Kubernetes has made application deployment and scaling easier — but it’s also made cost optimization much more complicated. While AWS, Azure, and GCP billing tools can tell you what you’re spending in aggregate, they rarely break down costs at the pod, container, or namespace level. 

That’s where Kubernetes cost optimization platforms come in. This guide compares Kubernetes visibility platforms Kubecost, Cast.ai, and nOps head-to-head, breaking down their strengths, limitations, and ideal use cases so you can choose the right solution for your Kubernetes and cloud optimization strategy.

What is Cast.ai?

Cast.ai is a Kubernetes-focused multi-cloud automation and optimization platform that provides automated scaling, rightsizing, and Spot management for Kubernetes clusters across AWS, Google Cloud, and Azure. The platform helps optimize resource allocation, enabling organizations to reduce cloud costs and maintain performance. Cast.ai uses its own default node autoscaler, which cannot be run alongside your existing Karpenter or Cluster Autoscaler setup—meaning you’ll need to replace those if you adopt Cast.ai’s scaling engine. CAST AI exclusively focuses on visibility and optimization of Kubernetes environments across different cloud providers. It does not provide full cost coverage for non-Kubernetes workloads, SaaS metering, or AI/ML infrastructure.

Pricing: Cast.ai employs a usage-based pricing structure, where costs are tied directly to compute consumption—with a fixed base fee plus a per-CPU rate. 

What is Kubecost?

Kubecost is a Kubernetes cost monitoring and visibility tool that provides detailed insights into cluster spend across AWS, Azure, and GCP. It breaks down costs to the namespace, deployment, service, and pod level, helping teams track budgets, allocate costs to business units, and identify inefficiencies. While it offers recommendations for rightsizing and optimization, much of the actioning is manual, and advanced automation features are limited compared to broader cloud optimization platforms. Kubecost can be deployed self-hosted within your clusters or accessed via a managed cloud service, but the self-hosted model can introduce performance overhead.

Pricing: Kubecost’s pricing is typically based on CPU core-hours for managed offerings. Paid tiers unlock features like multi-cluster visibility, advanced governance, and enterprise support, with costs scaling based on the number of CPUs monitored. We wrote this complete breakdown of Kubecost pricing for additional details and analysis.

What is nOps?

nOps is an all-in-one cloud cost optimization platform that unifies Kubernetes cost visibility with all your other multicloud, SaaS and GenAI costs. Within Kubernetes, nOps allocates 100% of your AWS bill down to the container level, factoring in applied credits, amortized commitments, and shared costs. Teams can drill into costs by namespace, workload, deployment, service, or even label, while also tracking budgets, forecasting usage, and detecting anomalies in real time. Rightsizing recommendations can be actioned automatically, and optimizations like Spot adoption or idle resource cleanup run without manual intervention.

Pricing: nOps uses a flat, predictable fee—without CPU-based or usage-based charges—so customers can onboard unlimited clusters and data sources without surprises and at a fraction of the cost of Kubecost or Cast.ai.

Feature Comparison

Let’s briefly compare the features offered by each platform.

