Canonical Chaos Engineering Platform vs Krkn
Canonical Chaos Engineering Platform
canonical-chaos-engineering.readthedocs-hosted.comKrkn
krkn-chaos.devSide by side
| Figure | Canonical Chaos Engineering Platform | Krkn |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · Chaos Engineering Platforms | 7.6 (#12) | 8.5 (#5) |
| Integrations | The tutorial integrates Litmus infrastructure with the control plane and uses a self-signed-certificates charm as its example system under test. | The documentation describes integrations with Prometheus, Elasticsearch, Grafana, Slack, S3, and CI/CD systems such as Jenkins and GitHub Actions. |
| Purpose | The platform deploys chaos engineering infrastructure and runs experiments to check whether a system under test recovers from faults. | Krkn injects deliberate failures into Kubernetes clusters to test their 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
Krkn injects deliberate failures into Kubernetes clusters so teams can examine resilience under disruption. Its 20-plus scenarios cover pods, containers, nodes, networks, applications, resources, storage and time. The recommended krknctl command-line tool discovers, runs and coordinates scenarios; other choices include pre-built containers, a browser dashboard and a standalone Python program. Krkn evaluates Prometheus metrics against SLO pass/fail criteria and can review metrics during and after scenarios. Dashboard integrations include Elasticsearch and Grafana, and telemetry can be sent to S3 or Elasticsearch. Cerberus monitors Kubernetes and OpenShift health, providing a go/no-go signal, reports and a metrics API; it can also report failures in Slack. Krkn-AI uses evolutionary algorithms against user-defined objectives to identify experiments. Krkn says it saves recovery state and can restore a cluster, but some disruptive scenarios may leave the target unrecoverable. It is intended for resilience measurement, not security provision, and does not currently adhere to compliance standards. Requirements include Kubernetes or OpenShift 1.21+, cluster-admin kubeconfig access, and Docker 20.10+ or Podman 4.0+.