node capacity where workloads permit.

Optimize commitments. Evaluate the stable, eligible portion of remaining cloud usage for Savings Plans, Reserved Instances, or other supported discounts.

The principle: Commit to the floor, autoscale the rest.

The goal is to avoid purchasing commitments based on temporary peaks. However, existing commitments, expected growth, and workload volatility must be considered before changing coverage.

Usage.ai: Commitment Savings for Your Remaining Cloud Baseline

Usage.ai doesn’t manage pods, replicas, or nodes. It works on the pricing layer underneath your cluster. Once rightsizing and autoscaling have reduced your infrastructure, Usage.ai helps you pay less for the stable usage that remains by managing eligible commitments across AWS, Azure, and Google Cloud. It also offers AWS rightsizing recommendations for instances outside Kubernetes-managed capacity.

Key features

AWS Rightsizing Recommendations: Identifies potentially oversized instances and estimates savings.

Autopilot: Automates eligible commitment management within configured controls.

CoPilot: Supports approval-based commitment recommendations.

Read-only Savings Test: Evaluates savings opportunities without modifying production workloads.

Pricing: Usage.ai uses savings-based pricing with no upfront platform fee under its published model. Fees depend on realized savings from covered commitments and the customer agreement.

Best for: Teams with a stable compute baseline after Kubernetes optimization who want to reduce its effective price.

Learn more about Usage.ai pricing.

CLOUD COST OPTIMIZATION

Commit to the Floor. Autoscale the Rest.

Find additional Kubernetes savings with a read-only test. No app changes; purchases require separate authorization.

Run a Free Savings Test

Kubernetes Cost Example: Rightsizing Plus Commitment Discounts

Suppose a Kubernetes environment runs 20 worker nodes at an illustrative On-Demand cost of $150 per node monthly.

Rightsizing and consolidation allow five nodes to be removed. The remaining compute may then qualify for additional commitment discounts

Metric	Before	After rightsizing	After rightsizing + commitments
Worker nodes	20	15	15
Monthly compute cost	$3,000	$2,250	$1,912.50
Monthly savings vs original	—	$750	$1,087.50
Annualized savings vs original	—	$9,000	$13,050

Illustration only. Assumes $150 per node monthly, five nodes removed, and an additional hypothetical 15% discount on all remaining compute spending. Figures exclude platform fees and assume full discount eligibility. Actual commitment discounts and coverage vary.

The important point is that rightsizing reduces the infrastructure required, while commitments can reduce the effective price of eligible remaining usage.

See our cloud cost optimization best practices for additional savings approaches.

How to Safely Automate Kubernetes Rightsizing

Optimization should reduce waste without creating performance problems.

Measure baseline performance: Monitor CPU, memory, latency, and errors.

Validate recommendations: Test resource changes before broad rollout.

Protect critical workloads: Configure availability and disruption controls.

Coordinate autoscalers: Avoid conflicting VPA, HPA, or commercial automation policies.

Verify savings: Compare actual cloud bills, not only resource utilization.

A cloud cost governance framework can help define ownership and approval policies.

Final Verdict: Which Kubernetes Optimization Tools Are Best?

For workload rightsizing, consider StormForge, VPA, Goldilocks, or Robusta KRR. CAST AI and ScaleOps provide broader commercial automation.

For autoscaling, use HPA or KEDA to manage replicas and Karpenter or Cluster Autoscaler to manage node capacity.

Once the environment is efficiently sized, Usage.ai can help evaluate eligible commitment savings on the remaining cloud baseline.

The best approach is usually a compatible combination of tools, not a single winner.

Frequently asked questions

What are the best Kubernetes rightsizing and autoscaling tools?

Leading options include CAST AI, ScaleOps, StormForge, VPA, Goldilocks, Robusta KRR, Karpenter, KEDA, HPA, and Cluster Autoscaler. Each addresses different workload or infrastructure optimization needs.

Can Kubernetes rightsizing and autoscaling tools work together?

Yes. Rightsizing tools adjust resource requests, HPA or KEDA manages replicas, and Karpenter or Cluster Autoscaler manages node capacity. Compatibility and automation policies must be checked.

What are the best free Kubernetes rightsizing tools?

Goldilocks, Robusta KRR, and VPA are open-source options. Goldilocks and KRR primarily provide recommendations, while VPA supports resource updates in configured modes.

Is Karpenter better than Cluster Autoscaler?

Karpenter provides flexible node provisioning in supported environments, while Cluster Autoscaler works with established node groups. The better option depends on cloud compatibility and cluster architecture.

Does Kubernetes rightsizing automatically reduce cloud costs?

No. Reducing resource requests alone does not guarantee savings. Costs typically decrease when optimization reduces billable infrastructure, improves instance selection, or enables other pricing efficiencies.

See your savings first