Up to 84% less cloud waste. A drop-in replacement for HPA that right-sizes your workloads in real time.
Kubernetes HPA waits until thresholds are crossed before scaling, causing latency spikes — or you over-provision and bleed money.
Most autoscalers react: watch a metric, cross a line, add a pod, wait, repeat. WayScaler computes the right capacity directly — capacity = demand / target_utilization — in one step, every tick. No doubling, no guessing, no cooldown-induced lag. The result is fewer pods, lower cost, and zero breaches.
Enterprise-grade Kubernetes operator with the algorithm intelligence to back it up.
Detects sudden demand surges and responds instantly — keeping latency steady and your SLAs intact when traffic jumps.
Learns your workload's shape automatically and adjusts as it changes. No thresholds to set, no knobs to babysit.
Live dollar savings tracked per workload. See exactly how much waste the adaptive algorithm eliminates vs static provisioning.
Optional standby capacity covers the window while new pods come online — so sudden bursts never wait on cold starts.
Single Helm chart. One CRD. No sidecars, no service mesh dependency. Works with any Kubernetes 1.25+ cluster in under 5 minutes.
Prometheus metrics and a preconfigured Grafana dashboard ship with the chart. See replicas, utilization, savings, and scaling events live — no glue code to wire up.
Replace your HPA with a single WayScaler resource. The operator handles the rest.
helm install wayscaler ./helm/wayscaler
apiVersion: autoscaling.wayscaler.com/v1
kind: WayScaler
metadata:
name: my-app-scaler
spec:
target:
apiVersion: apps/v1
kind: Deployment
name: my-app
metrics:
source: cpu
targetUtilization: 0.72
behavior:
minReplicas: 2
maxReplicas: 20
mode: cpu$ kubectl get wayscalers
NAME REPLICAS DESIRED UTIL SAVINGS PHASE
my-app-scaler 4 4 0.68 $127.40 ActiveStatic provisioning wastes 40-60% of your compute budget. WayScaler reclaims it.
We're accepting a limited number of teams for our free beta program. Get early access to the adaptive scaling algorithm and direct support from the engineering team.