Scalable and Cost-Effective Event-Driven Workloads with KEDA and Karpenter on Amazon EKS

In today’s cloud-native landscape, efficient management of event-driven workloads is essential for real-time data processing. Traditional autoscaling often falls short amidst unpredictable event volumes, leading to inefficiencies and increased costs. Amazon Elastic Kubernetes Service (EKS), which is a managed container orchestration platform and is well-suited for deploying container-based applications. By integrating Kubernetes Event-Driven Autoscaling (KEDA) and Karpenter with Amazon EKS, we can overcome these challenges. KEDA enables detailed autoscaling based on event metrics, and Karpenter ensures timely node provisioning, addressing the inefficiencies of manual scaling. This post aims to provide a simplified guide to integrating KEDA and Karpenter with Amazon EKS for a more efficient, cost-effective, and high-performing setup.

Through a step-by-step approach, we’ll simplify the configuration process to help teams better manage event-driven workloads on Amazon EKS. This integration showcases a scalable, cost-effective, and resilient setup for handling event-driven workloads in a Kubernetes environment, making the most of the capabilities of Amazon EKS, KEDA, and Karpenter.

Kubernetes Event-Driven Autoscaling (KEDA)

Shifting our attention to the domain of autoscaling, KEDA stands out as a cornerstone for event-driven scaling. It equips your Kubernetes deployments with the ability to dynamically adapt in response to external events funneled from a variety of sources, such as message queues and event streams. Embrace an event-driven paradigm, that allows your applications to scale precisely in line with event influx, ensuring resource optimization and cost-efficiency. With KEDA, tap into a plethora of scalers catering to different event sources, fortifying the bridge between Kubernetes and the external world.


Karpenter is a flexible, high-performance Kubernetes cluster autoscaler offering dynamic, groupless provisioning of worker node capacity to cater to unscheduled pods. Thanks to Karpenter’s groupless architecture, the restriction of using similarly specified instance types is eliminated. Karpenter perpetually assesses the collective resource demands of pending pods along with other scheduling constraints (for instance, node selectors, affinities, tolerations, and topology spread constraints), and orchestrates the ideal instance compute capacity as defined in the NodePool configuration. This augmented flexibility empowers different teams to tailor their own NodePool configurations to suit their application and scaling requisites. Moreover, Karpenter directly provisions nodes utilizing the Amazon EC2 fleet application programming interface (API), bypassing the need for nodes and Amazon EC2 auto scaling groups, which significantly accelerates provisioning and retrial times (shifting from minutes to mere milliseconds). This acceleration not only boosts performance but also enhances service level agreements (SLAs).

Solution architecture for running event driven workloads on Amazon EKS

Architecture diagram for running event driven workloads on Amazon EKS

Workflow outline

  1. Initiation of financial transaction: Users initiate financial transactions which are captured and forwarded to an Amazon Simple Queue Service (SQS) For demonstration purposes, a script emulating the role of a Message Producer will be executed to populate the queue with messages representing transactions.
  2. Transaction message consumption: Within an Amazon EKS cluster, a containerized Message Consumer application is actively monitoring and retrieving messages from the Amazon SQS queue for processing.
  3. Queue depth escalation: As user engagement escalates, exemplified by running the Message Producer script in multiple terminals during the demonstration, the influx of transaction messages causes an increase in queue depth, indicating a backlog as the Message Consumer application struggles to keep pace with the incoming message rate.
  4. Automated scalability response: Integrated within the Amazon EKS cluster are KEDA and Karpenter, which continuously monitor the queue depth. Upon detecting a message backlog, KEDA triggers the provisioning of additional Message Consumer pods, enhancing the system’s processing capacity to effectively manage the higher message volume in a timely manner.

Solution overview



Sample application overview: Orchestrating event-driven messaging

We’ll dive into the construction of a basic message processing application, showcasing the seamless orchestration of event-driven messaging. The application comprises the following integral components:

  • Amazon SQS: Serving as the backbone of our messaging architecture, Amazon SQS is a fully-managed message queuing service that graciously handles incoming messages and avails them to consumers upon request.
  • Message producer: Encapsulated within a container, this Python-based application emulates real-world transaction scenarios by publishing messages to the Amazon SQS queue.
  • Message consumer: Residing in a separate container, the Message Consumer stands vigil, incessantly scrutinizing the SQS queue to promptly seize the earliest message awaiting processing.
  • Amazon DynamoDB: Acting as the terminal point of our message journey, the Message Consumer retrieves messages from the queue and dutifully deposits them into a DynamoDB table, earmarking the conclusion of the message processing cycle.

