Krkn-AI vs PowerfulSeal

Krkn-AI is a free, open-source tool for automatically running chaos scenarios and finding experiments to evaluate system resilience. It uses evolutionary algorithms to generate experiments from Krkn scenarios, guided by user-defined objectives such as SLOs and application health checks. Its discover command creates a YAML configuration populated with cluster component information and basic setup. Results show the impact of scenarios on application liveness and include fitness scores; Prometheus metrics can measure system response. An interactive dashboard can monitor experiment results. The project describes testing Kubernetes and OpenShift applications and requires a cluster kubeconfig. Krkn-AI can be installed as a Python CLI or run from a pre-built container image, including on Kubernetes. Listed requirements include Python 3.11 or later, krknctl, oc or kubectl, Podman, Helm 3.x, and a Kubernetes or OpenShift cluster kubeconfig. The repository lists the Apache-2.0 license. Its README describes the tool as under active development and advises users to use it at their own risk. The standard container run example uses host networking and privileged mode, while the guide also documents a FUSE-based option without the privileged flag.

PowerfulSeal is a free, Apache-2.0 tool that injects failures into Kubernetes clusters to expose problems early. Users describe complete chaos experiments in YAML policies and choose autonomous, interactive, or label operation. It supports Kubernetes, OpenStack, AWS, Azure, GCP, and local machines. Autonomous mode can collect metrics through stdout, Prometheus, or Datadog, while its web interface can show logs and the policy. Policies can schedule one run for a CI pipeline or continuous execution. Label mode controls which pods may be killed, on which days and times, and with what probability. Local installation uses pip and requires Python 3.7 or newer; Docker images are published for each release. PowerfulSeal can run inside Kubernetes and load scenarios from Custom Resources. Cloud configurations cover Azure, AWS, OpenStack, and GCP. It needs SSH, cloud, and Kubernetes API access, but no external database or Spinnaker.