Kubernetes is great at keeping things running, but it doesn’t automatically know when to run them. That’s where autoscaling comes in.
If you are running a standard web server, native tools work fine. But if you are processing background jobs, video transcoding, or data streams, you have probably noticed that the native tools fall short. They react too slowly, or they don’t react at all.
This brings us to the main comparison: KEDA vs HPA:
What Is HPA (Horizontal Pod Autoscaler)?
HPA is the “vanilla” autoscaler that comes pre-installed with Kubernetes. It is reliable, simple, and built directly into the controller manager.
HPA looks at the pods you already have running and asks, “Are you guys busy?” If the CPU usage or memory consumption goes above a certain line (say, 80%), HPA adds more pods to share the load. It’s primarily designed for long-running services like APIs or websites where traffic correlates directly with resource usage.
What Is KEDA (Kubernetes Event-Driven Autoscaling)?
KEDA is an open-source project (now CNCF Graduated) that acts as an add-on to Kubernetes. It solves a problem HPA can’t: scaling based on external data.
HPA is stuck inside the cluster; it only knows about the CPU and RAM inside your nodes. KEDA looks outside. It connects to external event sources like a Kafka topic, an AWS SQS queue, or a PostgreSQL database query. It doesn’t care if your current pods are busy. It cares if there is work waiting to be done.
How HPA Works: Metrics and Scaling Logic
HPA operates on a simple feedback loop. Every 15 seconds (by default), the controller checks the Metrics Server.
If you set a target of 50% CPU utilization, HPA looks at the current usage. If usage hits 100%, it knows it needs to double the pod count to get the average back down to 50%.
The catch is that this is a lagging indicator. The load has to actually hit the server and potentially slow it down before HPA notices and starts spinning up new capacity.
How KEDA Works: Event-Driven Scaling Explained
KEDA works differently because it is proactive. It consists of three main parts:
- Scalers: Connectors that talk to external systems (RabbitMQ, Azure Service Bus, etc.).
- Metrics Adapter: A translator that turns “queue length” into a number Kubernetes can understand.
- Controller: The brain that manages the scaling logic.
Interestingly, KEDA doesn’t replace HPA completely. It actually extends it. KEDA feeds the external metric to the HPA to handle scaling from 1 to 100 replicas. However, KEDA handles the most critical step itself: activation. It can check a queue, see 0 messages, and shut the deployment down completely.
Supported Metrics and Triggers: HPA vs KEDA
When comparing HPA vs KEDA, the biggest differentiator is what triggers the scaling event.
HPA is limited to:
- Resource Metrics: CPU and Memory.
- Custom Metrics: You can technically feed application metrics to HPA, but setting up the adapters is complex and brittle.
KEDA simplifies this massively. It comes with over 60 built-in “scalers.” You don’t need to write custom code to scale based on:
- The lag in a Kafka Consumer group.
- The number of rows in a MySQL table.
- The depth of a Redis list.
- Incoming HTTP traffic (using the KEDA HTTP add-on).
Scaling Capabilities: Event-Driven, External, and Custom Metrics
The “Scale to Zero” capability is usually the deciding factor for many engineers.
Standard HPA has a floor: minReplicas: 1. You must always have at least one pod running to generate the CPU metrics HPA needs to make a decision. If you have a worker that only runs once a day, you are paying for 23 hours of idle time.
KEDA allows you to set minReplicas: 0. If your queue is empty, KEDA shuts down the application entirely. When a message arrives, KEDA wakes up the application, scales it to 1, and then lets HPA take over for further scaling.
Performance and Resource Efficiency Comparison
In a direct KEDA vs HPA efficiency matchup, KEDA wins for background processing.
If you use HPA for a queue worker, the worker has to pick up a job and max out its CPU before HPA scales. This creates a bottleneck. The queue might grow to 10,000 messages, but if the single worker is only at 70% CPU, HPA won’t add help.
KEDA looks at the queue length directly. It sees 10,000 messages and immediately scales to the maximum replica count to clear the backlog, regardless of CPU usage.
When to Use HPA
Don’t overcomplicate things if you don’t have to. HPA is the right choice if:
- You are hosting a standard REST API or web frontend.
- Your traffic increases gradually rather than in massive, instant spikes.
- CPU and Memory are accurate representations of your application’s load.
When to Use KEDA
You should switch to KEDA if:
- You are processing background jobs (emails, video processing, data ingestion).
- Your workload is “bursty” (e.g., nothing for hours, then 10,000 events at once).
- You want to save money by scaling unused services to zero.
- You need to scale based on something specific, like the number of users connected to a websocket.
Feature Comparison Table: KEDA vs HPA
| Feature | HPA (Native) | KEDA (Event-Driven) |
| Primary Signal | CPU / Memory | External Events (Queues, Streams, DBs) |
| Scale to Zero | No (Minimum 1 pod) | Yes |
| Proactive/Reactive | Reactive (Wait for load) | Proactive (Scale on pending work) |
| Setup | Out of the box | Requires installing an Operator |
| Best For | Web Servers / APIs | Workers / Consumers / Cron Jobs |
Which Autoscaler Is Best for Your Kubernetes Workloads?
Ultimately, HPA vs KEDA debate isn’t about picking a winner; it’s about picking the right tool for the specific microservice.
In a mature production environment, you will likely use both. You will use HPA for your user-facing frontend services to handle HTTP traffic, and you will use KEDA for your backend consumers to crunch through data efficiently. This gives you the best of both worlds: a responsive UI and a cost-effective backend.
