List of the Top 7 Chaos Engineering Tools for Amazon Web Services (AWS) in 2025

Our experiment editor simplifies the journey to achieving reliability, making the process faster and more intuitive, with all essential tools easily accessible and allowing total control over your experiments. Every feature is crafted to help you attain your goals while implementing chaos engineering securely at scale within your organization. You can seamlessly add new targets, attacks, and checks through the extensions offered by Steadybit. The user-friendly discovery and selection mechanism enhances the experience of choosing targets. By reducing barriers, you can foster better teamwork across departments, while also allowing for easy export and import of experiments in JSON or YAML formats. The comprehensive view provided by Steadybit’s landscape illustrates the dependencies of your software and the interconnections among various components, giving you a solid foundation for launching your chaos engineering initiatives. Furthermore, the powerful query language enables you to classify your systems into diverse environments based on consistent data applicable throughout your infrastructure, while also allowing specific environments to be assigned to selected users and teams to minimize the risk of accidental disruption. This meticulous strategy guarantees that your chaos engineering practice remains not only effective but also secure and methodically organized, ultimately leading to improved resilience in your systems. Additionally, with these capabilities, your organization can adapt more swiftly to changes and challenges in the digital landscape.

To ensure that your applications run efficiently and maintain superior quality, it's crucial to replicate real-world traffic scenarios during testing. By closely monitoring code performance, you can swiftly pinpoint problems and assure that your application functions optimally before it goes live. Crafting realistic testing environments, performing load tests, and designing intricate simulations of both external and internal backend systems will significantly improve your readiness for production. This approach eliminates the need to create costly new environments for every test, and the integrated autoscaling feature further minimizes cloud costs. You can avoid the hassle of cumbersome, custom frameworks and labor-intensive manual testing scripts, allowing you to release more code in a shorter timeframe. Rest assured that your updates can handle heavy traffic without issues, thus preventing major outages, meeting service level agreements, and ensuring user satisfaction. By effectively mimicking both external systems and internal infrastructures, you achieve testing that is both reliable and economical. There's no longer a requirement to invest in expensive, all-encompassing environments that demand extensive setup times. Transitioning away from outdated systems becomes effortless, guaranteeing a smooth experience for your customers. With these innovative strategies, your application can improve its resilience and performance under a variety of conditions, ultimately leading to a superior product. Additionally, this streamlining of processes allows for a more agile development cycle, empowering teams to innovate and adapt rapidly to changing market demands.

Harness is the world’s first AI-native software delivery platform designed to revolutionize the way engineering teams build, test, deploy, and manage applications with greater speed, quality, and security. By fully automating continuous integration, continuous delivery, and GitOps pipelines, Harness eliminates bottlenecks and manual interventions, enabling organizations to achieve up to 50x faster deployments and significant reductions in downtime. The platform simplifies infrastructure as code management, database DevOps, and artifact registry handling while fostering collaboration and reducing errors through automation. Harness’s AI-powered capabilities include self-healing test automation, chaos engineering with over 225 built-in experiments, and AI-driven incident triage for faster resolution and increased reliability. Feature management tools allow teams to deploy software confidently with feature flags and experimentation at scale. Security is deeply embedded with continuous vulnerability scanning, runtime protection, and supply chain governance, ensuring compliance without slowing delivery. Harness also offers intelligent cloud cost management that can reduce spending by up to 70%. The internal developer portal accelerates onboarding, while cloud development environments provide secure, pre-configured workspaces. With extensive integrations, developer resources, and customer success stories from companies like Citi, Ulta Beauty, and Ancestry, Harness is trusted to drive engineering excellence. Overall, Harness unifies AI and DevOps into a seamless platform that empowers teams to innovate faster and deliver with confidence.

