How to Choose Kubernetes Cost Optimization Software: A Buyer’s Guide

Key takeaways

• The average Kubernetes cluster uses just 8% of the CPU it pays for. Most waste is invisible without dedicated tooling.
• A $100K/month Kubernetes cluster typically spends 50-70% on compute. At 8% CPU utilization, that’s $46K-$64K provisioned for workloads that aren’t using it. Monitoring tools identify the gap. Automation platforms close it.
• Monitoring tools show you spend. Automation platforms act on it. Only automation closes the utilization gap without constant manual intervention.
• In a benchmark across EKS and GKE clusters starting from default configurations, Karpenter plus Cast AI’s open-source Evictor tool delivered 9.1% spend reduction. The full Cast AI optimization stack delivered 43%.
• SLO-aware automation is the critical differentiator. Look for tools that check error rates, latency, and OOM signals before touching production workloads.
• A two-week evaluation is the minimum. You need to capture traffic patterns, batch job cycles, and non-recurring load before committing.

The two categories: monitoring vs automation

Kubernetes cost optimization software splits into two distinct categories: monitoring tools that surface spend data, and automation platforms that act on it continuously.

Monitoring tools

OpenCost and Kubecost are the most common examples. Both allocate cost by namespace, workload, and team. Each surfaces rightsizing recommendations. However, acting on those recommendations is left entirely to the engineer.

That last point is the critical limitation. A dashboard doesn’t reclaim a single core. Every recommendation that sits unreviewed is waste that keeps compounding. For teams with limited bandwidth, the recommendation backlog grows faster than engineers can clear it.

Automation platforms

Platforms like Cast AI operate continuously. They rightsize pods, consolidate nodes, manage Spot interruptions, and optimize GPU scheduling without waiting for a human to review a report. Automation platforms act on every new deployment and every scaling event, not just on a weekly review cadence.

According to the Cast AI 2026 State of Kubernetes Optimization Report, CPU overprovisioning reached 69% of clusters in 2025, up from 40% the year before. The average cluster uses just 8% of the CPU it pays for. At that scale, recommendations alone don’t keep up. For a deeper comparison of how monitoring-only tools stack up against automation, see the Cast AI vs Kubecost breakdown.

Which category fits your situation?

If your cluster spend is under $20K/month, start with OpenCost for visibility. Above $50K/month with manual rightsizing, you are leaving recoverable savings on the table every sprint cycle. Above $200K/month, automation isn’t optional. At that scale, the manual overhead of managing resource requests across hundreds of services exceeds any savings a monitoring tool alone can generate.

Capabilities that matter

Not every capability carries equal weight. Start with this checklist to separate the features that recover real money from the ones that look good in a demo.

Rightsizing safety

Rightsizing is the highest-leverage optimization available. It also carries the most production risk if done carelessly. Ask every vendor how they handle the transition from current resource requests to rightsized values.

The risk isn’t in setting tighter limits. The risk is in setting them wrong. A tool that cuts memory requests without checking historical peak usage will trigger OOM kills. The Cast AI 2026 report shows that automated rightsizing dropped OOM kills from 40-50 per measurement interval to near zero. That result requires checking actual workload signals, not just averages.

Look for tools that analyze percentile-based utilization data over at minimum a 7-day window. Prefer tools that set separate CPU request and limit values rather than collapsing them into one number. Also confirm the tool respects PodDisruptionBudgets during any pod restart that applies new resource settings.

SLO awareness

SLO-aware automation checks workload health before and after any change. Specifically, it monitors error rate, p99 latency, OOM kill rate, and Pressure Stall Information (PSI) metrics. If signals degrade, the platform rolls back automatically.

This capability separates tools you can trust in production from tools you can only safely run in non-prod. For any workload with an uptime commitment, SLO awareness isn’t a nice-to-have. It’s a prerequisite.

