Krkn-AI vs Steadybit
Krkn-AI
github.comSteadybit
steadybit.comSide by side
| Figure | Krkn-AI | Steadybit |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | Yes |
| Open source | — | — |
| Web | — | Yes |
| Windows | — | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 2 |
| Spec score · Chaos Engineering Platforms | 8.0 (#10) | 7.6 (#13) |
| Deployment | Krkn-AI can be installed as a Python CLI or run from a pre-built container image, including on Kubernetes. | Steadybit is offered as SaaS and supports on-premises deployments, including air-gapped environments. |
| Integrations | The documented setup uses Krkn scenarios, krknctl, Kubernetes or OpenShift, and Prometheus metrics. | Its open source integrations include Kubernetes, AWS, Azure, GCP, Datadog, Grafana, Prometheus, Kafka, and RabbitMQ. |
| Purpose | Krkn-AI automatically runs chaos scenarios and discovers experiments to evaluate system resilience. | Steadybit is a chaos engineering platform for proactive reliability assessment and testing. |
| Support | The project documentation links to a community Slack, GitHub issues, and contribution guidelines. | Professional includes standard customer support during 9–5, while Enterprise includes enterprise support and tailored onboarding. |
Plans
In their own words
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.
Steadybit
Steadybit is a chaos engineering platform for teams assessing reliability through planned testing. A no-code editor and templates support experiment design, while a live run view shows progress. Reliability Advice flags risks against best practices and suggests experiments and mitigations. Discovery identifies targets and metadata so teams can visualize and group system components. Tests can inject faults and check health across network, resource, and application layers. The platform supports scheduled experiments and blast-radius controls, with open source integrations for Kubernetes, AWS, Azure, GCP, Datadog, Grafana, Prometheus, Kafka, and RabbitMQ. Teams can also create integrations and custom actions with Extension Kits, ActionKit, and AdviceKit. Steadybit is available as SaaS and supports on-premises deployments, including air-gapped environments. Teams, roles, and fine-grained permissions help govern testing. Pricing is available on request; there is no free plan, but a 30-day free trial is offered. Professional has standard support during 9–5; Enterprise lists on-prem installation, SAML, audit logs, enterprise support, and tailored onboarding.