To maintain the highest level of reliability in your business's digital services, it is crucial to implement strong safeguards against potential software and infrastructure failures. By incorporating chaos culture into your DevOps practices with ChaosNative Litmus, you can significantly improve the reliability of your services. ChaosNative Litmus offers a comprehensive chaos engineering platform specifically designed for enterprises, boasting excellent support and the ability to execute chaos experiments in diverse environments, such as virtual, bare metal, and various cloud infrastructures. The platform integrates smoothly with your existing DevOps toolset, facilitating an effortless transition. Built on the principles of LitmusChaos, ChaosNative Litmus preserves all the advantages of the open-source variant. Users can take advantage of consistent chaos workflows, GitOps integration, Chaos Center APIs, and a chaos SDK, ensuring that functionality remains robust across all platforms. This versatility makes ChaosNative Litmus not just a powerful resource, but an essential component for improving service reliability in any organization. Moreover, embracing this approach can lead to a culture of continuous improvement, where teams are empowered to innovate and respond proactively to potential issues.

Recognize the limitations in performance and potential weaknesses that traditional software testing may overlook. It is crucial to set definitive guidelines for stopping an experiment or returning to the pre-experiment state. Conduct tests rapidly by utilizing predefined scenarios from the extensive library provided by the AWS Fault Injection Service (FIS). By simulating authentic failure conditions, teams can gain deeper understanding of how different resources may perform under strain. As part of the AWS Resilience Hub, FIS serves as a robust tool for executing fault injection tests to improve application performance, visibility, and durability. The service simplifies the process of setting up and conducting controlled fault injection tests across various AWS services, which helps teams cultivate confidence in how their applications behave. Additionally, FIS incorporates vital safety features that allow teams to run experiments in production environments with safeguards in place, such as the automatic ability to halt or revert the experiment based on specific pre-established criteria, thereby enhancing overall safety during testing. This functionality equips development teams with the knowledge they need to navigate their applications in high-pressure situations and prepares them for unforeseen challenges. Ultimately, the use of FIS not only improves resilience but also fosters a more proactive approach to application performance management.

Minimizing downtime is essential for maintaining customer trust. NetHavoc transforms the landscape of performance engineering and qualitative delivery on a broad scale. By proactively addressing uncertainties, it prevents these issues from evolving into significant obstacles in real-time situations. Through intentional disruptions of application infrastructure, NetHavoc generates chaos within a regulated environment. This chaos engineering strategy is designed to analyze how applications respond to failures, thus boosting their overall resilience. The objective is to maintain robust application infrastructure during production by facilitating early detection and thorough investigation of potential issues. It is crucial to pinpoint vulnerabilities within the application to uncover hidden threats and mitigate uncertainties. By averting failures that could negatively impact user experiences, organizations can ensure smoother operations. Effective management of CPU core utilization and validation of real-time scenarios are achieved by introducing varied disruptions at the infrastructure level multiple times. Chaos can be seamlessly implemented via the API and an agentless method, allowing users to select either a specific or random timeframe for disruptions to occur. This comprehensive approach not only improves application reliability but also nurtures a culture of continuous enhancement and agility when faced with unforeseen challenges, ultimately leading to better service delivery and customer satisfaction.

Uncover the vital tools needed to build reliable software confidently using Chaos Engineering techniques. Leverage Gremlin's comprehensive array of failure scenarios to run experiments across your entire infrastructure, which includes everything from bare metal and cloud environments to containerized systems, Kubernetes, applications, and serverless frameworks. You can adjust resources by throttling CPU, memory, I/O, and disk performance, reboot machines, end processes, and even simulate time manipulation. Moreover, you can introduce delays in network traffic, create blackholes, drop packets, and mimic DNS outages, ensuring that your code can withstand unexpected issues. It's also crucial to test serverless functions for possible failures and delays to guarantee resilience. In addition, you can confine the impact of these experiments to particular users, devices, or a specified traffic percentage, allowing for targeted evaluations of your system’s strength. This method provides a comprehensive insight into how your software behaves under various stressors, ultimately leading to more robust applications. By embracing this approach, teams can better prepare for real-world challenges and enhance their system reliability over time.