CAST AI vs. Kubecost vs. Sealos: Choosing the Right K8s Cost Management Tool

Kubernetes gives you elastic, cloud-agnostic capacity—but it also makes costs slippery. Teams discover too late that unused headroom, over-provisioned requests, chatty services, and idle nodes quietly burn budget. Meanwhile, finance wants showback by team and product, and engineering wants hands-off automation that reduces costs without breaking SLAs.

Three names frequently come up when you look for answers: CAST AI, Kubecost, and Sealos. They represent three complementary approaches to Kubernetes cost management:

• CAST AI automates infrastructure optimization (autoscaling, bin packing, and spot diversification) to drive immediate savings.
• Kubecost provides granular, open-source cost visibility and allocation so you can measure, alert, and improve continuously.
• Sealos is a Kubernetes-based platform that emphasizes multi-tenant governance and developer self-service. It can underpin cost control with quotas, guardrails, and seamless deployment of cost tools.

This article explains what each is, why cost management matters, how the tooling works under the hood, and practical ways to use them—individually or together.

Why Kubernetes Cost Management Matters

Kubernetes changes how you consume infrastructure:

• Shared clusters blur the line between “who uses what.”
• Dynamic scaling means daily cost profiles change with traffic.
• Cloud provider SKUs (on-demand, reserved, spot/preemptible) complicate pricing.
• Over-provisioning requests leads to low utilization and higher node counts.

Without cost management you risk:

• Budget overrun: 20–40% of cloud spend is often waste in early K8s adoption.
• No accountability: Teams can’t see costs per namespace or service, blocking showback/chargeback.
• Slower delivery: Engineers fly blind, fear changing resources, and delay optimizations.

A strong cost practice gives you:

• Visibility: Accurate allocation by namespace, label, and workload.
• Optimization: Rightsizing, bin packing, and cheaper SKUs with guardrails.
• Governance: Budgets, quotas, alerts, and developer self-service with guardrails.
• FinOps alignment: Data and processes that inform planning and accountability.

How Kubernetes Cost Management Works

Regardless of tool, most solutions follow a pattern:

• Data sources:

  • Kubernetes metrics (resource usage, requests/limits) via Prometheus or APIs.
  • Node/asset prices from cloud providers (AWS, GCP, Azure) or on-prem price books.
  • Cloud billing exports (e.g., AWS CUR, GCP BigQuery billing, Azure Cost Management).
  • Container runtime and network metrics to assign shared costs.

• Cost allocation:

  • Map node costs down to pods/containers using usage and requests.
  • Allocate shared/idle costs across tenants using a policy (by CPU share, memory share, or equally).
  • Attribute network/storage costs based on traffic and volumes.
  • Roll up to labels (team, product, environment) for showback/chargeback.

• Optimization:

  • Identify over- and under-provisioned workloads.
  • Choose the right node types and sizes.
  • Shift to spot/preemptible where safe, with fallback strategies.
  • Consolidate nodes (bin packing) and scale down when idle.

• Governance and workflows:

  • Budgets, alerts, and dashboards visible to engineering and finance.
  • Policies for safe-to-evict workloads, pod disruption budgets (PDBs), and retry logic.
  • GitOps and automation to apply optimizations consistently.

Tool Overviews

CAST AI: Automated Infrastructure Optimization

What it is:

• CAST AI is a SaaS platform that integrates with your EKS, GKE, or AKS clusters to continuously optimize infrastructure. It replaces/augments cluster autoscaling, rebalances nodes, selects spot instances with graceful fallbacks, and right-sizes workloads.

Why it matters:

• If your top priority is immediate, measurable savings with minimal manual effort, CAST AI’s automation reduces the heavy lifting while protecting reliability.

How it works:

• Agent-based integration: A lightweight agent connects your cluster to CAST AI’s control plane.
• Real-time decisions: It evaluates your workload and constraints, then provisions/terminates nodes, chooses instance types, and orchestrates spot diversity.
• Recommendations and policies: You can preview rightsizing and scheduling recommendations or let CAST AI apply them automatically based on policies.

Typical capabilities:

• Node autoscaling with bin packing.
• Multi-AZ and spot diversification with fallbacks.
• Instance type selection and rebalancing to cut waste.
• Rightsizing recommendations for requests/limits.
• Policy-based automation with safety checks.
• Integrations for logging/alerts.

Deployment model:

• SaaS control plane + in-cluster agent. You configure credentials, policies, and guardrails in CAST AI.

Pros:

• Rapid savings without deep in-house algorithm work.
• Handles complex spot strategies and bin packing.
• Works across major managed Kubernetes providers.

