DevSecOps

The Top 7 Chaos Engineering Tools

Explore Top Chaos Engineering Tools known for their resilience testing, fault injection, and system stability assessment features to proactively identify and address weaknesses in distributed systems.

Last updated on Dec 05, 2024

Written by Mirren McDade

Technical review by Laura Iannini

The Top 7 Chaos Engineering Tools include:

1. AWS Fault Injection Service
2. Chaos Monkey
3. Gremlin Reliability Score
4. Litmus
5. Proofdock Chaos Platform
6. Simoorg
7. Steadybit

Chaos Engineering is a discipline that promotes system efficiency and resilience by intentionally inducing systemic failures to identify and mitigate potential weaknesses. As systems become more complex, ensuring they can withstand unexpected failures and unusual conditions is a critical part of modern software development and system administration.

Chaos Engineering tools enable systems administrators and development teams to experiment in a secure and systematic way, replicating potential challenges and issues in the production environment. They allow teams to understand how their system behaves under stress, manage incident responses, and ensure system resiliency.

Chaos Engineering tools are designed by orchestrating tests that deliberately introduce failures into systems, observing how the system responds and then using that information to improve the system. These may include crashes, latency, and network failures. Post-incident analytics and reporting are also key aspects of these tools, allowing teams to learn and improve from the experiments.

As the industry evolves, many tools and software providers have emerged in the market, each with distinctive offerings and varied feature sets. This guide will explore the top Chaos Engineering tools available on the market based on our technical assessments, industry research, and customer feedback.

AWS Fault Injection Service

Amazon Web Services (AWS) delivers cloud computing platforms and APIs to a diverse range of clients, offering variable, pay-per-use pricing. Among its many solutions is the AWS Fault Injection Service (FIS), integrated into AWS Resilience Hub. FIS is designed to optimize application resilience and performance via fault injection experiments.

Fault Injection Service empowers IT teams to identify performance bottlenecks or other weaknesses overlooked by conventional software tests. The service enables users to establish specific conditions to cease an experiment or revert to the state prior to the experiment. With the FIS Scenario Library, setting up and running controlled experiments takes minutes. FIS offers trackable, real-world failure conditions for enriched insight into different resource performance. Its capabilities extend to running a “game-day simulation”, where previous failure patterns or known weaknesses can be simulated to monitor the system’s performance. FIS can be seamlessly integrated into the delivery pipeline, enabling continual testing of fault actions’ impacts.

Additionally, AWS Fault Injection Service can run CPU stress in an instance, assessing how applications manage CPU stress to ensure that CPU utilization doesn’t exceed specified thresholds.

Chaos Monkey

Netflix’s Chaos Monkey is an open-source software tool designed to assess the strength and robustness of their operating systems. This tool operates by deliberately creating failures in a production environment to test resiliency. By frequently subjecting engineers to system disruptions, they are encouraged to create more durable services.

Chaos Monkey can intermittently cease the operations of virtual machine instances and containers within your production environment. It is fully integrated with Spinnaker, Netflix’s continuous delivery platform, and it is mandatory to manage your applications through Spinnaker in order to utilize Chaos Monkey for instance termination. It is compatible with any backend supported by Spinnaker (AWS, Google Compute Engine, Azure, Kubernetes, Cloud Foundry) and it has been proven to work with AWS, GCE, and Kubernetes specifically. Chaos Monkey is one of the inaugural chaos engineering tools and was the first to be open-sourced, thus kick-starting the movement.

This tool’s capabilities include locating system bottlenecks to minimize disturbances in production environments, testing application availability, and resilience at an infrastructure level, scheduling tests during specified timeframes, and facilitating convenient monitoring. As an open-source tool, Chaos Monkey is free to use and provides an essential function by conducting unpredictable, continuous attacks on your system.

Gremlin Reliability Score

Gremlin is a reliability management platform designed to aid high-velocity engineering teams in establishing and automating reliability across their operations without hindering software delivery. Their product, the Reliability Score, provides a clear standard for reliability. Gremlin also features an automated range of Reliability Management tools designed for easy integration and enhancement of reliability throughout the software lifecycle without causing delays.

Gremlin stands out as a unique reliability solution created for modern, enterprise-level technology organizations, aimed at meeting user availability demands. It offers custom Chaos Engineering experiments, pre-made reliability tests, and GameDays, all combined in one platform that prioritizes enterprise-level safety and security. The platform aids in identifying and prioritizing the most significant risks to availability, facilitating communication across teams, driving action, and integrating seamlessly with your CI/CD and observability tools through automated, repeatable test programs, and actionable risk reports.

