“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
HPA treats every pod the same and has no knowledge of the runtime or language inside the container.
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
HPA only skips pods that are not ready or still starting when it computes utilization; it 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
Analyzes each node group and the workloads on it, then suggests node policy changes to requirements, capacity type, zones, limits and disruption settings.
Node group consolidation opportunities
Observability
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.
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.
Savings Plan, RI and CUD awareness
Cost attribution
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.
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.
Flexible instance shapes in node groups
Cluster autoscaling
Node policies select instances by category, family, size and capacity type, so one policy spans many shapes.
Spot-to-spot consolidation
Cluster autoscaling
Installs Karpenter with spot-to-spot consolidation turned on for AWS, Azure, GCP and OCI, with a per-cluster toggle in Karpenter settings.
Manage existing Karpenter
Cluster autoscaling
Mirrors existing Karpenter NodePools as read-only cluster-managed policies, and a migration wizard moves them under DevZero management.
Static capacity
Cluster autoscaling
Supports fixed-size NodePools through spec.replicas on AWS, Azure and GCP once you turn on the Static capacity setting. OCI is not supported.
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
HPA records scaling events and status conditions but sends no alerts on restarts, OOM kills or throttling.
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
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.
Horizontal pod autoscaling
Workload autoscaling
Built into Kubernetes as the autoscaling/v2 API. Scales replicas on Resource, ContainerResource, custom, object and external metrics, with behavior policies and a stabilization window.
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.
Coexists with HPA, VPA and KEDA
Workload autoscaling
Runs alongside VPA only when they act on different metrics; both on CPU or memory conflict. KEDA creates and drives an HPA object itself.
Detects existing autoscalers automatically. An actuating VPA stands DevZero down. With an HPA it skips replica scaling, keeps vertical sizing, and offers an optional clamp against triggering scale-out.
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.
API-performance-based autoscaling
Workload autoscaling
Scales on requests per second or latency only if an adapter exposes them through the custom or external metrics API. HPA does not collect them itself.
RED metrics are collected for visibility, but no workload rule scales on them.
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.
Savings plan aware node choice
Cluster autoscaling
Savings estimates flag the share of billed node hours under a reservation or savings plan. It does not buy commitments or steer nodes onto them.
Workload profile-based node scaling
Cluster autoscaling
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
HPA is free; DevZero is billed per CPU with a free plan.
Freeopen source
Free to enable; the bill is the capacity it scales into.
$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 reads your HPAs, sizes their requests, and leaves replicas alone.
Install the operator. It reads each HPA as it is and never edits it.
Each recommendation on an HPA workload shows projected utilization and replicas.
Pick per policy: size normally, clamp to the HPA target, or skip.
Use a DevZero horizontal rule only on workloads without their own HPA.
HPA changes how many replicas run. DevZero changes how much CPU and memory each replica requests. For a workload with its own HPA, DevZero leaves replica counts to it.
No. DevZero reads your HPAs as they are and does not edit them. It sizes requests and proposes no replica changes for those workloads.
It can shift it, since lower requests raise utilization. DevZero shows projected utilization and replicas first. A policy can size normally (default), clamp to the HPA target with 20% headroom, or skip.
HPA can on object and external metrics, beta since Kubernetes 1.37, but not on CPU or memory. DevZero's KEDA template accepts a minimum of 0 and an idle replica count.
HPA is free and built in; you pay for the pods and nodes it runs. DevZero Free is $0 for monitoring, and Pro is $5 per CPU per month.
“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.