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Platform · Auto-Scaling

Scale exactly what needs scaling. Nothing else.

AravaliStack's auto-scaling layer goes beyond Kubernetes HPA. KEDA brings event-driven scaling from Kafka queue depth, database row counts, or custom metrics. VPA right-sizes pods without manual tuning. Predictive scaling anticipates demand before it arrives.

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Cost-aware autoscaling — scale out, but not beyond your budget.

Scale workloads to zero when idle. Scale instantly when a Kafka topic has messages, a queue is backing up, or a database table crosses a threshold. 60+ built-in scalers.

Automatically right-size CPU and memory requests based on actual usage. Eliminate the toil of manual resource tuning. VPA observes and recommends — or applies — in real time.

Standard HPA reacts to current load. AravaliStack's predictive layer analyses historical patterns to scale before demand arrives — so your users never see the lag.

AravaliStack's scaling policies are cost-aware. Set a hard cost ceiling per workload or team. The platform scales within that boundary — and alerts you when demand would breach it, rather than silently generating an overage.

  • Event-Driven Scaling

    Scale workloads to zero when idle. Scale instantly when a Kafka topic has messages, a queue is backing up, or a database table crosses a threshold. 60+ built-in scalers.

  • Vertical Pod Autoscaling

    Automatically right-size CPU and memory requests based on actual usage. Eliminate the toil of manual resource tuning. VPA observes and recommends — or applies — in real time.

  • Predictive Horizontal Scaling

    Standard HPA reacts to current load. AravaliStack's predictive layer analyses historical patterns to scale before demand arrives — so your users never see the lag.

  • Per-namespace cost ceilings enforced at scaling time
  • Alert-before-scale for budget-constrained workloads
  • Scale-to-zero for idle development environments (save 40–60%)
  • Multi-cluster scaling — burst to secondary cluster when primary is full
  • Kafka
  • RabbitMQ
  • AWS SQS
  • Redis
  • Custom metrics
  • CPU right-sizing
  • Memory optimisation
  • Cost reduction
  • Pattern recognition
  • Pre-emptive scaling
  • Cost-aware
Example: KEDA scaler config
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: payment-processor
spec:
  scaleTargetRef:
    name: payment-worker
  minReplicaCount: 0
  maxReplicaCount: 50
  triggers:
  - type: kafka
    metadata:
      topic: payment-events
      lagThreshold: "100"
      consumerGroup: payments

Scale exactly what needs scaling. Own every decision.

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