Cons:

• Requires trust in a SaaS control loop and external optimization.
• Less granular “accounting” detail than dedicated allocation tools (though it integrates with them).
• Optimization outcomes depend on good workload health signals (readiness, PDBs, retry logic).

Best for:

• Teams prioritizing savings via automation, especially on single-cloud EKS/GKE/AKS.
• Organizations wanting infra-level optimization with minimal manual tuning.

Learn more: CAST AI website and docs.

Kubecost: Open-Source Cost Visibility and Allocation

What it is:

• Kubecost (and the OpenCost specification) provides in-cluster cost visibility. It attributes costs to namespaces, workloads, labels, and more using Prometheus metrics and cloud pricing/billing data. It includes savings recommendations and enterprise reporting.

Why it matters:

• If you need transparent cost allocation across teams and products—and you want it self-hosted—Kubecost is a strong fit.

How it works:

• Deploys as a Helm chart in your cluster.
• Scrapes usage metrics (via Prometheus) and associates them with prices.
• Allocates shared and idle costs according to configurable policies.
• Exposes dashboards, APIs, and PromQL metrics for reporting and alerting.

Typical capabilities:

• Cost allocation by label/namespace/deployment; multi-cluster aggregation.
• Cloud billing integration for accurate pricing.
• Savings opportunities: rightsizing, overprovisioning, unused PVs, etc.
• Budgets and alerts per team or product.
• Export to BI tools and FinOps workflows.

Deployment model:

• In-cluster; open-source core with paid enterprise features.

Pros:

• Transparent, self-hosted cost data tied directly to K8s objects.
• Flexible label-based allocation for showback/chargeback.
• Works across cloud/on-prem; leverages Prometheus.

Cons:

• Doesn’t automate infra optimization; you apply recommendations yourself or via separate tools.
• Needs metrics hygiene, label discipline, and initial setup time.
• Accuracy depends on correct provider integrations and high-quality usage data.

Best for:

• Organizations seeking detailed, auditable cost allocation and FinOps reporting.
• Platform teams building internal dashboards and alerts for engineering.

Learn more: Kubecost website and docs.

Sealos: Platform-First Governance and Developer Self-Service

What it is:

• Sealos (sealos.io) is an open-source cloud operating system built on Kubernetes. It aims to simplify multi-tenant cluster operations with a developer-friendly experience, app marketplace, and guardrails. In a cost context, Sealos provides the platform layer where you enforce quotas, policies, and self-service, and you can integrate cost tools like Kubecost.

Why it matters:

• If you’re building an internal developer platform (IDP) or multi-tenant environment, platform-level features have as much impact on cost as point optimizations. Quotas, namespaces, and standardization prevent waste at the source.

How it works:

• Sealos manages Kubernetes clusters and provides a developer portal and app marketplace.
• You standardize tenants (e.g., namespaces/projects), apply quotas/limits, and offer curated apps (like observability and cost tools).
• With clear tenancy and policy guardrails, cost allocation is cleaner and waste is reduced.

Typical capabilities relevant to cost:

• Multi-tenancy with namespaces/projects and resource quotas/limits.
• App marketplace to deploy tools including cost visibility and observability stacks.
• Policy and governance primitives that complement FinOps.
• Integration-friendly environment to connect CAST AI or Kubecost.

Deployment model:

• Self-managed open-source platform; also available as Sealos Cloud.

Pros:

• Enables cost-aware self-service with quotas and governance.
• Simplifies deploying and operating cost tooling for teams.
• Reduces chaos and variance that often lead to cost sprawl.

Cons:

• Not a dedicated cost-analysis engine on its own; best when paired with tools like Kubecost for reporting and (optionally) CAST AI for optimization.
• Requires platform engineering maturity to get the most value.

Best for:

• Organizations building a multi-tenant platform with strong governance and self-service.
• Teams who want cost tooling integrated into a broader developer experience.

Learn more: Sealos website and docs: https://sealos.io

Side-by-Side Comparison

They aren’t mutually exclusive. Many teams run Kubecost for visibility, CAST AI for optimization, and use a platform like Sealos to standardize how teams consume the cluster.

Practical Scenarios and Recommendations

1. Startup on a single cloud, spending climbing fast:

• Priorities: immediate savings, minimal engineering time.
• Recommendation: Start with CAST AI to get autoscaling/bin packing/spot savings quickly. Add Kubecost later if you need granular showback.

2. Mid-size platform team, multiple product teams:

• Priorities: transparency, budgets, accountability.
• Recommendation: Deploy Kubecost for allocation and alerting. Establish label/namespace standards. Optionally pilot CAST AI on non-critical workloads.