GPU cost tracking

GPU workloads deserve special attention. The Cast AI 2026 report puts average GPU utilization at 5% across analyzed clusters. H200 instance prices rose 15% in January 2026, compressing margins for teams running AI workloads on on-demand capacity. Yet under 2% of GPU workloads run on Spot instances (Cast AI 2026 State of Kubernetes Optimization Report). Ask vendors specifically how they detect idle GPU instances and whether they support Spot placement for GPU node pools.

Build vs buy

Building a DIY optimization stack is viable if your team has 3-6 months of senior SRE capacity and ongoing bandwidth to maintain it. Here’s what that trade-off looks like.

What you can build

A typical DIY stack combines Karpenter for node autoscaling, OpenCost or Kubecost for cost allocation, Vertical Pod Autoscaler (VPA) for resource recommendations, and custom scripts to handle Spot interruptions and apply VPA outputs in production.

Kubernetes’ built-in Vertical Pod Autoscaler applies resource changes in Auto mode via pod restart, which causes disruption. Cast AI’s rightsizing uses gradual rollouts instead, respecting PodDisruptionBudgets. VPA is also incompatible with HPA on the same workload when both scale on the same resource metric. Running both simultaneously on the same deployment causes conflicting control signals. Cast AI handles this without HPA conflicts.

Each component covers one layer. Karpenter handles node provisioning. VPA handles pod sizing. OpenCost handles cost visibility. No single component handles all four optimization layers together: pod rightsizing, node bin-packing, Spot automation, and GPU scheduling.

What you miss with DIY

Karpenter alone doesn’t rightsize pods, allocate costs, or provide Spot pool intelligence. It covers nodes only. The benchmark makes this concrete: in a test across EKS and GKE clusters starting from default (unoptimized) configurations, adding Karpenter plus Cast AI’s open-source Evictor tool delivered 9.1% spend reduction. Covering pod rightsizing, node bin-packing, Spot automation, and GPU optimization, the full Cast AI stack delivered 43%. That gap reflects what pod-level rightsizing and multi-layer automation add beyond node scheduling alone.

Integration is also harder than it looks. Connecting VPA recommendations safely to production workloads without SLO monitoring requires custom logic. That logic needs to handle edge cases: stateful workloads, batch jobs, bursty traffic, GPU pods.

The real cost of DIY

Assembling a production-grade DIY stack typically takes 3-6 months of senior SRE time. Ongoing maintenance runs 2-5 hours per week. That includes component upgrades, bug fixes, tuning, and extending coverage as your cluster environment evolves.

Use these criteria to decide. Below $50K/month, DIY rarely pays off within a reasonable timeframe. Teams with dedicated SRE bandwidth who want deep component control: build. Teams that need savings faster than 6 months, or whose workloads span multiple clouds and GPU instances: buy.

Before approving the tooling spend, your CFO will want net ROI. A simple calculation: take your expected monthly savings (20-40% of compute spend for teams starting from defaults), subtract the platform fee, and divide into setup cost. For a $100K/month cluster saving 30%: $30K/month saved. If the tool costs $3K/month, net benefit is $27K/month. Payback on typical onboarding cost comes in under a week. Get the specific cost structure (spend-managed, node-managed, or flat fee) during your vendor call.

Questions to ask vendors

These questions cut through demo polish and reveal how a platform actually behaves in production. Bring this checklist to every vendor evaluation. For a detailed comparison of monitoring and automation platforms, see the best Kubernetes cost optimization tools guide.

Rightsizing safety

• What data window do you use to compute rightsizing recommendations?
• How do you handle workloads with high variance between peak and average utilization?
• What happens during the pod restart that applies new resource settings?
• Do you respect PodDisruptionBudgets and minAvailable settings?

SLO awareness

• What signals do you monitor before and after a change?
• How do you define rollback triggers? Who configures the thresholds?
• Can you pause automation for specific namespaces or workloads during incidents?

