Krkn-AI vs Tumult
Krkn-AI
github.comTumult
tumult.rsSide by side
| Figure | Krkn-AI | Tumult |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Chaos Engineering Platforms | 8.0 (#10) | 7.1 (#20) |
| Deployment | Krkn-AI can be installed as a Python CLI or run from a pre-built container image, including on Kubernetes. | Tumult is distributed as a static binary and as prebuilt Docker images on GHCR; the site says the binary needs no Python or runtime dependencies. |
| License | The public repository is licensed under Apache-2.0. | Tumult is open source under the Apache-2.0 license. |
| Purpose | Krkn-AI automatically runs chaos scenarios and discovers experiments to evaluate system resilience. | Tumult is a Rust-native chaos engineering platform for defining, running, and analyzing experiments that test how systems behave under failure conditions. |
| Security | The project documentation links to a security self-assessment; the repository page does not state a compliance certification. | The production deployment guide says HTTP exposure beyond loopback requires authentication and documents TLS configuration for the analytics daemon's HTTP and gRPC listeners. |
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.