Event-Driven Autoscaling: Using KEDA with Kubernetes

For platform engineering teams, controlling infrastructure costs is a constant battle. One of the biggest sources of waste comes from idle workloads—services that run 24/7 but only process jobs sporadically. Each replica consumes resources, and across a large fleet those costs add up quickly. The ability to scale workloads all the way down to zero is no longer a luxury; it’s a financial necessity.

Kubernetes Event-Driven Autoscaling (KEDA) delivers this capability out of the box. By monitoring event sources such as Kafka, RabbitMQ, or AWS SQS, KEDA ensures your applications run only when there’s actual work to process. This guide takes a practical look at implementing KEDA in production, exploring its core features, setup patterns, and best practices for maximizing both cost savings and operational efficiency.

Key takeaways:

• Scale on events, not just resources: KEDA extends the standard Kubernetes HPA to scale applications based on external metrics like queue length or stream lag. This allows you to scale workloads down to zero when idle, which significantly reduces resource costs for intermittent jobs.
• Fine-tune configurations for production: A reliable KEDA setup requires careful tuning of polling intervals and cooldown periods to prevent rapid scaling fluctuations. It's also critical to define proper pod resource requests and limits and use TriggerAuthentication to securely manage credentials for your event sources.
• Centralize KEDA management at scale: Managing KEDA across many clusters introduces complexity and configuration drift. A platform like Plural simplifies this by using a GitOps workflow to automate deployments, providing a unified dashboard for monitoring, and enforcing consistent security policies across your entire fleet.

What is KEDA and How Does it Work?

KEDA (Kubernetes Event-driven Autoscaler) extends Kubernetes’ native scaling capabilities to external event sources. While the Horizontal Pod Autoscaler (HPA) relies on resource metrics like CPU and memory, KEDA lets you scale workloads based on events—such as messages in a queue, Kafka lag, or cloud service triggers. It works alongside the HPA, enabling event-driven scaling for specific workloads while others continue using resource-based rules. A key feature is the ability to scale down to zero when idle, then scale up instantly as soon as events appear.

Core Components

KEDA’s main building block is the scaler—a connector that pulls metrics from an external system. Scalers are available for popular services such as RabbitMQ, Kafka, and Azure Event Hubs. Each scaler translates the state of an event source (e.g., queue length) into metrics KEDA can act on. This modular design makes it easy to plug in new event sources by configuring the right scaler.

Architecture

KEDA runs inside your cluster as a controller and metrics server. Scaling rules are defined using the ScaledObject custom resource, which links a deployment to an event source and scaling thresholds. When no events exist, KEDA scales the deployment to zero replicas. As soon as events appear, it brings the application back online, ensuring efficient resource usage.

Kubernetes Integration

KEDA integrates tightly with the HPA. When you create a ScaledObject, KEDA automatically generates an HPA and exposes external event metrics through the Kubernetes metrics API. The HPA consumes these metrics to adjust replica counts. If no metrics are available, KEDA pauses the HPA and scales the workload down completely. This approach keeps event-driven scaling consistent with native Kubernetes patterns, without requiring new abstractions for developers.

KEDA vs. HPA: What's the Difference?

Kubernetes provides the HPA out of the box, but it only reacts to internal resource metrics like CPU and memory. KEDA extends this model to external event sources, enabling scaling based on application-specific signals such as queue length or stream lag. Importantly, KEDA doesn’t replace HPA—it manages HPA objects behind the scenes using event-driven metrics.

Key Differences

• Trigger Type: HPA scales reactively on CPU/memory thresholds. KEDA scales proactively on external metrics (e.g., Kafka lag, RabbitMQ queue depth).
• Scale-to-Zero: HPA always maintains at least one pod. KEDA can scale workloads all the way to zero, restarting pods only when new events arrive.
• Metric Sources: HPA is tied to resource metrics. KEDA provides 60+ built-in scalers for queues, databases, and cloud services.

When to Use Each

• HPA: Best for steady, predictable traffic where CPU or memory directly maps to load (e.g., stateless APIs).
• KEDA: Best for event-driven or bursty workloads (e.g., job processors, message consumers) where external signals better reflect demand. Its scale-to-zero feature makes it especially valuable for intermittent workloads where cost efficiency matters.

How Event-Driven Autoscaling Works

Event-driven autoscaling adjusts resources based on the number of events in a queue or stream, rather than CPU or memory usage. This ensures compute capacity matches workload demand. KEDA enables this in Kubernetes by bridging external event sources with the cluster’s native autoscaling. It translates external metrics into values Kubernetes can act on, allowing workloads to scale dynamically.