3. Enterprise building an internal developer platform:

• Priorities: self-service with guardrails, cost-aware provisioning, governance at scale.
• Recommendation: Use Sealos as the platform layer to enforce quotas and standardize tenancy. Install Kubecost from the app marketplace for cost visibility. Integrate CAST AI for infra automation where safe.

Implementation Examples

Installing Kubecost with Helm

Kubecost’s Helm chart makes it easy to start. This example sets the cloud provider and creates a dedicated namespace.

helm repo add kubecost https://kubecost.github.io/cost-analyzer/
helm repo update
 
cat > values-kubecost.yaml <<'EOF'
kubecostProductConfigs:
  cloudProvider: AWS           # or GCP, Azure, or custom
  # Optionally point to billing exports for higher accuracy
  # awsBillingDataSource: "cur"
  # gcpBigQueryBillingTable: "project.dataset.table"
prometheus:
  kubeStateMetrics:
    enabled: true
  nodeExporter:
    enabled: true
serviceAccount:
  create: true
EOF
 
helm upgrade --install kubecost kubecost/cost-analyzer \
  --namespace kubecost --create-namespace \
  -f values-kubecost.yaml

Once running, expose the UI (e.g., via port-forward or an Ingress) and start validating allocations by namespace and label. Ensure your labeling strategy reflects teams and products.

Enforcing Resource Quotas (works on any Kubernetes, including Sealos)

Quotas are a powerful way to control costs. They prevent runaway consumption and align teams with budgets.

apiVersion: v1
kind: ResourceQuota
metadata:
  name: team-a-quota
  namespace: team-a
spec:
  hard:
    requests.cpu: '20' # total CPU requested
    requests.memory: 40Gi
    limits.cpu: '40'
    limits.memory: 80Gi
    persistentvolumeclaims: '10'
    requests.storage: 2Ti

Combine quotas with LimitRanges to enforce per-pod defaults:

apiVersion: v1
kind: LimitRange
metadata:
  name: team-a-defaults
  namespace: team-a
spec:
  limits:
    - type: Container
      default:
        cpu: '1'
        memory: 1Gi
      defaultRequest:
        cpu: '250m'
        memory: 256Mi

On Sealos, you can make these part of a tenant template so every new project starts with sane defaults.

Label Hygiene for Allocation

Create a small policy to enforce labels that map to cost centers.

apiVersion: v1
kind: Namespace
metadata:
  name: team-a
  labels:
    cost_center: 'payments'
    owner: 'team-a'
    env: 'prod'

You can also apply labels to workloads:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: api
  namespace: team-a
  labels:
    app: api
    cost_center: payments
spec: ...

Kubecost will roll up costs by these labels, enabling showback/chargeback.

PromQL: View Cost Rate by Namespace in Kubecost

Kubecost exposes metrics you can query in Prometheus/Grafana. A common view is cost rate by namespace:

sum(rate(kubecost_cost{type="allocated"}[1h])) by (namespace)

Use this in a Grafana panel to monitor which teams are consuming the most cost over time.

Installing CAST AI Agent via Helm

Check CAST AI docs for exact parameters. This pattern illustrates how to connect your cluster:

helm repo add castai https://castai.github.io/helm-charts
helm repo update
 
helm upgrade --install castai-agent castai/agent \
  --namespace castai-agent --create-namespace \
  --set apiKey=$CASTAI_API_KEY \
  --set clusterID=$CASTAI_CLUSTER_ID

After connecting, configure policies in the CAST AI console—for example, enable spot with fallbacks and set disruption windows.

Safer Spot Adoption Checklist (applies to CAST AI or DIY)

Spot/preemptible instances can slash costs, but only if your workloads are resilient:

• Ensure Deployment replicas > 1 for stateless services.
• Set PodDisruptionBudgets (PDBs) to avoid thundering herds.
• Use readiness/liveness probes for quick recovery.
• Mark temporary pods safe to evict (e.g., add the “cluster-autoscaler.kubernetes.io/safe-to-evict”: "true" annotation where appropriate).
• For data workloads, use StatefulSets with proper anti-affinity and plan for node churn.

Rightsizing Workloads

Too-large requests bloat node counts; too-small requests throttle performance. Start with conservative defaults, then adjust using recommendations from Kubecost (visibility) or CAST AI (recommendations/automation).

Example deployment with right-sized resources and HPA:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: checkout
  namespace: team-a
spec:
  replicas: 3
  selector:
    matchLabels:
      app: checkout
  template:
    metadata:
      labels:
        app: checkout
    spec:
      containers:
        - name: checkout
          image: ghcr.io/example/checkout:1.2.3
          resources:
            requests:
              cpu: '300m'
              memory: '512Mi'
            limits:
              cpu: '600m'
              memory: '1Gi'
          ports:
            - containerPort: 8080
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: checkout-hpa
  namespace: team-a
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: checkout
  minReplicas: 3
  maxReplicas: 10
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70

Combine HPA with node-level optimization (CAST AI) and visibility (Kubecost) for balanced cost/perf.

