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Comparing HPA and KEDA: Choosing the Right Tool for Kubernetes Autoscaling

In Kubernetes, autoscaling is key to ensuring application performance while managing infrastructure costs. Two powerful tools that help achieve this are the Horizontal Pod Autoscaler (HPA) and Kubernetes Event-Driven Autoscaling (KEDA). While they share the goal of scaling workloads, their approaches and capabilities are actually very different and distinct.

In this introductory blog, we will provide a bird's eye view of how they compare, and when you might choose one over the other.

HPA vs KEDA

Horizontal Pod Autoscaler (HPA)

Let's first start with HPA since it is natively available in Kubernetes for horizontal autoscaling. HPA works by automatically adjusting the number of pod replicas in a deployment or stateful set based on observed CPU and memory usage.

Note

Other metrics outside observed CPU and Memory usage can be used if they are configured. This requires integration with a metrics for non-default metrics.

How HPA Works

Strengths of HPA

  • Native to Kubernetes and widely supported
  • Simple to configure for basic scaling scenarios
  • Supports scaling on CPU, memory, and custom metrics

Limitations

  • Requires integration with a metrics server for non-default metrics
  • Challenging to scale on event-driven or external sources like message queues
  • Cannot scale to zero (always maintains at least one replica)
  • Cannot be used with DaemonSets (Unlike Deployments and StatefulSets, they can’t be used with HPA because they can’t be assigned a replica count)
  • Depends on appropriate Pod resource requests and limits (HPA uses your requests and limits to determine which scaling changes to make)
  • Only considers Pod-level resource utilization

Info

Read this page for an illustrative example/walkthrough of HPA

Kubernetes Event-Driven Autoscaling (KEDA)

KEDA extends the capabilities of HPA by introducing event-driven scaling. It acts as a metrics adapter that feeds external metrics into the HPA. KEDA also supports scaling from zero.

Key Benefits of KEDA

  • Supports over 50 built-in scalers (AWS SQS, Kafka, Prometheus, etc.)
  • Scales to and from zero, saving resources during idle times
  • Easy integration with cloud-native and open-source event sources
  • Supports cron-based and custom metric triggers

Core Components

Component Description
ScaledObject Defines how a deployment should scale based on triggers
ScaledJob Enables autoscaling of batch jobs
TriggerAuthentication Enables integration with external systems

The image below illustrates how KEDA integrates with various external metric backends (like Prometheus, Kafka, Datadog, etc.) to trigger Kubernetes Horizontal Pod Autoscaler (HPA) based on custom event-driven metrics. KEDA’s components (Operator, Scaled Object, and Metrics Server) work together to scale application deployments in or out dynamically based on workload demand.

How KEDA Works

Comparing HPA vs KEDA

Feature HPA KEDA
Native to Kubernetes Yes No (but integrates tightly)
Metric support CPU, memory, custom metrics 50+ external and custom triggers
Ease of setup Simple for basic use cases Easy with built-in scalers
Scale to zero No Yes
Event-driven scaling Limited Native and extensive
Authentication support Manual integration Native support for AWS, Azure, etc.
Cron-based scaling No Yes

Practical Use Cases

At this point, you are probably wondering when to use what.

Use HPA when:

  • Your workload primarily scales based on CPU or memory usage
  • You need a simple and quick autoscaling setup
  • Scaling to zero isn't required

Use KEDA when:

  • You need to scale based on external events (e.g., message queues, streams)
  • You want to optimize cost by scaling idle apps to zero
  • You require cron-based or custom scaling logic
  • You work with cloud-native services like AWS SQS, Kafka, or Azure Queues

Conclusion

HPA is a reliable starting point for Kubernetes autoscaling, especially for straightforward CPU and memory-based scenarios. But for teams operating dynamic, event-driven, or cost-sensitive workloads, KEDA offers a more flexible and modern approach. By enhancing HPA with support for external triggers, scale-to-zero, and simplified integrations, KEDA empowers platform and DevOps teams to build smarter autoscaling strategies for cloud-native applications.

Ultimately, many organizations benefit from using both: HPA for traditional autoscaling, and KEDA for specialized or event-driven workloads. As Kubernetes ecosystems evolve, combining these tools can unlock truly adaptive and efficient infrastructure scaling.