The Mechanics of Scaling

KEDA runs as a metrics server and introduces a Custom Resource Definition (CRD) called ScaledObject. A ScaledObject defines the event source (e.g., Kafka topic, SQS queue), the target deployment, and scaling thresholds. KEDA continuously monitors the event source and exposes metrics to the Kubernetes Horizontal Pod Autoscaler (HPA). The HPA then adjusts replica counts according to the defined rules. This design extends native Kubernetes autoscaling to cover a broad range of external systems.

Supported Event Sources

KEDA ships with over 70 built-in scalers that connect to popular systems out of the box. These include message brokers like RabbitMQ and Kafka, cloud services such as AWS SQS and Azure Service Bus, and databases like PostgreSQL and MongoDB. It also supports time-based triggers with a cron scaler. This flexibility lets you implement event-driven scaling for diverse workloads—from stream processing to background jobs—without writing custom integrations.

Scaling from Zero

Unlike the default HPA, which keeps at least one pod running, KEDA supports scale-to-zero. When no events are present, KEDA can reduce replicas to zero, freeing all resources. As soon as new events appear, it automatically scales the deployment back up. This feature is especially valuable for workloads with sporadic traffic, significantly reducing infrastructure costs.

Authentication Methods

KEDA supports secure authentication through TriggerAuthentication and ClusterTriggerAuthentication objects. These allow credentials and identity configurations to be managed separately from ScaledObjects and reused across workloads. Supported methods include Kubernetes secrets, Microsoft Entra Workload ID, and AWS IAM Roles for Service Accounts (IRSA), enabling secure access to event sources without hardcoding credentials in manifests.

Common KEDA Challenges and How to Solve Them

While KEDA provides powerful event-driven scaling, implementing it effectively across multiple teams and applications introduces operational challenges. Managing diverse scaler configurations, ensuring resource efficiency, and troubleshooting scaling events require a systematic approach. As your Kubernetes environment grows, these issues become more pronounced, demanding standardized practices and robust tooling to maintain stability and performance. Addressing these common hurdles is key to unlocking KEDA's full potential without adding unnecessary complexity to your platform.

Handle Complex Configurations

KEDA uses ScaledObject resources to define how an application should scale based on metrics from external sources. Each event source, or scaler, has its own specific metadata and authentication requirements. For example, scaling based on a Kafka topic's lag requires different parameters than scaling on an AWS SQS queue length. As you integrate more applications, managing these varied and often complex configurations becomes a significant challenge. Inconsistent or incorrect configurations can lead to scaling failures or unintended behavior.

To solve this, establish standardized templates for your most common scalers. By using a GitOps workflow, you can enforce consistency and manage these configurations as code. A platform like Plural can further simplify this by allowing you to define and deploy KEDA configurations across your entire fleet from a central point, ensuring every team follows the same proven patterns.

Manage Resources Effectively

One of KEDA's most compelling features is its ability to scale workloads down to zero replicas when there are no events to process, which can lead to significant cost savings. However, this introduces the risk of cold starts, where the application takes time to initialize when a new event arrives. The challenge lies in balancing resource optimization with application responsiveness. Setting an aggressive pollingInterval can reduce latency but increases load on the event source, while a high maxReplicaCount can lead to resource contention on your nodes.

Effectively managing resources requires careful tuning based on application-specific needs. Analyze your workload's performance characteristics to determine an acceptable cold start time and set minReplicaCount to 1 or more for latency-sensitive applications. Use Kubernetes resource requests and limits to prevent a rapidly scaling application from starving other workloads on the same node.

Monitor and Troubleshoot

When an application doesn't scale as expected, identifying the root cause can be difficult. The problem could lie with the application itself, the KEDA configuration, the event source, or the underlying Kubernetes cluster. Troubleshooting requires correlating data from multiple systems: metrics from the event source, logs from the KEDA Operator and Metrics Server pods, and the state of the Horizontal Pod Autoscaler (HPA) that KEDA manages.

Effective troubleshooting in KEDA starts with centralized observability. Ensure you are scraping metrics from both KEDA and your event sources into a unified monitoring platform. Plural’s embedded Kubernetes dashboard provides a single pane of glass to inspect logs, events, and resource states across your fleet. This consolidated view simplifies the process of tracing a scaling issue from the initial trigger to the final pod deployment, reducing the time it takes to diagnose and resolve problems.