Feature

nOps

Kubecost

Cast.ai

EKS COST VISIBILITY

Free EKS benchmarking

✅ Yes — cluster, node, container, EC2 cost breakdown

❌ No

✅ Yes

Budgets & targets

✅ Container-level budgets; real-time alerts

❌ Limited; mostly high-level

❌ Not built-in; focus on optimization savings

COGS tracking

✅ Full support; container-level unit economics

❌ Not supported

❌ Not supported

Forecasting

✅ Any dimension/metric, including namespace/container

❌ Basic only

⚠️ Basic forecast of savings; not full spend forecasting

Anomaly detection

✅ Yes — Clara AI

❌ No true AI detection

⚠️ Basic detection for scaling anomalies

Global dashboards & reports

✅ Prebuilt multi-cloud & EKS dashboards

❌ Requires manual setup

⚠️ Optimization-focused dashboards; limited finance reporting

Integrations

✅ Slack, Jira, Datadog, Snowflake, OpenAI, Azure, GCP, MongoDB, SaaS tools

❌ Slack only; limited via webhook

✅ Slack, Datadog, Prometheus, Grafana, Terraform

Container rightsizing summary & policies

✅ Detailed per-container + automation policies

❌ Basic recommendations

✅ Recommendations + automation policies

Open-Source

❌ Closed-source SaaS platform

✅ Widely adopted open-source project with active contributor base

❌ Closed-source SaaS platform

EKS COST OPTIMIZATION

Deployment architecture

✅ Lightweight, one-line agent; no in-cluster server

❌ Heavy in-cluster server; resource intensive

✅ SaaS-based control plane; lightweight agent in clusters

Setup time

✅ <5 minutes

❌ 30 minutes to hours

✅ ~15–30 minutes

Pricing model

✅ Fixed, predictable; unlimited clusters

❌ CPU-based; unpredictable costs, higher at scale

⚠️ Usage-based: base fee + per-CPU-hour rate

Container rightsizing automation

✅ Fully automated with policies

❌ Manual or semi-automated

✅ Automated with policies

Node rightsizing configuration automation

✅ Automated — granular hourly visibility & trend analysis

❌ Manual only

✅ Automated

Container Rightsizing Reports

✅ Full reporting with actionable insights; automated delivery to email/Slack

❌ Limited reporting

✅ Available with automation; reporting tied to optimization engine

Support for Cluster Autoscaler or Karpenter

✅ Yes

❌ No native automation

⚠️ No, uses its own autoscaling engine

Multicloud Kubernetes support

⚠️ Primarily intended for AWS

✅ Yes

✅ Yes

COST ALLOCATION

Allocation granularity

✅ Pod, container, namespace, label; custom showback & chargeback

❌ Namespace & label only

⚠️ Namespace, label, workload; no deep finance alignment

Showback & chargeback programs

✅ Full customization

❌ Limited

❌ Limited

Custom showback rules

✅ Even, usage-based, or % split

❌ Limited flexibility

❌ Limited

Credits & discounts allocation

✅ Accurate to container/pod; 100% CUR-matched

❌ Less granular

❌ Not supported for pod/container level

Orphaned PVC & workload cleanup

✅ Automated detection & cleanup

✅ Available

✅ Available

COMMITMENT MANAGEMENT

RI/SP/Spot orchestration

✅ Fully automated

❌ Not supported

⚠️ Spot only; no RI/SP management

Commitment tracking (EDP, SaaS)

✅ Full burn-down tracking

❌ Not supported

❌ Not supported

Credit & discount visibility

✅ Accurate to container/pod

❌ Less granular

❌ Not built for finance-level detail

 

The Bottom Line: With nOps you get all the features of Cast.ai and Kubecost + additional value at a fraction of the cost

Pricing that is based on the number of vCPUs monitored can add up fast in production environments with many clusters or high-core workloads. Since the IBM acquisition of Kubecost, some teams have reported even steeper enterprise pricing—raising concerns about affordability at scale.

Kubecost and Cast.ai offers solid visibility into Kubernetes costs, but that’s only part of the picture. If you’re managing multiple cloud providers, running non-containerized workloads, or using AI infrastructure like Amazon Bedrock or Vertex AI, they won’t capture the full scope of your cloud spend. On the other hand, nOps integrates with all of your multicloud, SaaS, and AI spending making it easy to break down, allocate and analyze ALL of your costs in one place.

Why engineering and FinOps teams choose nOps:

  • One contract: No need to patch together multiple vendors or agents.
  • One source of truth: Centralized visibility across AWS and Kubernetes workloads.
  • Unified data model: Cloud, container, and cost data tied together for accurate, actionable insights.
  • Built-in automation: Optimization engines share data—so rightsizing, scheduling, and commitment management compound instead of conflict.

Understand your Kubernetes costs and leverage automation with complete confidence by booking a demo today or try out interactive demo below!