Multi-cloud

• Which cloud providers do you support today?
• Is multi-cloud cost visibility unified into a single view or split across dashboards?
• Do optimization policies work consistently across EKS, GKE, and AKS?

Spot and commitment management

Spot instance management:

• How do you select Spot instance types? Do you use interruption rate data?
• What is your fallback behavior when Spot capacity disappears?

Commitment purchasing:

• Do you provide Reserved Instance or Savings Plan coverage analysis and purchase recommendations?

GPU

• Do you track per-GPU utilization, not just GPU instance cost?
• Can you schedule GPU workloads on Spot GPU instances with safe fallback?
• How do you handle idle GPU detection for batch inference workloads?

Pricing and TCO

• Is pricing based on spend managed, nodes managed, or a flat fee?
• What does the typical ROI timeline look like for a cluster at our scale?
• How long from agent installation to first automated rightsizing action in a typical production cluster?
• Are there charges for API calls, data egress, or additional integrations?

Security and compliance

• What permissions does the agent require in our cluster?
• Do you support private clusters with no outbound internet access?
• What certifications do you hold: SOC 2, ISO 27001, others?

Migration and lock-in

• What does offboarding look like if we switch tools?
• Do you use proprietary node provisioning that requires migration on exit?
• Can we export historical cost data in standard formats?

Cast AI is agent-based and non-destructive. Remove the agent and your cluster keeps running with whatever resource configuration was in place at the time. Your nodes, your workloads, and your cloud account are not locked to Cast AI’s control plane. The vendor question to ask any automation platform is whether the same applies to their agent. If they pause or need to renegotiate before you can exit, that is a lock-in risk.

How to run a two-week evaluation

Two weeks is the minimum evaluation window. One week captures daily traffic patterns. Two weeks captures weekly batch cycles and non-recurring load spikes. Shorter evaluations produce misleading baselines.

Week 1: baseline and discovery

Connect the tool in read-only mode first. Most platforms support a monitoring-only phase that requires no cluster changes. Use this phase to:

• Collect actual CPU and memory utilization across all workloads
• Identify the top 10 cost drivers by namespace and workload
• Map which workloads have PodDisruptionBudgets defined
• Set SLO thresholds for error rate, latency, and OOM kill rate on production workloads
• Document your current cloud billing numbers to use as the comparison baseline

Don’t skip the PDB audit. Automation tools rely on PDBs to know how many pods they can safely restart at once. Missing or misconfigured PDBs are the most common source of availability incidents during initial automation rollout.

Week 2: automation and measurement

Enable automation starting with non-production workloads. Specifically, target dev and staging namespaces where SLO risk is low. Observe the first round of changes before expanding.

Next, expand automation to low-criticality production workloads: background jobs, internal tooling, data pipelines with long retry windows. Compare your cloud billing against the Week 1 baseline at day 10.

Finally, measure reliability alongside cost. Track OOM kill rate, pod restart rate, and any SLO threshold breaches during the automation window. A good platform shows measurable cost reduction without a corresponding uptick in reliability incidents.

Success criteria

• Measurable cost reduction visible in cloud billing within 7-10 days
• OOM kill rate stable or lower than pre-automation baseline
• No SLO degradation on workloads covered by automation
• Recommendation backlog reduced, not growing

In a 90-day structured benchmark using the LeanOps evaluation framework (a Kubernetes cost assessment methodology applied across 5-to-50 node clusters on EKS and GKE), compute spend dropped 45-58%. Validate that your evaluation points toward comparable outcomes at your cluster scale.

Conclusion

A $200K/month cluster is typically spending 50-70% on compute. At 8% CPU utilization, that’s $70K-$140K provisioned for workloads that aren’t using it. Fixing that gap returns real money to the budget. Every month without rightsizing automation is a month of recoverable waste that won’t come back.

Start with a cluster cost analysis. Cast AI surfaces waste within 5 minutes of connecting in read-only mode. Most teams identify their first $20K+ savings opportunity before the first call.

Frequently Asked Questions