Gremlin offers continuous improvement of reliability, resiliency, and availability with standardized reliability scores. Their comprehensive dashboards and reporting capabilities allow tracking and enhancement of your reliability over time, giving tangible proof of your results. It’s designed to provide a new level of assurance for those reliant on your software services.

Litmus

Litmus is an open-source Chaos Engineering platform that facilitates the identification of potential infrastructures’ vulnerabilities and possible outages by administering chaos tests in a regulated manner. This platform is easy to use, reflects modern chaos engineering practices, and is advanced through collaborative community efforts. It is a platform for conducting chaos engineering on cloud-native infrastructure and applications.

This platform empowers developers and SREs to orchestrate and examine chaos within their environments. Litmus offers ChaosHub, which accommodates most of the chaos experiments essential for a swift start in Chaos Engineering. These experiments are thoroughly tested, highly adjustable, and declarative. Litmus also includes a design for steady-state hypothesis to be established and verified using Litmus probes, aiding users in defining comprehensive chaos scenarios. The platform provides Chaos Observability, exporting Prometheus metrics and events that quantify the chaos impact on applications or infrastructure in real-time.

Litmus also offers a Multi-Tenant solution for Kubernetes, employing namespaces as fully managed environments for individual developers. Overall, Litmus integrates with other tools to provide a platform for managing chaos in a declarative fashion, enabling Kubernetes developers and SREs to discover weaknesses in their applications and infrastructure.

Proofdock Chaos Platform

Proofdock is a software engineering company that provides a Chaos Engineering Platform for Azure and Azure DevOps. Their aim is to foster chaos engineering practices, to enhance the performance and stability of software systems. The primary features of the Proofdock Chaos Engineering Platform revolve around testing and stress-calling infrastructure, allowing software developers to expose weaknesses and address them accordingly.

The platform delivers a user-friendly Graphical User Interface (GUI) which allows seamless creation of attack scenarios. These attacks can target certain resources and applications, simulating real-world conditions such as high CPU stress, IO burning, and introducing network latency. In addition to Azure resource attacks, Proofdock’s platform facilitates application level attacks. This includes inducing errors or delays in request handling and enabling conditions to potentially minimize the damage radius of an attack. It encourages chaos engineering at the application level through the integration of Chaos Middleware.

By utilizing Azure’s inherent features, users gain control access to Azure resources and can execute their attacks via Azure Pipelines. Additionally, Proofdock’s platform neither stores secrets outside your environment nor requires the installation of additional software, thereby minimizing intrusion and enhancing security in your cloud environment.

Simoorg

Simoorg is a failure induction framework designed to be both powerful and user-friendly, suitable for a wide range of applications. It is developed by LinkedIn. The framework stands out due to its widespread adaptability and key features.

These features include custom failures, allowing users to develop their own scenarios and reversion scripts specific to their unique cluster like IO failures, traffic surges, graceful restart, and data corruption. It also offers basic failure scripts such as ungraceful shutdown and graceful restart. Simoorg supports deterministic and nondeterministic scheduling, allowing failures to be timed in a specific order or randomly within a given timeframe. Users can set constraints such as the number of hosts affected at a time, the minimum gap between failures, and the maximum duration of a failure. A health check option is available to assess the cluster before each failure. Additionally, Simoorg includes exhaustive logging for comprehensive tracking.

As it is written in Python, Simoorg is operable on multiple operating systems. The framework’s pluggable architecture allows for notable customization and supports service-specific requirements. Simoorg provides default classes along with the package for these configurable components.

Steadybit

Steadybit is a company offering a solution focused on Chaos Engineering, presenting a robust tool for businesses to enhance the reliability of their system landscapes. The company’s product can be installed as a SaaS or on-prem solution, which can be integrated into your development and testing workflow with ease.

Steadybit features an Experiment Editor, aiding the creation and control of experiments for your Chaos Engineering journey, supplemented with features like advanced target selection, extensions for new attacks or checks, and the capability to import/export experiments using JSON or YAML. Its system landscape visualization feature enables you to understand and document your software dependencies and relationships, automatically discovering all targets, thereby identifying an optimal starting point for your Chaos Engineering implementation. The software also allows you to spot common architecture pitfalls, as well as limit and control the chaos introduced into your system.

The platform enables you to divide your system into separate environments and assign specific permissions to users and teams. For added security, you can integrate the system with your SAML provider or through an on-prem installation with your OIDC provider. It also integrates with your CI pipeline via their API, GitHub action or CLI, making resilience verification a routine part of your build or deploy jobs.

Everything You Need To Know About Chaos Engineering Tools (FAQs)

What Are Chaos Engineering Tools?

