krkn/ADOPTERS.md

Krkn Adopters

This is a list of organizations that have publicly acknowledged usage of Krkn and shared details of how they are leveraging it in their environment for chaos engineering use cases. Do you want to add yourself to this list? Please fork the repository and open a PR with the required change.

Organization	Since	Website	Use-Case
MarketAxess	2024	https://www.marketaxess.com/	Kraken enables us to achieve our goal of increasing the reliability of our cloud products on Kubernetes. The tool allows us to automatically run various chaos scenarios, identify resilience and performance bottlenecks, and seamlessly restore the system to its original state once scenarios finish. These chaos scenarios include pod disruptions, node (EC2) outages, simulating availability zone (AZ) outages, and filling up storage spaces like EBS and EFS. The community is highly responsive to requests and works on expanding the tool's capabilities. MarketAxess actively contributes to the project, adding features such as the ability to leverage existing network ACLs and proposing several feature improvements to enhance test coverage.
Red Hat Openshift	2020	https://www.redhat.com/	Kraken is a highly reliable chaos testing tool used to ensure the quality and resiliency of Red Hat Openshift. The engineering team runs all the test scenarios under Kraken on different cloud platforms on both self-managed and cloud services environments prior to the release of a new version of the product. The team also contributes to the Kraken project consistently which helps the test scenarios to keep up with the new features introduced to the product. Inclusion of this test coverage has contributed to gaining the trust of new and existing customers of the product.
IBM	2023	https://www.ibm.com/	While working on AI for Chaos Testing at IBM Research, we closely collaborated with the Kraken (Krkn) team to advance intelligent chaos engineering. Our contributions included developing AI-enabled chaos injection strategies and integrating reinforcement learning (RL)-based fault search techniques into the Krkn tool, enabling it to identify and explore system vulnerabilities more efficiently. Kraken stands out as one of the most user-friendly and effective tools for chaos engineering, and the Kraken team’s deep technical involvement played a crucial role in the success of this collaboration—helping bridge cutting-edge AI research with practical, real-world system reliability testing.