Intelligent adaptive autoscaling

Kubernetes autoscaling
that knows what you need.

Up to 84% less cloud waste. A drop-in replacement for HPA that right-sizes your workloads in real time.

84% avg waste reduction
26 workloads validated
<2s reaction time
The problem

HPA is reactive by design

Kubernetes HPA waits until thresholds are crossed before scaling, causing latency spikes — or you over-provision and bleed money.

Static HPA

  • Reactive — scales after latency hits
  • Fixed thresholds miss traffic patterns
  • Over-provisions to avoid incidents
  • No cost awareness
  • Manual threshold tuning

WayScaler

  • Adaptive — scales ahead of demand
  • Learns your workload patterns automatically
  • Right-sizes in real time
  • Built-in savings tracking ($)
  • Self-tuning — no knobs to babysit
Features

Built for production

Enterprise-grade Kubernetes operator with the algorithm intelligence to back it up.

Spike Protection

Detects sudden demand surges and responds instantly — keeping latency steady and your SLAs intact when traffic jumps.

Self-Tuning

Learns your workload's shape automatically and adjusts as it changes. No thresholds to set, no knobs to babysit.

Real-Time Savings

Live dollar savings tracked per workload. See exactly how much waste the adaptive algorithm eliminates vs static provisioning.

Warm Standby

Optional standby capacity covers the window while new pods come online — so sudden bursts never wait on cold starts.

Drop-In Install

Single Helm chart. One CRD. No sidecars, no service mesh dependency. Works with any Kubernetes 1.25+ cluster in under 5 minutes.

Observable by Default

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.

How it works

Three steps to adaptive

Replace your HPA with a single AdaptiveScaler resource. The operator handles the rest.

1

Install the operator

helm install adaptive-scaler oci://registry.adaptivescaler.io/chart
2

Define your scaler

apiVersion: autoscaling.adaptivescaler.io/v1
kind: AdaptiveScaler
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
3

Watch it adapt

$ kubectl get adaptivescalers
NAME            REPLICAS  DESIRED  UTIL   SAVINGS   PHASE
my-app-scaler   4         4        0.68   $127.40   Active
Savings

The math is simple

Static provisioning wastes 40-60% of your compute budget. WayScaler reclaims it.

Per Workload
~$0.05/hr per idle pod eliminated
10 over-provisioned pods = $365/mo saved
Cluster
40–60% typical compute waste reclaimed
$10k/mo cluster = $4–6k/mo saved

Join the beta

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.

Free during beta. No credit card required. We'll reach out within a few days.