AWS Fault Injection Service vs Krkn-AI
AWS Fault Injection Service
aws.amazon.comKrkn-AI
github.comSide by side
| Figure | AWS Fault Injection Service | Krkn-AI |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | — | Yes |
| Plans published | 1 | 1 |
| Spec score · Chaos Engineering Platforms | 8.2 (#6) | 8.0 (#10) |
| Integrations | FIS can be accessed through the AWS Management Console, CLI, SDKs, CloudFormation, and HTTPS API, and its APIs can be used in CI/CD pipelines and custom tooling. | The documented setup uses Krkn scenarios, krknctl, Kubernetes or OpenShift, and Prometheus metrics. |
| Monitoring | FIS supports Amazon CloudWatch metrics for monitoring experiments and shows experiment progress and action details through its console and APIs. | An interactive dashboard can monitor Krkn-AI experiment results. |
| Purpose | AWS FIS runs fault injection experiments on AWS workloads to help teams improve application performance, observability, and resilience. | Krkn-AI automatically runs chaos scenarios and discovers experiments to evaluate system resilience. |
Plans
AWS Fault Injection Service
- AWS Fault Injection Service$0.10, paid once
In their own words
AWS Fault Injection Service
AWS Fault Injection Service 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.