Integrate with Existing Workloads

Introducing KEDA to existing, stateful, or legacy applications can be challenging. These workloads may not be designed to scale horizontally or handle the rapid startup and shutdown cycles that event-driven scaling produces. Forcing KEDA onto an application that isn't architected for elasticity can lead to data corruption, connection issues, or unpredictable behavior. The integration process requires careful planning to avoid disrupting production services.

The key is to adopt a gradual rollout strategy. Start by identifying stateless applications or services that are good candidates for event-driven scaling. Test the integration thoroughly in a staging environment that mirrors production traffic patterns. For stateful applications, evaluate whether they can be re-architected for horizontal scaling before introducing KEDA. Using a deployment platform that manages distinct environments helps isolate these changes and ensures you can validate behavior before promoting to production.

Best Practices for a High-Performing KEDA Setup

Deploying KEDA is the first step, but optimizing its configuration is what unlocks its full potential for performance and cost savings. A well-tuned KEDA setup ensures your applications scale responsively without over-provisioning resources or overwhelming event sources. Implementing best practices helps you build a resilient, efficient, and predictable autoscaling system. Managing these configurations consistently across a large fleet can be challenging, which is where a unified platform like Plural simplifies governance and deployment through its GitOps-based workflow. Below are key practices to ensure your KEDA implementation is robust and high-performing.

Optimize Your Scaler Configuration

Each scaler in KEDA has specific metadata that dictates its behavior, such as the target value that triggers a scale-up event. Properly configuring these thresholds is critical. Set them too low, and you risk scaling too aggressively; set them too high, and your application may not respond quickly enough to load. Beyond thresholds, you should also configure the cooldownPeriod, which defines how long KEDA waits after the last trigger was active before scaling down. This prevents "flapping"—rapidly scaling up and down. As noted in one overview of KEDA, this allows workloads to "scale down to zero when no work is happening," which is a primary driver for optimizing resource usage and cost.

Allocate Resources Efficiently

KEDA determines when to scale, but your deployment configuration determines what resources each new pod receives. It's essential to define appropriate CPU and memory requests and limits for your scaled workloads. Without proper resource requests, the Kubernetes scheduler may not be able to place new pods, causing scaling events to fail. Without limits, a single pod could consume excessive resources and destabilize the node. KEDA’s ability to scale applications to zero makes efficient resource allocation even more critical. When scaling up from zero, you need to ensure the first pod has the resources it needs to start quickly and handle the initial load.

Fine-Tune Polling Intervals

The pollingInterval in your ScaledObject defines how frequently KEDA checks the event source for new metrics. The default is 30 seconds, but this may not be optimal for every workload. A shorter interval provides faster scaling responses, which is ideal for applications handling bursty traffic. However, frequent polling increases the load on the event source and can lead to API throttling or increased costs. A longer interval reduces this overhead but may cause delays in scaling. You must adjust this parameter to strike the right balance between responsiveness and efficiency based on your application's specific requirements and the event source's rate limits.

Implement Multiple Scalers

A key feature of KEDA is its ability to use multiple triggers within a single ScaledObject. This allows you to create more sophisticated scaling logic based on different conditions. For example, you could scale a data processing application based on the number of messages in a RabbitMQ queue and also use a cron scaler to pre-warm the application during peak business hours. KEDA evaluates all scalers and scales the deployment to the highest replica count required by any single scaler. With over 70 built-in scalers, you can combine triggers from various sources like message queues, databases, and cloud provider metrics to build a highly resilient and context-aware scaling strategy.

Use Custom Metrics

While KEDA’s extensive list of built-in scalers covers most common use cases, you may have application-specific metrics that are better indicators of load. For these scenarios, you can use scalers like the Prometheus, Datadog, or New Relic scalers to trigger scaling based on custom metrics. This gives you the flexibility to scale on any metric you can expose, such as active user sessions, shopping cart sizes, or transaction processing latency. Leveraging custom metrics for scaling allows you to align your infrastructure's behavior directly with business-level objectives, creating a more intelligent and efficient autoscaling system when standard CPU or memory metrics are insufficient.

How to Set Up KEDA in Production

Moving KEDA into a production environment is a logical next step for teams looking to optimize resource usage and costs. The process is well-documented, but a production setup requires careful attention to configuration, security, and monitoring to ensure reliability and performance at scale. Properly implemented, KEDA becomes a powerful, hands-off component of your Kubernetes stack that intelligently responds to real-time demand. The key is to move beyond a basic proof-of-concept and establish a robust, repeatable deployment pattern.

This involves not just installing the controller but also defining clear scaling rules, securing access to event sources, and integrating KEDA's operational metrics into your existing observability platform. By following a structured approach, you can confidently deploy KEDA to manage your event-driven workloads.

