Canonical Chaos Engineering Platform vs Krkn-AI
Canonical Chaos Engineering Platform
canonical-chaos-engineering.readthedocs-hosted.comKrkn-AI
github.comSide by side
| Figure | Canonical Chaos Engineering Platform | Krkn-AI |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Linux | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · Chaos Engineering Platforms | 7.6 (#12) | 8.0 (#10) |
| Deployment | The getting-started tutorial deploys the platform using Terraform and Juju on Kubernetes. | Krkn-AI can be installed as a Python CLI or run from a pre-built container image, including on Kubernetes. |
| Integrations | The tutorial integrates Litmus infrastructure with the control plane and uses a self-signed-certificates charm as its example system under test. | The documented setup uses Krkn scenarios, krknctl, Kubernetes or OpenShift, and Prometheus metrics. |
| Purpose | The platform deploys chaos engineering infrastructure and runs experiments to check whether a system under test recovers from faults. | Krkn-AI automatically runs chaos scenarios and discovers experiments to evaluate system resilience. |
Plans
Canonical Chaos Engineering Platform
No plans published.
In their own words
Canonical Chaos Engineering Platform
Canonical Chaos Engineering Platform has no write-up on Specifiction yet; its figures are above.
Krkn-AI
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.