Chaos engineering is a strategy of experimentation on a system in order to build confidence in – or discover faults with – the systems capability to withstand turbulent conditions in production. Chaos engineering involves intentionally trying to break the system under specific stresses to locate weak points, ascertain where the risk for potential outages lies, and boost overall resiliency for the system.

A chaos engineering tool is a software application or platform designed to facilitate and automate the process of carrying out chaos experiments. These tools come with a variety of features and functionalities that work together to enable teams to properly simulate real-world scenarios – like service outages, network failures, and resource constraints – to monitor what kind of response these adverse conditions get from the system.

The organizations who should be embracing chaos engineering as a practice are those demanding high levels of resilience, digital maturity, and high observability via dashboards and other tools. This is because they can take swift action when issues are uncovered through these experiments, while organizations that lack observability may struggle to resolve the experiments they create through chaos engineering, leaving them in a weakened state.

Why Use A Chaos Engineering Tool?

The purpose of using chaos engineering tools is not just to put your system through the ringer. Rather, it is to identify the reliability risks in your system that, if left unchecked, could eventually lead to real incidents and outages. The only way to be certain of the resiliency of your system is to test it thoroughly, which is what chaos experiments are for. Adopting Chaos Engineering practices reduces downtime and increases system reliability, serving as an effective preemptive measure against production mishaps.

Chaos engineering tools offer many benefits, including:

The proactive identification of weak points
Better system resilience
Risk mitigation
Enhanced incident response
Increase confidence in system stability
Cost saving
Continuous improvement
Optimized resource allocation

While chaos engineering tools bring with them a number of benefits, it is important for organizations to be smart about adopting these processes and ensure that there are appropriate safety controls in place to be certain that there is no lasting negative impact on the production environments. With the right considerations in place, regular use of chaos engineering tools can help to build a more robust, resilient, and reliable system in the long run.

What Features Should You Look For In Chaos Engineering Tools?

While chaos engineering tools may vary in their features sets, there are common functionalities that all solutions in this category will share as they work together to provide a foundation for improving the resiliency of systems through controlled experiments and testing. These features include:

Experiment Design – This means being able to design and define the chaos experiments by mapping out the specific parameters, conditions, and scope of the disruptions that will be introduced into the systems. Experiment design is a crucial step in the conducting of effective chaos engineering experiments and is a thoughtful and systematic process that aids in ensuring that chaos engineering processes are carried out in a secure manner, with minimal disruption to production systems.
Fault Injection – This refers to the deliberate introduction of controlled faults into a system to simulate real-world scenarios and oversee how the system reacts under these adverse conditions. Being able to inject disruptions, failures, and faults into the system to observe how it reacts within a safe simulated environment is highly valuable, as it provides insight without real risk. These faults may include things like service outages, resource constraints, and latency, and the tool should also give users control over the frequency, timing, and intensity of these simulated faults.
Observation and Monitoring – To properly capture and analyze system behaviors while chaos experiments are taking place, it is important for your chaos engineering tool to monitor performance indicators and observe real-time metrics. It’s also useful to trace the flow of transactions and requests across the system, and to capture logs and events to be analyzes after the chaos experiments have concluded.
Safety Controls – This is a vital feature, as the last thing anyone needs is to unintentionally cause legitimate damage to the system while trying to conduct controlled, safe experiments for the betterment of overall system security. These safety controls are mechanisms implemented to negate the risk of real damage being done by ensuring that experiments are conducted without impacting the overall stability and functionality of the system. This helps organizations to find a balance between the need for experimentation to boost resilience and the imperative to maintain stability and reliability in the production systems.
Analysis and Reporting – The ability to generate reports and analyze the findings is essential for gaining a thorough understanding of the impact of chaos experiments, which allows users to visualize the outcome of genuine faults of a similar nature. This makes it possible to spot concerning trends and, via detailed reports, convey to stakeholders what steps need to be taken to improve future outcomes.

Mirren McDade

Senior Journalist & Content Writer

Mirren McDade is a senior writer and journalist at Expert Insights, spending each day researching, writing, editing and publishing content, covering a variety of topics and solutions, and interviewing industry experts. She is an experienced copywriter with a background in a range of industries, including cloud business technologies, cloud security, information security and cyber security, and has conducted interviews with several industry experts. Mirren holds a First Class Honors degree in English from Edinburgh Napier University.

Laura Iannini

Cybersecurity Analyst

Laura Iannini is an Information Security Engineer. She holds a Bachelor’s degree in Cybersecurity from the University of West Florida. Laura has experience with a variety of cybersecurity platforms and leads technical reviews of leading solutions. She conducts thorough product tests to ensure that Expert Insights’ reviews are definitive and insightful.