Step-by-Step Installation

KEDA can be installed in a Kubernetes cluster using several methods, but for production environments, using the official Helm chart is the recommended approach. Helm provides a standardized, configurable, and easily upgradeable way to manage the KEDA components. The installation deploys the KEDA Operator, which includes the controller manager and the metrics adapter. The controller manager watches for ScaledObject resources, while the metrics adapter serves the external metrics from your event sources to the Horizontal Pod Autoscaler (HPA). This setup allows you to manage KEDA's lifecycle just like any other application in your cluster, ensuring consistency and simplifying updates.

Applying Basic Configuration

Once installed, KEDA is ready to operate. Its fundamental value lies in enabling workloads to scale down to zero replicas when there are no events to process. This is a significant advantage over the standard HPA, which requires a minimum of one replica. When an event source shows activity, KEDA scales the workload back up. This "scale-to-zero" capability is especially useful for asynchronous or job-based processing workloads, as it can dramatically reduce resource consumption and lower infrastructure costs during idle periods. The initial configuration is minimal because KEDA’s core logic is ready to go post-installation; the real work begins when you define what and how to scale.

Configuring a ScaledObject

The ScaledObject is a Kubernetes Custom Resource Definition (CRD) that defines how KEDA should scale a specific application. This manifest is where you connect a deployment, StatefulSet, or custom resource to an event source. Inside the ScaledObject, you specify the scaleTargetRef (the workload to scale), polling intervals, cooldown periods, and minimum/maximum replica counts. The most critical part is the triggers section, where you define the event source—like a RabbitMQ queue length or a Kafka topic's consumer lag—and the threshold that initiates a scaling action. Each trigger tells KEDA which external metric to monitor and how to respond to changes in that metric.

Following Security Best Practices

In production, securing access to your event sources is critical. KEDA requires credentials to connect to message brokers, databases, and other systems. Instead of hardcoding secrets in your ScaledObject, you should use a TriggerAuthentication resource. This allows you to reference secrets stored securely within Kubernetes or in an external provider like HashiCorp Vault. For cloud environments, leveraging pod identity mechanisms like IAM Roles for Service Accounts (IRSA) on AWS is an even better practice. This approach grants the KEDA operator permissions directly, avoiding the need to manage secrets. With Plural, you can manage these configurations and associated RBAC policies as code, ensuring consistent and secure deployment across your entire fleet.

Monitoring Performance

To run KEDA reliably in production, you must monitor its components and scaling behavior. KEDA exposes detailed Prometheus metrics that provide insight into scaler activity, errors, and latencies. By scraping these metrics, you can build dashboards to track how many replicas are active for a given ScaledObject, whether scalers are encountering errors when polling event sources, and how quickly KEDA is responding to events. Integrating these metrics into your central observability platform is essential for troubleshooting. Plural’s unified console simplifies this by providing a single pane of glass to view KEDA's performance alongside your application and cluster metrics, making it easier to diagnose issues across your infrastructure.

Manage KEDA at Scale with Plural

KEDA is a powerful, lightweight tool that adds event-driven autoscaling to any Kubernetes cluster. While deploying it on a single cluster is straightforward, managing its configuration, monitoring its behavior, and ensuring security across a large fleet of clusters introduces significant operational complexity. This is where a unified management platform becomes essential.

Plural provides a single pane of glass for managing your entire Kubernetes fleet, simplifying how you deploy, monitor, and secure KEDA at scale. By leveraging a GitOps-based workflow and a centralized console, Plural helps you maintain consistency and control over your event-driven workloads, no matter how many clusters you run.

Simplify Your Fleet Management

Managing KEDA configurations across dozens or hundreds of clusters can quickly lead to drift and inconsistency. Each cluster might have slightly different ScaledObject definitions, scaler configurations, or authentication secrets. Plural solves this by providing a centralized platform to manage your entire fleet. Using our GitOps-based continuous deployment, you can define your KEDA configurations once in a Git repository and have Plural ensure they are applied consistently across all target clusters. This approach eliminates manual errors and makes it easy to track changes, roll back configurations, and maintain a clear audit trail for your entire event-driven infrastructure.

Automate KEDA Deployments

KEDA extends Kubernetes with its own CRDs, such as ScaledObject and TriggerAuthentication, which define scaling behavior. Manually applying these CRDs to each cluster is not a scalable solution. Plural automates this entire process. You can package KEDA and its configurations as an application within Plural and use our deployment pipelines to roll it out to any number of clusters automatically. Whether you're deploying a new Prometheus scaler to your monitoring clusters or updating authentication details for an SQS queue scaler, Plural ensures the changes are deployed reliably and efficiently, allowing your team to focus on application logic instead of manual configuration management.

