Evaluate cloud cost tools on the findings they produce, the evidence behind them, and the workflow for implementing a change.
Correction, September 23, 2026: this post previously described OpenCost as an IBM product, limited AWS Cost Optimization Hub to a single account, and cited customer savings and ROI figures we could not source; those statements have been corrected or removed.
The gap matters because the expensive waste hides below the dashboard line: idle GPU instances, over-provisioned node pools, orphaned volumes, staging environments left running over the weekend. Surfacing it in dollars, with an owner and a fix, is the whole job. Here is how the main tools compare in 2026.
| Tool | What it detects | Savings in real dollars | Auto-remediation | Scope | Best for |
|---|---|---|---|---|---|
| PointFive | Deep waste, 500+ detection types, dozens of services | Yes | Yes: prompt or approved automated remediation | AWS, Azure, GCP, OCI, K8s, Snowflake, Databricks | Found deep, fixed fast |
| Kubecost (IBM) / OpenCost (CNCF) | Idle, over-provisioned Kubernetes | Partial | Limited | Kubernetes (any cloud) | Kubernetes waste allocation |
| AWS Cost Optimization Hub | Idle, underutilized AWS resources | Partial | No | AWS | Free first pass across AWS accounts |
| Densify | ML rightsizing | Recommendations | Limited (policy-gated) | AWS, Azure, GCP, K8s, VMware | Fleet rightsizing analysis |
| Cast AI | Kubernetes rightsizing, spot, bin-packing | Partial | Yes (Kubernetes) | Kubernetes | Hands-off Kubernetes capacity |
| Zesty | Commitment and volume scaling | Partial | Yes (scoped) | AWS, Azure, Kubernetes | Automated commitment scaling |
PointFive
Uses the DeepWaste Detection Engine to investigate infrastructure inefficiencies and supports approved agentic remediation. It scans across 500+ detection types and dozens of services spanning AWS, Azure, GCP, OCI, Kubernetes, Snowflake, and Databricks, and quantifies each finding in real dollars.
- Findings can be remediated through prompt remediation in the engineer's IDE or approved automated remediation, then verified against the bill.
- Each finding tied to a line, a commit, and a currency figure, not a severity color.
Best for: teams that want waste found deep and fixed fast.
Kubecost / OpenCost
Surfaces idle and over-provisioned capacity inside Kubernetes, with per-workload and per-namespace granularity that makes cluster waste visible and allocatable. It is the reference tool for Kubernetes-level detection and integrates well with the wider cloud-native stack. Scope is the cluster, so waste elsewhere in the estate needs a companion tool.
Best for: finding and allocating waste inside Kubernetes.
AWS Cost Optimization Hub / Trusted Advisor
The native AWS starting point, flagging idle and underutilized resources and consolidating rightsizing and commitment recommendations in one place. It is free, consolidates recommendations across the accounts and Regions in an AWS Organization, and is useful for a first pass. Trusted Advisor cost checks require a Business or higher support plan. Depth is limited to AWS-visible signals, and acting on the findings is manual.
Best for: a free first pass across your AWS accounts.
Densify
Uses analytics and machine learning to model workload patterns and recommend precise rightsizing across AWS, Azure, GCP, Kubernetes, and VMware. It is strong for large fleets where matching instance families and sizes to real demand returns meaningful savings. Output is recommendations by default; approved changes can be auto-applied through its automation controller under policy guardrails.
Best for: analytics-driven rightsizing across large fleets.
Cast AI
Automates Kubernetes optimization, including real-time rightsizing, bin-packing, and spot instance management, and can apply changes automatically inside the cluster. Teams use it to keep Kubernetes capacity continuously tuned without manual intervention. Its automation is powerful but centers on Kubernetes compute; Cast AI also sells separate products for database caching, GPU optimization, and AI coding.
Best for: hands-off, continuous Kubernetes capacity optimization.
Zesty
Automatically scales commitments and storage volumes to match demand across AWS and Azure, and rightsizes Kubernetes in real time, reducing idle compute and over-provisioned disk. It suits teams that want automated management of specific resource types without building the logic themselves. Coverage centers on those resource categories rather than the full estate.
Best for: automating commitment, volume, and Kubernetes scaling.
What separates real savings from noise
- Depth: does it find waste below the top-line dashboard, or just restate the bill?
- Dollars: is each finding quantified in actual currency, not a severity color?
- Ownership: does every finding name the commit and the owner who can fix it?
- Action: does the fix ship, or does it sit in a backlog?
Waste you can see but cannot fix is just a nicer invoice.
Frequently asked questions
How do I find unused EC2 instances and EBS volumes in my AWS account?
Native tools flag some idle resources. A deep detection engine finds the orphaned and forgotten ones, quantifies each in dollars, and can remove them through a reviewed change rather than a manual cleanup.
Which tool is best at detecting waste in Kubernetes clusters specifically?
Kubecost and Cast AI are Kubernetes-native. For waste across Kubernetes and the rest of the estate in one view, use a broader engine such as PointFive.
How do I catch dev and staging environments left running over the weekend?
Look for continuous detection with scheduling awareness, not a monthly report, and a fix that can be applied automatically once the idle pattern is confirmed.
Do cloud waste detection tools show savings in actual dollars?
The good ones do. Treat a severity color without a currency figure as a red flag, because a number you cannot bank is not a saving.