“We were essentially able to reduce the cost of that cluster by about 75%. On AWS, DevZero demonstrated they could achieve significantly higher savings than we initially thought possible.”

Mihir Nair
Head of Architecture, Databahn
Workload profiling
Observability
Builds a usage profile per workload from collected CPU, memory and runtime data and uses it to drive rightsizing recommendations.
Automatic language detection
Observability
The node agent detects JVM, Node.js, .NET, Go, Rust, GraalVM native image, Python, Ruby, Deno and Bun in every container with no workload changes. Low-privilege installs turn it off.
Network tracing (eBPF)
Observability
Collects pod-to-pod network flows with netfilter by default. An eBPF collector mode is available as an opt-in setting.
Application health profiling
Observability
Karpenter tracks node health for auto repair, behind the NodeRepair feature gate, but does not profile application health.
Tracks OOM kills, crash loops, CPU throttling and container startup lifecycle events for each workload.
Node group profiling for shapes and sizes
Observability
You define allowed instance shapes in NodePool requirements; Karpenter picks from them per launch but does not report which shapes fit best.
Analyzes each node group and the workloads on it, then suggests node policy changes to requirements, capacity type, zones, limits and disruption settings.
Workload cost
Cost attribution
Splits node prices into CPU, memory and GPU unit costs and attributes them to each workload.
Network cost
Cost attribution
Prices network traffic by type, including cross-AZ, cross-region, internet egress and NAT paths, estimated at cloud list rates.
Workload storage cost
Cost attribution
Calculates persistent volume claim storage cost for each workload.
Vertical pod autoscaling
Workload autoscaling
Sizes CPU and memory requests per container and adjusts memory limits with them. Applies changes in place on Kubernetes 1.33+, with a restart-based fallback when in place is unavailable.
Live migration of running pods
Reliability
Checkpoints a running container with CRIU and restores it on the replacement pod with its in-memory state. Opt-in per policy, on labelled nodes in the same cluster.
Checks before a change is applied
Reliability
Live migration checks workload kind, image coherence and node readiness first, and skips the workload without changes if a check fails. Live migration is opt-in per policy and part of Pro.
Alerts on restarts, OOM kills and throttling
Reliability
Karpenter sends no alerts; you build alerts yourself from its Prometheus metrics and Kubernetes events.
Alerts on application restarts, out-of-memory kills and CPU throttling, delivered by email or by webhook to Slack, Microsoft Teams, Discord or your own endpoint.
API latency, throughput and error detection
Observability
Karpenter metrics cover pods, nodes, scheduling, and disruption, with no API latency, throughput, or error data.
Records inbound request rate, errors and latency per container from socket probes in the network monitor. Off by default; enable it with the containerRedMetrics Helm value.
Node group consolidation opportunities
Observability
Emits Unconsolidatable events that explain blocked nodes, such as a PDB preventing eviction, and a karpenter_voluntary_disruption_eligible_nodes Prometheus gauge.
Previews underused nodes whose pods fit elsewhere in their node group and drains them on demand or by policy. Also flags duplicate and overlapping node policies.
Savings Plan, RI and CUD awareness
Cost attribution
The alpha NodeOverlay API, off by default, lets you set price or priceAdjustment per instance type to reflect savings plans. You enter the discounts by hand.
Reads AWS, GCP and Azure billing exports to show commitment coverage and utilization and to price nodes at commitment-adjusted cost. It does not buy or manage commitments.
Horizontal pod autoscaling
Workload autoscaling
Karpenter scales nodes only; pod replica counts come from HPA, KEDA, or another autoscaler.
Horizontal scaling runs through KEDA ScaledObjects rendered from the workload rule. KEDA is an opt-in install, off by default, and DevZero creates no native HPA.
Language-aware autoscaling
Workload autoscaling
Raises the CPU request of detected Java containers to a startup floor, 75m by default, and can tune JVM heap flags when the heap rule is on. No other runtime has its own rule.
Workload profile-based autoscaling
Workload autoscaling
The sizing engine adapts percentile targets and headroom to each workload's observed CPU and memory behavior. No rule triggers scaling from a stored profile.
OOM and CPU throttling handling
Workload autoscaling
Raises memory right after an OOM kill, on by default, with configurable multipliers and cooldowns. A CPU throttling response exists in the operator but ships off.
Spot-to-spot consolidation
Cluster autoscaling
Requires the alpha SpotToSpotConsolidation feature gate, off by default. Single-node replacements need at least 15 cheaper instance type options.
Installs Karpenter with spot-to-spot consolidation turned on for AWS, Azure, GCP and OCI, with a per-cluster toggle in Karpenter settings.
Static capacity
Cluster autoscaling
Setting spec.replicas on a NodePool keeps a fixed node count. It needs the alpha StaticCapacity feature gate, off by default, and cannot revert to dynamic.
Supports fixed-size NodePools through spec.replicas on AWS, Azure and GCP once you turn on the Static capacity setting. OCI is not supported.
Workload profile-based node scaling
Cluster autoscaling
Karpenter sizes nodes from declared pod requests, not from observed workload profiles.
Node policy suggestions read workload profiles, such as spot suitability and startup time, to propose spot capacity and grace periods. You apply them; no rule scales nodes on profiles.
Last updated
Karpenter is free; DevZero is billed per CPU with a free plan.
Freeopen source
Free to install; the bill is capacity and effort.
$5per CPU per month
Billed monthly on consumption. The more you save on compute, the less you pay DevZero.
Start freePublished results from customers who run DevZero in production.
DevZero starts read-only; authoring needs a per-cluster move to dzkarp.
Install the operator. It lists existing NodePools as read-only policies.
Compare each policy with its live YAML. Policies stay disabled.
Run Simulation on a pool to see cost and risk of new constraints.
Install dzkarp, scale the old controller to zero, then enable policies.
No. Node policies compile to Karpenter NodePool and NodeClass objects, and provisioning runs on dzkarp, DevZero's build of Karpenter. DevZero adds authoring, simulation, and monitoring.
Yes. DevZero shows your NodePools as read-only policies. To author and push policies from DevZero, the cluster's controller moves to dzkarp, per cluster.
Only briefly during the move. Two controllers on one cluster contend for the same NodePools, so the migration installs dzkarp, then scales the old Karpenter to zero.
DevZero detects Argo CD, Flux, and Helm ownership and blocks the migration by default. You can acknowledge each conflict and continue, but you must pause the other tool yourself.
Karpenter is free; you pay for nodes and the time to run it. DevZero Free is $0 for monitoring, and Pro is $5 per CPU per month, with no annual commitment.
“We were essentially able to reduce the cost of that cluster by about 75%. On AWS, DevZero demonstrated they could achieve significantly higher savings than we initially thought possible.”

Mihir Nair
Head of Architecture, Databahn
Use your own data
See your cluster costs and recommendations in the dashboard.
Compare side by side
Check DevZero's results against your current tool.
Plan the switch
Move one namespace at a time, and keep the rest where it is.