Using Open Source Tools to Get Started with Chaos Engineering

Chaos engineering is essential for building resilient systems. By intentionally injecting failures, you can uncover weaknesses before they cause production outages. Fortunately, a powerful ecosystem of open-source tools makes it easier than ever for Site Reliability and Platform Engineering teams to get started.

Let’s explore some of the top open-source chaos engineering tools that can help you improve system reliability and train your operational readiness.

Understanding the Chaos Engineering Landscape

The core principle of chaos engineering is to run controlled experiments that test your system’s ability to withstand turbulent conditions. This practice helps you proactively identify and fix issues, ultimately leading to higher uptime and a better customer experience. Open-source tools are often the first step for many teams, offering a way to learn and experiment without initial financial commitment.

These tools provide the frameworks to attack your systems, but remember that a successful chaos engineering practice involves more than just breaking things. It requires careful planning, defining steady-state hypotheses, and analyzing the results to gain actionable insights.

Watch: Getting Started with Open Source Tools for Chaos Engineering 

Leading Open-Source Tools for System Resilience

While many tools are available, a few have become foundational in the chaos engineering community. Here’s a look at some of the most prominent open-source options that SREs and platform teams rely on.

Chaos Monkey

Chaos Monkey is the tool that started it all. Originally developed by Netflix, it was created to test the resilience of their infrastructure on Amazon Web Services (AWS). As its name suggests, Chaos Monkey works by randomly terminating virtual machine instances and containers.

Key Features:

• Simplicity: Its primary function is straightforward—it randomly disables production instances.
• Integration: It was designed for the Spinnaker platform, enabling continuous delivery and infrastructure management.
• Foundational Learning: Using Chaos Monkey forces teams to build stateless, resilient services that can survive instance failures.

By forcing services to be built without depending on any single instance, Chaos Monkey helped Netflix automate failure recovery and build a more robust architecture. It’s a great starting point for teams new to chaos engineering, as it tests one of the most common failure scenarios.

Before Benjamin Wilms founded Steadybit, a commercial platform designed to make chaos engineering scalable across teams, he authored the open source version of Chaos Monkey for Spring Boot applications.

LitmusChaos

LitmusChaos is a powerful, cloud-native chaos engineering framework for Kubernetes. It has gained significant popularity and is now a CNCF incubating project. Litmus provides a comprehensive chaos marketplace with a wide variety of pre-defined experiments.

Key Features:

• Kubernetes-Native: Designed specifically for Kubernetes, allowing you to run detailed experiments on pods, nodes, and other resources.
• ChaosHub: An open marketplace where the community can contribute and share chaos experiments, making it easy to find tests for common scenarios.
• Declarative Approach: Experiments are defined via YAML, fitting seamlessly into GitOps workflows and CI/CD pipelines.

Litmus empowers SREs to run experiments declaratively, which simplifies automation and integration. Its focus on Kubernetes makes it an excellent choice for teams managing containerized applications.

Chaos Mesh

Chaos Mesh is another CNCF incubating project that offers a comprehensive chaos engineering solution for Kubernetes. Developed by PingCAP, it provides a rich set of fault injection capabilities and a user-friendly dashboard for managing experiments.

Key Features:

• Diverse Fault Types: Chaos Mesh allows you to simulate a wide range of issues, including pod failures, network latency, I/O stress, and even kernel-level faults.
• Declarative and Imperative Modes: You can define experiments using CRDs (Custom Resource Definitions) in Kubernetes or manage them dynamically through the dashboard.
• Fine-Grained Control: Enables precise targeting of failures, allowing you to limit the “blast radius” to specific namespaces, labels, or even individual pods.

With its robust feature set and easy-to-use interface, Chaos Mesh is a strong contender for teams looking for a versatile and powerful chaos engineering platform for their Kubernetes environments.

ChaosBlade

ChaosBlade is an open-source chaos engineering tool designed to help engineers uncover potential weaknesses in their systems before they lead to significant outages. Created by Alibaba, this tool supports injecting failures into various layers of your infrastructure, including application, operating system, and Kubernetes environments. By leveraging ChaosBlade, teams can improve their systems’ resilience and gain a deeper understanding of complex dependencies within their architecture.

Key Features:

• Multi-Layer Fault Injection: ChaosBlade enables fault injection across multiple layers, such as application logic, operating systems, and container orchestration platforms like Kubernetes.
• Rich Experiment Scenarios: Provides a diverse set of pre-configured failure scenarios, including CPU throttling, memory overload, process termination, network latency, and pod failures, empowering teams to simulate critical issues effectively.
• Integration with Kubernetes: Offers native support for Kubernetes environments, enabling users to define and execute chaos experiments directly for their containerized applications.

ChaosBlade is particularly well-suited for platform engineering and SRE teams operating in large-scale distributed environments. Its straightforward design and robust fault injection capabilities make it a valuable tool for identifying potential points of failure while ensuring systems can withstand even the most challenging conditions.

Why Scaling a Reliability Program is Challenging with Open Source Tools

Open Source tools provide a fantastic starting point for implementing chaos engineering practices, particularly for teams looking to explore resilience without significant upfront investment.

However, as organizations scale their reliability programs, inherent limitations of Open Source tools begin to emerge:

• Time-Intensive Deployment: Configuring scripts to actually impact the desired targeted technology often requires considerable manual effort and customization, resulting in extended setup times that can delay value realization.
• Integrations & Maintenance: Additionally, Open Source tools may lack seamless integrations with the wide range of technologies found in complex enterprise environments, making it harder to incorporate them into existing workflows. If you decide to build your own custom integrations, then you’ll need to document and maintain them over time, as well as any other modifications.
• Web UI and Usability: and is another critical differentiator. Commercial tools are often designed with user-friendly interfaces and guided workflows that minimize the learning curve, even for engineers less experienced in chaos engineering.
• RBAC Maturity and Enterprise Requirements: Open source tools often lack Role-Based Access Control (RBAC) features that meet the security and compliance standards of enterprise environments. Without this, scaling a program across teams and environments is prohibitive without a workaround. 
• Limited Reporting: Open Source tools also typically provide limited options for analyzing and sharing experiment results. By contrast, commercial solutions offer robust, real-time dashboards and detailed reporting, empowering teams to act on insights effectively and demonstrate the impact of reliability efforts to stakeholders.

These gaps make commercial solutions a compelling choice when scaling chaos engineering practices at the enterprise level.

Building a Reliability Culture with a Commercial Tool Like Steadybit

While these open-source tools are incredibly powerful, if you want to scale a program across your organization, commercial tools make a lot of sense.

Tools like Steadybit are designed to be easy to adopt, deploy, and start getting value from right away; and they are built to scale.

Steadybit provides an enterprise-grade chaos engineering platform that makes it easy for organizations to:

• Integrate and deploy experiments across your entire tech stack
• Customize your experiments with an OS extension framework
• Automate resilience testing into your CI/CD processes
• Manage fine-grained permissions for teams and test environments
• Create experiment templates to scale best practices across teams
• Leverage AI-powered insights with the Steadybit MCP Server

Rolling Out Chaos Engineering at Scale

We could add more bullets about why Steadybit will make your chaos engineering rollout easier, but if you’re curious, you should just check it out yourself.

We built a platform to specifically address these open source challenges head on.

You can test out Steadybit with a free 30-day trial or you can schedule a quick demo with our team of experts to hear why teams are choosing Steadybit for their reliability platform.