In the world of Kubernetes autoscaling, two popular options often come to mind: KEDA vs HPA. But what sets KEDA apart from the traditional Horizontal Pod Autoscaler (HPA), and how do you decide which one is best for your scaling needs?
Kubernetes is a one-stop solution and a powerful platform for deploying and managing all your containerized applications. Since most companies use this platform due to its performance and potential usage, scalability is also needed.
For many production-grade systems, scalability plays a major and vital role in maintaining the uptime and availability of systems as well as application performance and efficiency.
Among its scaling tools, the Horizontal Pod Autoscaler (HPA) and Kubernetes-based Event Driven Autoscaling (KEDA) are popular options.
But the question is then what’s the difference between both the strategies and which one needs to be used when?
So, this blog explores the differences between HPA and KEDA to help you determine which is better suited for your use case.
In this post, we will analyze HPA and KEDA in detail, contrast their advantages and disadvantages, and examine real-world applications. My objective is to provide you with the information you need to make wise choices from the beginning, so you are aware of when to utilize HPA and when to switch to KEDA.
What is HPA?
There is an inbuilt feature of Kubernetes called the Horizontal Pod Autoscaler (HPA) which dynamically modifies the number of pods in a deployment, replication controller, or replica set in response to the specified metrics like CPU/memory currently in use.
Important HPA Features
- CPU and memory usage are the main metrics used in resource-based scaling.
- Support for Custom Metrics: If integrated with a metrics server such as Prometheus, it is possible to use external or custom metrics.
- Native Integration: Completely integrated with Kubernetes, meaning that configuration is the only setup needed.
- Control Loops: By default, HPA does metrics checks every 15 seconds.
- Predictable Behavior: Fits well with workloads that are consistent and predictable.
Architecture
When to Apply HPA
- when you want to purely scale based on resource usage.
- when you want to use the native tools for scaling in Kubernetes.
- for workloads where traffic patterns are predictable.
What is KEDA?
Kubernetes-based Event Driven Autoscaling (KEDA) as the name suggests is an external tool that can be used and integrated in our Kubernetes cluster to support scaling based on some external metrics or sources. It enables fine-grained scaling based on external event sources, making it ideal for dynamic and bursty workloads.
Important KEDA Features
- Event-driven Scaling: Pods are scaled according to external metrics like database events, HTTP requests, and message queue lengths.
- More than 50 built-in scalers for services including Kafka, RabbitMQ, AWS SQS, and Azure Monitor are supported by Custom Scalers.
- Smooth HPA Integration: Supports HPA by enabling it to manage scaling choices based on resource-based and external measurements.
- Deploying the KEDA operator on your Kubernetes cluster is necessary for flexible deployment.
- Scale to Zero: When demand is low, it is possible to scale down to zero pods, which lowers expenses.
When to Use KEDA
- For event-driven workloads where scaling depends on external metrics.
- when there are several event sources involved in the scaling needs.
To deal with scaling to zero, KEDA uses a special technique that avoids the typical behavior of the HPA it controls. KEDA scales the deployment down to zero replicas if no events are found that would trigger scaling. This is in contrast to the usual HPA behavior where at least one replica would typically remain active. KEDA instructs the HPA to scale up from zero in accordance with events that satisfy the specified triggers, thereby reactivating the pods as required.
Architecture
When to use What?
The HPA is a reliable and straightforward solution for scaling workloads based on resource usages like CPU and memory. KEDA, on the other hand, offers enhanced functionality, enabling autoscaling for event-driven applications and custom or external metrics.
If your use case involves complex, event-driven architectures or demands scaling based on metrics outside HPA’s native scope, KEDA provides the advanced capabilities required. It is a sophisticated extension of Kubernetes autoscaling, purpose-built for modern, cloud-native applications.
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I’m a DevOps Engineer with 3 years of experience, passionate about building scalable and automated infrastructure. I write about Kubernetes, cloud automation, cost optimization, and DevOps tooling, aiming to simplify complex concepts with real-world insights. Outside of work, I enjoy exploring new DevOps tools, reading tech blogs, and play badminton.
