KEDA 2.12: Expanded Autoscaling and Enhanced Scalers

Introduction

Autoscaling is easy to sell and hard to get right. For event-driven workloads (queues, streams, schedulers), you’re usually scaling on signals that can be spiky, delayed, or misleading—so the difference between “works” and “thrashes” is all in the scaler details.

KEDA 2.12, released on October 10, 2025, expands the scaler catalog and improves the practical fundamentals: evaluation efficiency, safer integration with HPA/external metrics, and better security/credential handling for production event sources.

Why this matters in practice

• More event sources: new scalers reduce the need for custom adapters or brittle metric exporters.
• Lower control-plane overhead: performance work matters in clusters with many ScaledObjects.
• Fewer scaling surprises: better HPA behavior/stabilization helps avoid oscillation under bursty load.

New Scalers

• Redis Streams scaler enables autoscaling based on Redis stream message backlog.
• Apache Pulsar scaler provides autoscaling for Pulsar message queue workloads.
• MongoDB scaler enables autoscaling based on MongoDB collection metrics.
• Custom metrics scaler allows integration with any metrics provider through a flexible API.

Performance Improvements

1. Scaler evaluation optimizations reduce CPU usage during metric collection.
2. Caching enhancements improve response times for frequently accessed metrics.
3. Concurrent scaling improvements enable better handling of multiple scaled objects.
4. Resource usage optimizations reduce memory footprint in large clusters.

Security Enhancements

• Authentication improvements support more authentication methods for scalers.
• Secret management integration with external secret operators provides secure credential handling.
• RBAC refinements provide more granular permissions for KEDA components.
• Audit logging tracks all scaling decisions and metric evaluations for compliance.

HPA Integration

• Metrics API improvements provide better integration with Kubernetes metrics API.
• External metrics support enables scaling based on custom external metrics.
• Behavior configuration allows fine-grained control over scaling behavior.
• Stabilization windows provide smoother scaling decisions with reduced oscillations.

Getting Started

# Install KEDA
kubectl apply -f https://github.com/kedacore/keda/releases/download/v2.12.0/keda-2.12.0.yaml

Summary

KEDA 2.12 continues to be the leading solution for event-driven autoscaling on Kubernetes, enabling efficient resource utilization for diverse workloads.