Deploying Amazon EKS Cluster, KEDA, Karpenter and sample application

Follow the steps to set up an Amazon EKS cluster, and install KEDA and Karpenter on your Amazon EKS cluster.

Clone the repository to your local machine or download it as a ZIP file using the following command:

git clone

Navigate to the repository’s directory:

cd amazon-eks-scaling-with-keda-and-karpenter

Modify the file located in the deployment directory as per your requirements.

1 Variable  Name Description
2 AWS_REGION The AWS region.
3 ACCOUNT_ID The AWS account ID.
4 TEMPOUT Temporary output  file. This used to temp. store CFN for karpenter
5 DYNAMODB_TABLE The name of the Amazon DynamoDB table.
6 CLUSTER_NAME The name of the Amazon EKS  cluster.
7 KARPENTER_VERSION The version of Karpenter.
8 NAMESPACE The Kubernetes  namespace for KEDA.
9 SERVICE_ACCOUNT The Kubernetes  service account for KEDA.
10 IAM_KEDA_ROLE The AWS IAM role for  KEDA.
11 IAM_KEDA_SQS_POLICY The AWS IAM policy for  KEDA to access SQS.
12 IAM_KEDA_DYNAMO_POLICY The AWS IAMpolicy for  KEDA to access DynamoDB.
13 SQS_QUEUE_NAME The name of the Amazon SQS  queue
14 SQS_QUEUE_URL The URL of the Amazon SQS  queue.
15 SQS_TARGET_DEPLOYMENT The target deployment  for KEDA to scale based on Amazon SQS messages.
16 SQS_TARGET_NAMESPACE The target namespace  for the deployment that KEDA scales based on Amazon SQS messages.

To deploy the application, run this command

sh ./deployment/

You’ll be asked to verify the account in the context:

Image asking you to validate your AWS account

Next, select your deployment option:

Image shows deployment options

Under the hood

Upon selecting options 1, 2, or 3, the corresponding components are deployed. Specifically, option three triggers the deployment of all components – the Amazon EKS cluster, Karpenter, and KEDA.

Each of these components comes with its own independent deployment scripts. Thus, if you wish to bypass the aforementioned deployment process or desire to modify features for each component, you can alter the relevant files associated with each component:


This setup provides a flexible approach to deploying and customizing the components as per your requirements.

The GitHub repository houses the essential configuration files required for deploying KEDA and Karpenter. These files can be tailored to meet your particular needs.

Below are some notable files to consider:

Testing: KEDA and Karpenter scaling  in action

In this section, we’ll evaluate the scaling efficacy through a simulation script. Rather than real-world multiple requests being funneled into Amazon SQS from human transactions, we’ll execute a script that injects messages into Amazon SQS, which mimics a real-world scenario.

The script  has a straightforward function that dispatches messages to Amazon SQS at specified recurring intervals.

As illustrated in the previous diagram, the architecture of our environment deployment remains unchanged; however, we have introduced a message pump script. Multiple instances of this script will be run to simulate a realistic load, thereby triggering KEDA and Karpenter to scale pods and nodes accordingly.

Upon the deployment of the Cluster, Karpenter, and KEDA, your shell interface will exhibit a similar appearance.

KEDA install confirmation

Open two additional terminals and establish a connection to the Amazon EKS cluster.

In the first terminal, navigate to the keda-test namespace.

In the second terminal, navigate to the Nodes section (your terminal should appear as shown below).

Open three or more terminals, copy the content from deployment/ and execute it on all three terminals.

This action configures the terminal context with all the required environment variables needed for the script execution.

Run the script on all four terminals.

Image displays all the variables used to deploy EKS cluster, Karpenter and KEDA

Navigate to the app → keda directory and create a Python virtual environment.

cd app/keda
python3 -m venv env
source env/bin/activate
pip install boto3
cd  {your path}/amazon-eks-scaling-with-keda-and-karpenter
python3 ./app/keda/

Open three or four terminals and run the script (i.e., multiple terminals are needed so that we can pump more messages in queue and increase the queue depth)

Upon activation, the scripts will begin injecting messages into Amazon SQS. As previously mentioned, there is a single pod operating within the keda-test namespace, which continuously scans Amazon SQS for messages to process.

Given that the three script instances from earlier are dispatching messages at a high frequency, this pod struggles to maintain the requisite message processing scale to keep the queue length within the defined limits (i.e., 2) as specified in

image presents KEDA scaler object config file marking queue length

Once the scaleobject ==> queueLength threshold is exceeded, KEDA will trigger the instantiation of additional pods to expedite the processing of Amazon SQS messages and reduce the queue length to the desired limits.

