How we compare

Every vendor comparison page eventually turns into marketing. This one tries not to — we tell you exactly what each competitor does well and exactly where we think we're different. If we're wrong about anything, open an issue.

The matrix

	KubeHero	Flexera / Cloudability	Kubecost	OpenCost	CAST AI	PerfectScale	Grafana + Prom only
Attribution accuracy	eBPF 1s resolution	Billing records, 24h stale	cadvisor 5-min avg	cadvisor 5-min avg	proprietary	proprietary	DIY
GPU / TPU first-class	✓ DCGM + MIG + TPU API	Partial	Limited	No	No	No	DIY
Retroactive Savings Plan replay	✓	No	No	No	N/A	N/A	No
Multi-cloud normalization	AWS + GCP + Azure, single schema	Strong	Limited	Limited	AWS-bias	Limited	DIY
Policy enforcement CRDs	BudgetPolicy / CeilingPolicy / RightsizingPolicy	Alerts only	No	No	Proprietary	Proprietary	No
humanArm default	✓	N/A	N/A	N/A	Varies	Varies	N/A
Reversible within cooldown	✓ 10-min default	N/A	N/A	N/A	Partial	Partial	N/A
Open-source components	Apache agent + CLI + proto + cost-model	Closed	Core open, adv. features paid	Full CNCF	Closed	Closed	Upstream
Self-host + air-gap	✓ Helm + values.airgap.yaml	Limited	✓	✓	No	No	✓
Audit log export	syslog · webhook · S3 · SIEM	✓	Limited	No	Limited	Limited	DIY
Keyboard-first UX	✓ cmd-K, URL state, actions	No	Limited	N/A	Proprietary	Proprietary	Grafana
Pricing shape	$10/node · first 25 free; self-host free under 100 nodes	Enterprise-seat + platform fee	Free core + Enterprise	Free	% of savings	Enterprise	OSS
Runs on Prometheus stack	✓ ServiceMonitor + PrometheusRule + Grafana ConfigMap	Separate stack	Bundled	Separate	Separate	Separate	Native

Head-to-head, honestly

Flexera / Cloudability

What they get right: Multi-cloud ingest is polished — AWS, Azure, GCP, Oracle, IBM. Enterprise-ready commercial surface. Finance-team-friendly reporting and chargeback workflows.

Where we differ:

• Their Kubernetes attribution runs on billing records + tag-based allocation rules. 24–48h stale by design. Ours is 1-second eBPF-accurate.
• Their GPU visibility is vendor-level (EC2 p4d spend) not pod-level.
• Their policy engine is alerts. No enforcement, no humanArm, no reversible actions.
• Their Savings Plan handling applies forward from the commit date; historical numbers don't restate.

Pick them if: you're a Fortune 500 CFO office needing multi-cloud billing rollup for 20+ accounts and your Kubernetes footprint is small. Pick us if: Kubernetes is >40% of your compute and you want sub-minute accuracy.

Kubecost / Stackwatch

What they get right: Kubernetes-focused from day one. Great allocation model for namespace / label / team attribution. Open core with a credible enterprise offering.

Where we differ:

• Same 5-minute cadvisor averaging ceiling as everyone in that product generation.
• GPU support is there but not first-class. No MIG-slice attribution, no tensor-core utilization.
• No policy enforcement CRDs. Their "Actions" feature is client-side recommendations, not reconciler-driven.
• Savings Plan / CUD handling is billing-based, not replay-based.

Pick them if: you need allocation for a single-cloud shop and are happy with 5-min accuracy. Pick us if: GPUs matter, or you want enforcement teeth.

OpenCost (CNCF sandbox)

What they get right: It's the canonical cost allocation model in the CNCF ecosystem. Free, open, actively maintained. Kubecost's open core is built on it.

Where we differ:

• No UI. It's a library + a server exposing /allocation. You build the product.
• Same cadvisor accuracy as Kubecost.
• No enforcement layer.

Pick them if: you have engineering capacity to build on top. Pick us if: you want a product.

We can ingest OpenCost allocation rules via an importer if you've already built on their label schema — continuity matters.

CAST AI

What they get right: Genuine autoscaling + rightsizing product. Strong on savings delivered. Commercial polish.

Where we differ:

• AWS-first, GCP okay, Azure catch-up.
• Black-box attribution. You trust their number; you can't audit the derivation.
• No CRD surface. Their actions don't survive a CAST AI uninstall — they run in their infrastructure.
• Proprietary policy engine, no humanArm-level safety guarantees exposed.

Pick them if: AWS-only, comfortable with SaaS-owned policy. Pick us if: multi-cloud, or you need every action to live as code in your cluster.

PerfectScale / ScaleOps

What they get right: Rightsizing specifically. Good observation windows, statistical confidence scores.

Where we differ:

• Single-capability. They rightsize well; they don't attribute multi-cloud cost or run budget policies.
• No K8s-native CRD surface for their actions.
• No GPU story.

Pick them if: you just want rightsizing and are happy running multiple FinOps tools. Pick us if: you want one tool.

Grafana + Prometheus alone

What they get right: The actual data plane. Everyone else (including us) runs on top.

Where we differ:

• They're a library, not a product. You need to build attribution, recommendations, and actions yourself. That's a two-year engineering project.

Pick them + us: we use both. KubeHero ships a ServiceMonitor, a PrometheusRule, and three Grafana dashboards so your existing stack is our UI.

The seven moats

Pulled out of the matrix — each is a feature Flexera / Kubecost / OpenCost / CAST AI / PerfectScale structurally cannot ship without rewriting their data plane.

1. eBPF 1s resolution — everyone else averages over minutes
2. Retroactive Savings Plan replay — historical cost restates when a SP kicks in
3. humanArm: true default — enforcement with safety rails, not alerts
4. Multi-cloud cost-per-second normalization — one number across AWS / GCP / Azure SKUs
5. Keyboard-first operator UX — cmd-K, URL state, action buttons everywhere
6. Open-core with a clean split — agent + CLI + proto + cost-model under Apache 2.0
7. Posture + cost correlation — CVE findings ranked by workload $/day (see Posture)

"What about X?"

We'll add rows to the matrix as people ask. If we missed your tool or you think we're wrong about a row, open an issue.