Gain Deeper Monitoring Insights

KEDA scales workloads based on metrics from external sources like message queues or databases. While KEDA's metrics server exposes this data to the Horizontal Pod Autoscaler (HPA), you still need a way to visualize and understand scaling behavior in context. Plural’s embedded Kubernetes dashboard provides a unified view of your clusters, allowing you to monitor pod counts, resource utilization, and KEDA-related events from a single interface. This centralized observability helps you correlate scaling events with application performance and troubleshoot issues quickly, without needing to switch between different monitoring tools or query Prometheus directly for each cluster.

Work from a Unified Console

Operating event-driven systems requires clear visibility into how applications react to real-world demands. Without a centralized console, engineers are forced to juggle kubeconfigs and terminal windows to inspect ScaledObjects or check the status of deployments across different environments. The Plural console provides a secure, SSO-integrated dashboard for all your clusters. From this single interface, your team can view the status of KEDA components, inspect logs from scaled pods, and manage application configurations. This simplifies troubleshooting and gives everyone on the team, from developers to SREs, the visibility they need to manage their services effectively.

Enforce Security Controls

Deploying KEDA requires granting it permissions to interact with the Kubernetes API and external event sources. Managing these permissions consistently across a fleet is critical for security. Plural helps you enforce standardized security controls through features like Global Services. You can define fleet-wide RBAC policies for KEDA in a central Git repository and use a GlobalService to sync them across all your clusters. This ensures KEDA operates with the principle of least privilege everywhere it's deployed. By automating the distribution of security configurations, Plural reduces the risk of misconfiguration and helps you maintain a strong security posture for your event-driven workloads.

Related Articles

• Kubernetes Autoscaling: Your Essential Guide
• How to Monitor Kubernetes Performance: A Full Guide

Frequently Asked Questions

Does KEDA completely replace the standard Kubernetes Horizontal Pod Autoscaler (HPA)? No, KEDA works with the HPA rather than replacing it. Think of KEDA as an extension that makes the HPA smarter. KEDA acts as a metrics server that monitors external event sources and exposes those metrics to Kubernetes. For each ScaledObject you create, KEDA automatically generates and manages a corresponding HPA object, feeding it the external metrics it needs to make scaling decisions. This allows you to continue using the native HPA for resource-based scaling on other workloads while applying event-driven logic where it's needed most.

Is KEDA only useful for applications that can scale down to zero replicas? While the scale-to-zero capability is one of KEDA's most well-known features for optimizing costs, its utility is much broader. KEDA's core function is to scale workloads based on metrics from external systems. This is valuable even for applications that must always have at least one replica running. You can set a minReplicaCount greater than zero in your ScaledObject to ensure high availability while still benefiting from KEDA's ability to proactively scale your application based on real-time demand, such as the length of a message queue.

What happens if the event source KEDA is monitoring becomes unavailable? KEDA is designed to be resilient in the face of external system failures. If a scaler cannot connect to its event source to retrieve metrics, it will not report any new values. As a result, KEDA will not take any scaling action, and your application will maintain its current number of replicas. This prevents your workload from being scaled down incorrectly due to a temporary network issue or an outage in the event source itself. Once the connection is restored, KEDA will resume polling and adjust the replica count as needed.

How do I choose the right pollingInterval for my application? The pollingInterval determines how frequently KEDA queries the event source, and finding the right value involves a trade-off between responsiveness and system load. For latency-sensitive applications that need to react to sudden spikes in traffic, a shorter interval (e.g., 5-10 seconds) is appropriate. For batch processing or less critical workloads, a longer interval (e.g., 30-60 seconds) can reduce the load on your event source and prevent potential API throttling. The best approach is to analyze your application's requirements and the rate limits of your event source to find a balance that works for you.

My team already uses a GitOps workflow. How does Plural make managing KEDA easier? While a GitOps workflow is excellent for defining the desired state of your KEDA configurations, Plural simplifies the management and operational aspects across a large fleet. Plural automates the consistent application of your KEDA configurations to any number of clusters, eliminating configuration drift. Furthermore, Plural provides a unified console and embedded Kubernetes dashboard, giving you a single pane of glass to monitor scaling behavior, troubleshoot issues, and manage RBAC policies across your entire infrastructure. This centralizes observability and control in a way that a standard GitOps toolchain alone does not.