Scaling to eight Pods and two Nodes:

Due to this rapid scaling action initiated by KEDA, we begin to observe several pods in a pending state, as the default number of nodes in the cluster (i.e., two) can only accommodate a certain number of pods.

At this point, Karpenter steps in and continuously monitors the queue of pending pods. Upon analyzing the situation, Karpenter initiates the provisioning of new nodes within the cluster.

The horizontal scaling of pods and nodes orchestrated by KEDA and Karpenter will persist until either of the following conditions occurs:

  • The target queueLength is attained (or)
  • KEDA reaches the maxReplicaCount (or)
  • Karpenter encounters the CPU and memory limits.

Upon clearing the backlog of messages in Amazon SQS, the strength of this architecture shines as it facilitates scaling in both directions:

  • Scale-out – proficiently tackle pending activities or
  • Scale-in – optimizes the infrastructure for enhanced utilization and cost benefits.

Scaling to 50+ Pods and six Nodes (i.e., four Karpenter Nodes):

Scale-out and scale-in

The scaling journey commences at the pod level (i.e., orchestrated by KEDA) based on the backlog of messages awaiting processing, and extends to Karpenter to ensure ample nodes are available to host the pods provisioned by KEDA. The scaling-in process mirrors this pattern. As KEDA notices the pending tasks (in this instance, Amazon SQS) nearing the desired queue depth, it begins a controlled termination of the provisioned pods. The elegance of this setup lies in KEDA’s foresight—it doesn’t delay until the exact queue depth is achieved, but rather proactively gauges the optimal timing for pod termination, rendering it highly resource-efficient.

As Pods begin to wind down, Karpenter keeps a close eye on the resource utilization across nodes within the cluster. It can shut down underutilized nodes, minimizing resource squandering and trimming operational costs.

Back default state, all Karpenter instance are terminated post scale event


Navigate to the root directory of the repository:

cd amazon-eks-scaling-with-keda-and-karpenter

To initiate cleanup:

sh ./

Executing this command removes all AWS services and workloads established for this solution.


In this post, we showed you running event-driven workloads efficiently on Amazon EKS is substantially enhanced with the integration of KEDA and Karpenter. KEDA’s meticulous autoscaling based on event metrics, coupled with Karpenter’s timely node provisioning, creates a robust framework that excels in handling the dynamic nature of event-driven workloads. This synergy significantly bridges the scaling challenges, ensuring a responsive and cost-effective environment. The collaborative expertise of KEDA and Karpenter on Amazon EKS not only streamlines the management of event-driven workloads but also paves the way for a scalable, resource-efficient, and high-performing cloud-native ecosystem. Through this integration, businesses can effectively manage event-driven workloads, optimizing resource utilization while ensuring superior performance and cost effectiveness.

Additionally, stay tuned for upcoming posts focusing on various architecture patterns for running Event-Driven Architecture (EDA) workloads on EKS using KEDA. These forthcoming insights will further explore and illuminate the potential and versatility of this powerful integration in cloud-native environments.


For a deeper dive into KEDA and Karpenter, the following resources are highly recommended:

Sanjeev Ganjihal

Sanjeev Ganjihal

Sanjeev Ganjihal is a Sr. Specialist Solutions Architect for Containers at AWS. Sanjeev specializes in Service Mesh, Platform Engineering, Generative AI, Prompt Engineering, GitOps, IAC, Autoscaling, Cost Optimization & Observability. He helps customers in modernizing their applications by transitioning to containerized solutions, implementing AWS best practices, and steering their journey through cloud transformation. He is actively spending time on integrating AI with cloud-native solutions, delving into the realms of generative AI, prompt engineering, and harnessing data on Kubernetes. Outside of work he likes playing cricket and spends his time with his family.

Asif Khan

Asif Khan

Asif Khan is a seasoned Sr. Solution Architect on the AWS Enterprise team in Sydney, Australia. Specializing in catering to the needs of major retail clients. Asif boasts a rich history in containers and Kubernetes, seamlessly spanning across various cloud platforms. He possesses a knack for solving complex business challenges through a fusion of proprietary and open-source technologies. Beyond his tech prowess, Asif treasures quality time with his loving wife and two adorable children. Asif is a firm believer in technology's ability to provide equal opportunities for a better life, where access is granted solely through hard work, without any forms of discrimination or undue demands.