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Krkn-AI vs Steadybit

Krkn-AI

github.com

Steadybit

steadybit.com

Side by side

FigureKrkn-AISteadybit
Price fromFree—
Free planYes—
Free trial—Yes
Open source——
Web—Yes
Windows—Yes
LinuxYesYes
API—Yes
Self-hostedYesYes
Plans published12
Spec score · Chaos Engineering Platforms8.0 (#10)7.6 (#13)
DeploymentKrkn-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.
IntegrationsThe 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.
PurposeKrkn-AI automatically runs chaos scenarios and discovers experiments to evaluate system resilience.Steadybit is a chaos engineering platform for proactive reliability assessment and testing.
SupportThe 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

Krkn-AI

  • Apache License 2.0Free

Krkn-AI pricing →

Steadybit

  • EnterprisePriced on request
  • ProfessionalPriced on request

Steadybit pricing →

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.

Visit github.com

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.

Visit steadybit.com