Governance, Security, and FinOps Alignment

• Data residency and control:

  • Kubecost runs in-cluster; cost data stays within your environment by default.
  • CAST AI uses a SaaS control plane; ensure IAM, network, and data-sharing policies meet your requirements.
  • Sealos is self-hosted; you control the platform and can integrate your SSO, policies, and network controls.

• FinOps workflows:

  • Create budgets by team and set alert thresholds (e.g., 80%, 100%, 120%).
  • Review savings opportunities weekly with engineering leads.
  • Hold monthly showback reports with finance, using Kubecost exports.
  • Track realized savings from CAST AI optimizations and include them in FinOps KPIs.

• Developer experience:

  • Use Sealos to present a curated, self-service portal where teams request namespaces/projects with pre-applied quotas and policies.
  • Provide default dashboards and cost bookmarks per team.
  • Document label standards and resource sizing guidelines.

Total Cost of Ownership (TCO) and ROI

• Kubecost:

  • Low software cost to start (open source).
  • Engineering time to deploy, integrate billing, and curate dashboards.
  • Big ROI in accountability and waste detection; empowers continuous improvement.

• CAST AI:

  • Subscription cost often tied to realized savings.
  • Minimal engineering time; automated savings can be immediate and material.
  • ROI strong when workloads are steady-state or eligible for spot/preemptible.

• Sealos:

  • Platform investment that pays back by reducing chaos and enabling cost-aware self-service.
  • ROI grows with scale as governance prevents waste before it starts.
  • Ideal foundation for standardizing and scaling cost practices organization-wide.

Decision Framework: Which Should You Choose?

Ask these questions:

• What’s my primary pain today: lack of visibility, rising costs, or platform sprawl?

  • Visibility → Start with Kubecost.
  • Rising costs with limited time → Add CAST AI.
  • Platform sprawl and multi-tenant governance → Adopt Sealos as the platform backbone.

• How much automation am I comfortable with right now?

  • High: CAST AI for infra optimization, with guardrails.
  • Moderate: Use Kubecost insights and apply changes via GitOps.
  • Platform-level: Enforce quotas and policies in Sealos.

• Do I need self-hosted control?

  • Yes: Kubecost + Sealos are fully self-managed.
  • Mixed: Combine self-hosted visibility (Kubecost) with selective SaaS optimization (CAST AI).

• Will I run multiple clusters/clouds?

  • All three support multi-cluster; Kubecost consolidates reporting, CAST AI optimizes provider-specific clusters, Sealos standardizes the developer experience across environments.

In many organizations, the winning move is “and,” not “or”:

• Use Sealos to standardize tenancy, quotas, and developer workflows.
• Deploy Kubecost for transparent allocation and FinOps reporting.
• Layer CAST AI for automated, continuous optimization where it’s safe.

Putting It Together: A Reference Architecture

• Platform layer (Sealos):

  • Multi-tenant projects/namespaces with quotas and LimitRanges.
  • App marketplace entries for Kubecost, Prometheus/Grafana, logging, and security tools.
  • SSO and RBAC integration to map teams to namespaces.

• Visibility layer (Kubecost):

  • In-cluster install across all production clusters.
  • Billing integrations for precise pricing.
  • Standard dashboards per team; budgets and alerts.

• Optimization layer (CAST AI):

  • Connect production clusters with policies:
    • Enable spot for stateless workloads with fallback.
    • Set disruption windows for rebalancing.
    • Apply bin packing and instance right-sizing.
  • Track realized savings; feed results into monthly FinOps reviews.

This model balances developer autonomy with financial control and operational safety.

Conclusion

Kubernetes cost management is not a single tool—it’s a practice that spans visibility, optimization, and governance.

• CAST AI excels at automated infrastructure optimization. It delivers fast savings by handling autoscaling, bin packing, and spot diversification so your team doesn’t have to.
• Kubecost provides detailed, self-hosted cost visibility and allocation. It’s the FinOps backbone for showback/chargeback, budgets, and alerts that align engineering and finance.
• Sealos offers a platform-first approach. By standardizing tenancy, quotas, and self-service, it prevents waste at the source and makes it easy to deploy and operate cost tools across teams.

You don’t have to choose just one. Many high-performing teams combine them: Sealos for governance and developer experience, Kubecost for transparent accounting, and CAST AI for continuous optimization. Start where your pain is greatest today—then iterate toward a balanced, cost-aware Kubernetes platform that scales with your business.