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Last updated
July 18, 2025
Bruno Lopes
Product Leader
Testkube
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Table of Contents
Load testing is an essential part of the software development process to ensure the performance and scalability of applications. As more and more organizations move to Kubernetes to manage their containerized workloads, load testing in a Kubernetes environment presents unique challenges. In this article, we will explore the best practices and tools for load testing in Kubernetes and how Testkube can help address common pitfalls.
Best Practices for Load Testing in Kubernetes
Start with small-scale testing: Before running large-scale load tests, start with small-scale tests to ensure the application is functioning as expected. This will help identify and fix any issues before running large-scale tests.
Use real-world scenarios: Or simulate it as much as you can. Simulating real-world scenarios is crucial to understanding how the application will perform under different conditions. This can include simulating user behavior, network latency, and other variables that can impact performance.
Monitor the infrastructure: Monitoring the Kubernetes infrastructure is critical to identifying performance bottlenecks and other issues. This can include monitoring CPU and memory usage, network traffic, and other metrics that can impact application performance. Observability tools like Tracetest can become really handy in these scenarios.
Use load testing tools designed for Kubernetes: There are several load testing tools available for Kubernetes environments, such as k6, Gatling, and Locust. These tools are designed to work seamlessly with Kubernetes and can help simplify the load testing process.
Gatling: Gatling is another popular open-source load testing tool that offers a user-friendly interface and supports real-time monitoring of performance metrics. It can be easily integrated with Kubernetes using the Kubernetes plugin.
Locust: Locust is an open-source load testing tool that offers a simple and intuitive user interface. It supports distributed load testing and can be easily integrated with Kubernetes using the Kubernetes plugin.
How Testkube Can Help with Load Testing in Kubernetes
Testkube is a cloud-native testing solution designed to streamline the testing process in a Kubernetes environment. It supports any testing tool including k6, Gatling or even bringing your own load testing solution. Additionally, here are some ways Testkube can help with load testing in Kubernetes:
Scalability: Testkube leverages the scalability of Kubernetes to enable the parallel execution of tests, significantly reducing the time required to run an entire test suite.
Automation: Taking your testing tools (k6, Gatling, Locust), packaging them and deploying them in Kubernetes might make the testing flow very slow. Figuring out the right CI/CD flow can become even harder. Testkube provides built-in automation capabilities, making it easy to integrate with existing CI/CD pipelines and eliminating the need for extensive manual intervention..
Consolidated Test Results: Testkube consolidates test results from multiple sources, streamlining the analysis process and making it easier to identify trends and areas for improvement. This can help with the analysis of load testing results and identifying bottlenecks.
Check our guides on how to run k6 tests in Kubernetes with Testkube.
Conclusion
Load testing in Kubernetes presents unique challenges, but with the right tools and best practices, it can be effectively managed. k6, Gatling, and Locust are popular load testing tools designed for Kubernetes environments, and Testkube can help address common pitfalls such as scalability, automation, and test result management. By implementing these best practices and leveraging these tools, organizations can ensure their applications perform well under load and maintain high-quality software.
If you have any questions you can join our Slack community or, if you have any ideas for other useful features, you can create the feature requests at our Github Issues page.
Load Testing in Kubernetes - FAQs
Top 5 Most Important Load Testing in Kubernetes FAQs
Load testing in Kubernetes simulates traffic to assess how applications perform under stress in a dynamic, containerized environment. It's crucial because Kubernetes introduces unique challenges that only emerge under load, such as autoscaling behavior, resource contention, and network policies affecting performance.
Load testing helps teams validate that their applications can handle production traffic while taking advantage of Kubernetes' auto-scaling capabilities. It ensures stability and performance before release, preventing costly outages and performance degradation in production environments.
You can run load tests by deploying testing tools as Kubernetes resources:
Kubernetes Jobs: Deploy tools like k6, Locust, or JMeter as one-time Jobs for on-demand testing
Kubernetes Pods: Run load tests directly in Pods for more control over the testing process
CronJobs: Schedule regular load tests to monitor performance over time
CI/CD Integration: Trigger load tests automatically as part of your deployment pipeline
Orchestration platforms: Use tools like Testkube to centralize and manage your load testing process
Popular load testing tools that work well in Kubernetes include:
k6: Modern, developer-friendly tool with JavaScript scripting and excellent Kubernetes integration
Locust: Python-based tool with web UI, great for complex scenarios and distributed testing
JMeter: Feature-rich GUI tool with extensive protocol support, though heavier on resources
Artillery: Node.js-based tool with simple configuration and good performance
Gatling: High-performance tool with detailed reporting capabilities
These tools can be containerized and deployed within Kubernetes clusters for scalability and closer-to-production testing conditions.
Both approaches have their merits and should be used strategically:
Inside the cluster:
Tests internal service-to-service communication
Validates service mesh behavior and network policies
Leverages cluster resources for scalable testing
Provides insight into internal bottlenecks
Outside the cluster:
Simulates real-world user traffic patterns
Tests ingress controllers and load balancers
Validates end-to-end performance including external dependencies
Avoids resource contention with the application under test
The choice depends on your testing goals - use internal tests for component validation and external tests for user experience validation.
Effective monitoring during load testing requires observing multiple layers:
Application metrics: Response times, error rates, throughput, and business-specific KPIs
Kubernetes metrics: Pod restarts, CPU/memory usage, network traffic, and autoscaling events
Infrastructure metrics: Node resources, storage I/O, and network latency
Use these tools for comprehensive monitoring:
Prometheus and Grafana: For real-time metrics collection and visualization
Testkube or k6 Cloud: For centralized test reporting and historical analysis
Alert systems: Set up threshold-based alerts for immediate notification of performance issues
Distributed tracing: Use tools like Jaeger or Zipkin to understand request flows
About Testkube
Testkube is a test execution and orchestration framework for Kubernetes that works with any CI/CD system and testing tool you need. It empowers teams to deliver on the promise of agile, efficient, and comprehensive testing programs by leveraging all the capabilities of K8s to eliminate CI/CD bottlenecks, perfecting your testing workflow. Get started with Testkube's free trial today.